Estratégia de reversão média ssrn

Esquivando-se do rolo compressor: Fundamentos versus o carry trade ☆
Destaques.
As carteiras de negociação de carry yield geram retornos altos (baixos) quando a volatilidade é baixa (alta).
As carteiras de taxa de câmbio real dão retornos altos (baixos) quando a volatilidade é alta (baixa).
A volatilidade é persistente o suficiente para ser explorada na formação de portfólio.
Carteiras de comutação quando a volatilidade é alta produz retornos mais altos.
Esse resultado anômalo é robusto em relação a subperíodos.
Embora, de acordo com a paridade de taxa de juros descoberta, as taxas de câmbio devam se mover de modo a impedir que o carry trade seja sistematicamente lucrativo, há uma vasta literatura empírica demonstrando o contrário. Moedas de juros altos tendem a apreciar mais do que depreciar, como observado por Fama (1984). Neste artigo, tratamos a volatilidade como a variável de estado crítico e mostramos que os retornos positivos para o carry trade são esmagadoramente gerados no estado "normal" de baixa volatilidade, enquanto o estado de alta volatilidade está associado a retornos mais baixos ou com perdas em moedas revertem para o nível de longo prazo aproximado por sua taxa de câmbio real média - em outras palavras, a paridade do poder de compra (PPP) tende a se reafirmar, pelo menos em certa medida, durante os períodos de turbulência. Confirmamos estes resultados comparando os retornos de três possíveis estratégias de negociação mensais: o carry trade, uma estratégia que é longa a desvalorização e curto das moedas sobrevalorizadas (a estratégia “fundamental”) e uma estratégia mista que envolve a mudança do carry trade para os fundamentos sempre que a volatilidade do período anterior estivesse no quartil superior. Ficamos com o resultado anômalo (mas aparentemente robusto) de que a estratégia mista parece gerar retornos mais altos e índices de Sharpe do que qualquer uma das estratégias puras.
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Sujeito ao aviso habitual, os autores gostariam de agradecer os comentários úteis de Lukas Menkhoff e David Peel sobre versões anteriores deste documento e de participantes em seminários nas Universidades de Cardiff e Oeste da Inglaterra, bem como na 10ª Conferência BMRC-DEMS. sobre Economia Macro e Financeira na Universidade de Brunel, maio de 2014, a Conferência sobre Dinheiro, Macro e Finanças, setembro de 2014 na Universidade de Durham, Reino Unido, e a Conferência do PFMC, Paris, dezembro de 2015.

Financiamento corporativo e comportamento alvo: Novos testes e evidências.
Destaques.
Abordamos as preocupações do teste de comportamento de alvo confiável na literatura passada e oferecemos uma nova estratégia para fazer isso.
Em vez de focalizar os movimentos do rácio da dívida, examinamos se as opções de financiamento das empresas são consistentes com o comportamento alvo.
Descobrimos que essas opções de financiamento geralmente não são consistentes com um comportamento-alvo sistemático.
Descobrimos que as opções de financiamento das empresas não são influenciadas principalmente pelos desvios de seus índices de endividamento.
Nossa metodologia distingue o comportamento alvo do comportamento de financiamento aleatório com sucesso.
Este estudo aborda as preocupações recentes na literatura sobre estrutura de capital sobre a confiabilidade dos testes do comportamento de seguimento de metas. Usando uma nova estratégia de testes, examinamos se e em que medida as firmas & # x27; as opções de financiamento - em vez do movimento de seus índices de endividamento - concorrem com o comportamento de seguimento de metas. Nós achamos que firmas & # x27; As decisões de financiamento não são geralmente consistentes com a sistemática de seguimento de metas. Nossos resultados permanecem semelhantes quando examinamos um período prolongado de tempo e também quando consideramos que as empresas podem ter uma faixa de índices de dívida-alvo em vez de um único alvo ou restrições financeiras variáveis. Nossos resultados também são robustos para especificações de alvos diferentes e nossa metodologia pode diferenciar com segurança o comportamento alvo de financiamento aleatório. Testes adicionais também confirmam nossos resultados sugerindo que as firmas & # x27; as decisões de financiamento não são impulsionadas principalmente por desvios das firmas & # x27; endividar os rácios da dívida.

Estratégia RRSP.
Pós-navegação.
O momentum do setor supera o momentum das ações?
Uma estratégia de momentum pode ser implementada em um nível de estoque individual ou nível de setor.
Existe uma vantagem em dividir os estoques em setores e possuir o setor mais forte do que comprar os maiores estoques de momentum do mercado? Os fundos do momentum de ações estão disponíveis há alguns anos e, recentemente, a rotação setorial foi empacotada em um ETF.
Ken French publica um portifólio de dez portfólios e momentum do setor para ações dos EUA que podem ser usados ​​para investigar:
A curva azul é o setor de topo (classificado por 12 meses de retorno, mensalmente). A curva vermelha é o estoque de tercil superior (classificado por 12 meses de retorno, mensalmente) nos 50% maiores por capitalização de mercado.
A rotação setorial supera claramente o momentum das ações (cerca de 3% ao ano desde 1950). No entanto, esse resultado é apenas para o setor com maior classificação. É difícil determinar a fração do mercado representada pelo setor superior versus os grandes estoques do tercil superior, mas as fatias setoriais são provavelmente menores.
Vários setores são comparados abaixo. Os retornos do top 2 e top 3 são comparáveis ​​com a estratégia de momentum do estoque individual.
Compare o & # 8220; Top 1 & # 8221; curva com a simples rotação entre valor e momento mostrado neste blog. Desempenho semelhante (17% CAR), mas com apenas dois instrumentos e menos negociações.
Por fim, combine setores com valor, momentum e sem risco (RF). O topo do portfólio por 12 meses de retorno é selecionado a cada mês:
Os retornos são melhorados cerca de 1% ao ano em todo o conjunto de dados, mas a superação de 4% desde 2000 (evitando a correção do GFC) representa a maioria.
Uma estratégia de rotação com o setor superior supera o grande portfólio de ações da FF. No entanto, isso pode ser parcialmente devido à seleção de uma seção menor do mercado.
Esses testes mostram que bater um simples modelo rotacional de valor-momento é difícil. Adicionar dez setores a esse modelo aumenta ligeiramente os retornos, mas às custas de um maior volume de negócios.
Isso demonstra o que é possível apenas com a classificação baseada em preço. Adicionar volatilidade e correlação à pontuação pode melhorar ainda mais (por exemplo, Keller 2015 & # 8220; Alocação de recursos clássicos & # 8221;):
Otimização de portfólio Markowitz com código VBA.
Wouter, Butler e Kipnis [2015] recentemente demonstraram a Classical Asset Allocation (CAA) para portfólios longos, baseados em Markowitz & # 8217; conceitos. O método usa apenas dois parâmetros, minimizando assim as chances de ajuste de curvas e de espionagem de dados. Os parâmetros são o período de lookback (12 meses) e a volatilidade desejada.
Os principais resultados do trabalho são os seguintes (de 1915 a 2015):
R retorno anual, volatilidade V, volatilidade alvo TV, D max. rebaixamento, EW peso igual.
SP500, EAFE, EEM, US Tech, Japão Topix, T-Bills, US Gov10y e US High Yield.
10 setores de Fama / França dos EUA, cinco títulos americanos, ações Small Caps dos EUA, GSCI, Gold, títulos estrangeiros, US TIPS, US REITs compostos, US Mortgage REITs, FTSE EUA 1000 / US 1500 / Global ex EUA / Desenvolvido / EM, JapanGov10y , Dow Util / Transporte / Indústria, FX-1x / 2x e Timber.
Resultados consistentes de todos os conjuntos de dados fornecem mais confiança no método.
Meu principal interesse é em investir em fator. Eu apliquei o método ao momentum e ao valor de portfólios normalmente usados ​​neste blog mais o fator Mkt e & # 8216; livre de risco & # 8217; (todos da biblioteca de dados de Ken French). Os dividendos são continuamente reinvestidos e os atritos comerciais são negligenciados (essa estratégia é negociada apenas algumas vezes por ano).
Os resultados de duas volatilidades alvo são mostrados abaixo. O caso de menor volatilidade exibe um notável Índice Sharpe de 65 anos de 1,3. A estatística t é 10,6!
Os retornos anualizados são de 9,7% e 12,6% respectivamente.
O próximo post abordará portfólios setoriais e conjuntos de dados reais.
Eu usei o solver do Excel para maximizar o retorno à direita de 12 meses com uma meta de desvio padrão de 12 meses. Uma restrição é aplicada da soma dos pesos = 1 (sem alavancagem).
A planilha é trivial para criar. O layout da coluna para corresponder ao código do VBA é o seguinte. Isso é para 4 conjuntos de dados, mas pode ser estendido conforme necessário. As colunas F-L começam na linha 13, pois exigem 12 meses de histórico.
B-E Dados de retorno mensais (4 conjuntos de dados)
Pesos da carteira F-I (calculados pelo solucionador)
J Soma dos pesos (F: I)
K Weights multiplicou por retornos de 12 meses, somados.
L pesos multiplicado por 12 meses de retornos, somado.
Quando o solucionador terminar, multiplique os retornos mensais por pesos da linha anterior e soma para obter o retorno do portfólio.
O código do VBA para o solver é executado como uma macro na planilha que contém os dados:
Para i = 13 a 790 & # 8216; dados de retorno mensais nas linhas 2-790.
SolverAdd CellRef: = & # 8221; $ J $ & # 8221; & amp; i, Relação: = 1, FormulaText: = & # 8221; 1 & # 8243; & # 8216; soma de pesos = 1.
SolverAdd CellRef: = & # 8221; $ L $ & # 8221; & amp; i, Relação: = 1, FormulaText: = & # 8221; 3 & # 8243; & # 8216; target stdev = 3% (10% anualizado)
Recessões nos EUA, o Fator de Valor (HML) e o status atual.
O fator de valor Fama-French HML exibe um ciclo de 4 anos bastante confiável. Crescimento e Valor out-performance oscilam com um período de 4 anos (veja meu post anterior sobre isso).
Liew e Vassilou (1999), mostram que a mudança anual no HML está relacionada à futura mudança do PIB (veja meu post no blog aqui). Portanto, o rastreamento do HML nos permite compreender as condições econômicas futuras.
Onde estamos no ciclo atual?
A linha preta é a média móvel de 12 meses do HML. As recessões do St. Louis Fed estão em vermelho. As baixas HML ocorrem claramente em anos divisíveis por 4, embora possam levar ou atrasar por alguns meses. As baixas correspondem a um alto crescimento, que muitas vezes (mas nem sempre) leva à recessão. Uma hipótese é que a recessão corrige os excessos de crescimento em partes superaquecidas da economia.
A HML está atualmente no ciclo ou próximo do ciclo baixo (circulado). Portanto, as condições estão em vigor para uma recessão em potencial. Também digno de nota é a rapidez com que o valor supera o crescimento nas saídas de recessão.
Felizmente, há melhores ferramentas de previsão de recessão do que a HML, porque a maioria dos períodos de mercado de fraqueza prolongada coincide com a recessão. O gráfico abaixo mostra a média de 12 meses do Fator de Mercado em preto.
Isso destaca por que usar o sinal do retorno de 12 meses como um filtro de investimento é tão eficaz. Uma vez que o retorno se torne negativo, a desaceleração é geralmente sustentada.
No entanto, o retorno de 12 meses pode não fornecer um sinal de reentrada ideal. Várias medidas de sobrevenda, como a porcentagem de estoques abaixo de uma média móvel de N dias, podem ser comparadas a níveis típicos de recessão para se transformar em fundos de valor a preços baixos.
À medida que o retorno acumulado em 12 meses se torna cada vez mais negativo, os retornos médios anuais futuros aumentam rapidamente:
Sazonalidade debunked (parcialmente)
Os retornos médios trimestrais do mercado (Mkt-RF) de 1950 até o presente são mostrados abaixo (dados da biblioteca de Ken French). Os quartos 1-4 são até anos e 5-8 são anos ímpares.
A tabela mostra que os retornos médios dos trimestres 4-6 são maiores que zero com alta significância (t-stat & gt; 2,3).
Exceto para Q8 que é marginal, todas as outras médias trimestrais (incluindo valores negativos) não são estatisticamente diferentes de zero (t-stat & lt; 2). Portanto, não é possível lucrar com este efeito excluindo períodos negativos, por isso, o & # 8216; parcial & # 8217; desmascarando.
Advertências a esses resultados de testes são de que o conjunto de dados é pequeno (32 pontos) e os dados financeiros não são normalmente distribuídos.
A sazonalidade é um efeito estatisticamente significativo: os trimestres 4-6 têm retornos médios acima de zero. Outras médias trimestrais não são estatisticamente diferentes de zero. Uma estratégia de calendário robusta para evitar períodos negativos não pode ser projetada.
Ganhos cumulativos de mercado são zero em todos os anos - mesmo anos - # 8217;
Mkt-RF retorna em & # 8216; anos pares & # 8217; soma para zero nos últimos 50 anos ou mais (dados da biblioteca de Ken French). Este poderia ser um resultado espúrio, embora as estatísticas sugiram o contrário.
Este resultado é estatisticamente significativo?
A aplicação do teste t de Student fornece uma estatística de 2,3, ou seja, os retornos médios de anos pares versus anos ímpares são diferentes no nível de significância de 5%.
Momento Relativo Fator: achado surpreendente no ranking.
Um portfólio de valor ou momento é selecionado a cada mês, com base no maior retorno dos últimos 12 meses (R). Os dados são da biblioteca de Ken French de 1950 a 2015. Uso o portfólio de grande momento e o portfólio de pequeno valor (a anomalia de HML não existe em ações de grande capitalização).
Eu encontrei duas surpresas:
1) Ranking em retornos ao quadrado (n = 2) supera consistentemente a classificação apenas pelo retorno (n = 1). Em outras palavras, a magnitude do retorno é importante, positiva ou negativa. A reversão média provavelmente explica a melhora, mas isso requer uma investigação mais detalhada. Os retornos anuais excedem 20% nas últimas 4 décadas.
A tabela mostra que a estratégia n = 2 tem um desempenho muito melhor do que os portfólios de componentes, particularmente neste século: 17% em comparação com 13% e 8% para Valor (V) e Momento (M), respectivamente.
2) Sobrepor um filtro de momentum absoluto (reter dinheiro quando retornar & lt; 0) degrada os retornos. A margem aumenta com recência: para 3,6% anualmente desde 1999! O índice de Sharpe não é materialmente reduzido, pois o desvio diminui proporcionalmente.
NYSE Advance & # 8211; Diminuir o volume acima de 1000 (3 anos de altura)
3 anos de alta para $ NYUD: fluxo de dinheiro significativo, especialmente em grandes capitalizações:
Estatísticas dos últimos 3 anos.
1 mês de retorno após $ NYUD & gt; 500 1,8%
1 mês de retorno (todos) 1,2%
3 Fator Dual Momentum: Valor, Momento e Baixa Volatilidade (ou BAB)
Este post analisa Factor * Dual Momentum com 3 fatores: Valor, Momento e Baixa Volatilidade (ou Apostar contra Beta). Postagens anteriores abrangiam apenas dois fatores.
* Portfólios longos dos fatores, em vez dos longos fatores curtos.
Dados de baixa volatilidade foram gentilmente fornecidos pelo leitor Paolo, mas só remontam a 1998, ao invés de 1950, como os testes anteriores.
A estratégia detém a carteira mais alta por 12 meses, se o retorno for maior que zero. Os resultados são mostrados para as estratégias de fatores 2 e 3 e os portfólios subjacentes.
A posição de 3 fatores é mostrada no traço inferior como 4 níveis:
0 = Dinheiro, 1 = Valor, 2 = Momento, 3 = Baixa Volatilidade.
A baixa volatilidade é claramente realizada principalmente em 2001 e 2012.
O índice de Sharpe e o retorno anual são resumidos acima.
As estratégias retornam resultados semelhantes, exceto por um período em 2012, quando o acesso à baixa volatilidade permite que 3 fatores superem o desempenho.
A carteira de valor tem os retornos mais altos em alguns períodos (e no geral), mas um índice de Sharpe baixo.
Baixa Volatilidade tem o Índice de Sharpe mais alto devido à curva de patrimônio líquido uniforme e menor rebaixamento em 2008.
O valor e a estratégia de 3 fatores têm os maiores retornos durante o período de teste.
Status Dual Momentum fator e argumento para dados.
A série recente analisou o Factor Dual Momentum. Os portfólios de valor dos EUA e fator Momentum foram testados em 1950, cortesia da biblioteca de dados de Ken French.
As carteiras são classificadas no retorno de 12 meses. Usando VBR e PDP para valor e momento, a imagem atual se parece com isto:
A estratégia deve ser investida no PDP, pois os retornos relativos de 12 meses são maiores e os retornos absolutos são maiores que zero.
Gostaria de voltar a executar a análise com um portfólio de baixa volatilidade, pelo menos até 1980. Esses dados estavam em beta-bitragema que agora desapareceu. Chris Asness & # 8217; site tem fatores, mas não portfólios. Se alguém puder ajudar, por favor me avise nos comentários.
Momento duplo: parâmetro de lookback.
Uma grande vantagem do dual momentum é o baixo número de parâmetros (normalmente é utilizado apenas um período de lookback de 12 meses). Isso reduz a probabilidade de que os resultados sejam ajustados ou descobertos pela mineração de dados e, posteriormente, inúteis em negociações em tempo real.
O gráfico abaixo compara um lookback de 12 meses contra 1 mês e uma combinação de 50:50 de ambos os lookbacks:
Os retornos anuais e as taxas de sharpe estão listados na legenda do gráfico e são muito semelhantes.
De maior interesse, porém, a correlação entre & # 8217; 12 & # 8217; e & # 8216; 1 & # 8217; retornos mensais é de apenas 0,62. Encontrar estratégias consistentemente não correlacionadas é difícil, mas recompensador. Quando as duas estratégias são combinadas, o desvio padrão é reduzido e a taxa de sharpe é aumentada para 1,3.
Um gráfico ampliado de 2000 até o presente é mostrado abaixo:
Os maiores rebaixamentos experimentados pelas estratégias individuais (2002, 2009 e 2011) foram reduzidos pela combinação das duas curvas relativamente não correlacionadas, sem sacrificar os retornos.

A estratégia de ETF curta alavancada inversa de Darwin & # 8211; Resultados incríveis descritos.
por Darwin em 26 de janeiro de 2010.
Chegou a hora de finalmente desvendar a Estratégia de ETFs Alavancas Inversas de Darwin. Se você está se perguntando o que é e por que isso importa, em poucas palavras, mudou completamente a cara da negociação para mim & # 8211; e também para você, se tiver acesso aos Fundos Alavancados 2X ou 3X necessários, se você ativar a margem de negociação, se tiver os requisitos de capital e se puder monitorar e sustentar o risco de perda. É um nome longo, e pode ser complexo de seguir, então, por favor, leia.
O que é a estratégia de ETF de curto alcance alavancada inversa de Darwin?
Vou começar no nível mais básico e mergulhar em níveis crescentes de complexidade à medida que avançamos. No nível mais básico, você se opõe a ETFs ao mesmo tempo com fundos iguais. O resultado desejado é uma estratégia neutra de mercado, na qual você pode obter retornos significativos ao longo do tempo, independentemente do que as ações em geral estejam fazendo. O que os investidores ricos pagam a um gestor de fundos de hedge 2% mais 20% dos lucros está agora ao seu alcance & # 8211; com riscos, que você deve entender e gerenciar. Dê uma olhada no gráfico abaixo para um exemplo de 2009. Eu mapeei o desempenho do 3X short ETF, 3X Long ETF e do setor subjacente ETF todos relacionados ao Setor Financeiro.
Observe como a Blue Line (XLF & # 8211; 1X Financials ETF) aumenta marginalmente durante o período. Enquanto isso, o FAS, o 3F Leveraged Financial ETF está perto de 50% enquanto o FAZ, o 3X Short Financials ETF está abaixo de 90%. Se você encurtou FAS e FAZ para o ano de 2009, você fez 50% em FAS e 90% + em FAZ para um ganho normalizado de 70%. Você fez isso em 70% durante um dos anos de negociação mais tumultuados, voláteis e desesperados da nossa geração. E consiga isto & # 8211; você não está realmente colocando os fundos para ganhar 70% (mais ou menos). Você pode ser comprido em qualquer coisa que quiser (dinheiro, ações, títulos, etc) e usar sua pequena capacidade para dedicar uma parte de sua carteira a posições invertidas de ETF curtas, como descreverei abaixo.
Espere, se um ETF Leveraged estiver ativo, o ETF Inverso deve estar inoperante, certo?
Não # 8211; Mais frequentemente do que não, eles são para baixo durante longos períodos de tempo (apenas meses, não estamos falando anos aqui). Isso é a tragédia (para investidores longos) e a beleza (para shorts). Devido ao reequilíbrio diário, que lentamente corrói o valor desses ETFs, todo mundo está gritando nos telhados que eles são ruins para comprar e manter investimentos. De fato, há inúmeras ações judiciais contra a Direxion e o Proshares por vender esses “instrumentos de destruição em massa”. para investidores de varejo & # 8211; e até gerentes de dinheiro profissionais # 8211; quem não consegue entender o conceito. Basicamente, se você for pegar um número e subir 2%, cair 2% para frente e para trás, contanto que não seja uma marcha constante em uma direção por semanas a fio, ambos os lados vão declinar. ao longo do tempo. Experimente você mesmo em uma planilha eletrônica & # 8211; você verá & # 8211; é tão simples assim. Desde que eles são investimentos tão ruins ao longo do tempo, ao invés de mergulhar de cabeça e comprá-los; Curta-os!
Retornos reais de Darwin: Estratégia de ETF curta alavancada inversa.
Para remover qualquer dúvida, incluí a captura de tela real da minha conta de negociação neste fim de semana, onde a Ameritrade descreve claramente meus ganhos e perdas desde a abertura da posição. Em cada um dos pares (ERX) (ERY) Energia, (FAS) (FAZ) Finanças e (GLL) (UGL) Ouro, eu estou em cima. Note como o ganho NET para cada par é positivo & # 8211; essa é a chave. Eu estou indo, independentemente do que aconteceu com os setores subjacentes. Para entender como seriam esses retornos em uma base anualizada, realizei algumas excelentes funções do Excel, já que estive em cada posição por apenas alguns meses. Também fiz questão de incluir o impacto das vendas e distribuições de dividendos curtos ocorridas no final do ano passado (o que faz com que meu retorno pareça pior, não melhor).
Você pode estar dizendo "& # 8220; E daí? O S & amp; P500 é até 65% desde o fundo em março. E você está perdendo tempo fazendo 37%? & # 8221; Isso viria de alguém que está totalmente perdendo o ponto. O mercado não voltará a subir 65% no futuro previsível. O mercado sofrerá correções e anos sem brilho. Este modelo é indiferente aos caprichos dos retornos gerais do mercado. Este modelo também pode fazer 37% quando o mercado está em baixa. Pode fazer 37% quando o mercado é plano. Usando setores que não são correlacionados de perto (petróleo, ouro, finanças), e compensando o tempo de entrada, eu estou introduzindo diversificação nos retornos de cada par.
Lembre-se de Madoff? As pessoas perderam suas economias de vida perseguindo 12% em qualquer mercado.
Bem, isso é tão transparente quanto possível e você pode capturar ganhos de dois dígitos anualmente em qualquer mercado & # 8211; contanto que você gerencie e entenda seus riscos conforme descrito abaixo. Eu continuarei compartilhando meus negócios e resultados curtos específicos aqui (Inscreva-se).
Parece bom demais para ser verdadeiro & # 8211; O que é o Catch?
Há um fator importante que eu ainda não compartilhei & # 8211; e quero destacá-lo com destaque. Este modelo se desfaz quando o setor subjacente decola. Existem questões de margem a serem consideradas. Existem vários riscos e considerações & # 8211; leia a próxima seção antes de tentar isso.
Já tenho um aviso de isenção no meu blog, mas quero reiterar esse fato de que não estou certificado para fornecer consultoria financeira. Eu sou um comerciante individual e não seu conselheiro. Se você quiser embarcar em uma estratégia arriscada que envolve requisitos de margem, a capacidade de cobrir chamadas de margem, a capacidade de sustentar perdas em caso de movimentos imprevistos do mercado e outros riscos que podem não ter sido descritos aqui, você deve consultar o seu próprio conselheiro antes de o fazer. Eu acho que você entendeu. Além disso, quero destacar onde esse modelo se rompe e como eu pessoalmente gerencio o risco no meu portfólio curto alavancado:
Ações insuficientes para encurtar com o corretor: Encontrei isso com o par curto TMF / TMV, conforme evidenciado no instantâneo de desempenho. Basicamente, Ameritrade ligou um dia e disse que eu tinha que fechar minha posição vendida porque não havia ações suficientes. Este foi alguns meses para a posição e em olhar para trás para 01/07/09, se eu ainda mantivesse essas ações a descoberto, eu teria um impressionante 19% em média, uma vez que cada um deles perdeu 19% em relação a isso.
Período de 6 meses coincidentemente (ver Tabela de Pares de Tesouraria Abaixo). Essa foi uma dessas circunstâncias em que cada lado do comércio perdeu uma quantia substancial de dinheiro durante um breve período. De qualquer forma, não há realmente nada que você possa fazer para evitar que isso aconteça além de ir com um corretor on-line maior e ir com os pares mais proeminentes. O que eu encontrei é que vários pares que eu tentei encurtar com vários corretores on-line não estavam disponíveis para breve. Então, eu tive que me contentar com os 3 pares que eu tenho agora. Chamada de Margem & # 8211; Dada a recente exigência de margem mais rigorosa para ETF alavancados (que realmente não fez nada para resolver a falta de compreensão destes instrumentos e só tornou mais caro o comércio), é totalmente plausível que, quando um dos pares pode ter ganho digamos, 50% (o que significa que você está 50% no buraco em uma posição curta), embora o par de ETFs inversos possa ter perdido, digamos, 70% (o que significa que você tem um ganho de 70% , para uma posição positiva líquida de 20%), o representante do serviço ao cliente provavelmente não vai nem entender o procedimento de matemática envolvido e citar e dizer que você tem que desembolsar mais capital ou fechar sua posição vendida. Enquanto isso ainda lhe renderia um ganho geral neste cenário hipotético, você pode ser chamado de margem em uma situação sub-ótima ou não ter dinheiro extra para entrada. Custos de margem & # 8211; Dependendo de que tipo de capital e propriedades você tenha em seu portfólio, tenha cuidado para não pagar taxas de margem exorbitantes para manter essa estratégia. Embora eu não esteja sendo atingido com despesas de margem porque essas posições não estão ocupando a maior parte do meu portfólio, se você tiver essa estratégia comendo a janela de margem máxima, talvez esteja pagando 10% + em taxas de margem para manter uma estratégia que pode até não fazer 10% para você ex-despesas. Mercado em fuga & # 8211; Este é praticamente o seu maior risco. Enquanto eu descrevi como você pode ganhar dinheiro em ambos os lados dos pares ETF alavancados inversos em muitas situações, quando um índice subjacente aprecia (ou se deprecia) tão rapidamente diariamente sem uma quebra significativa na tendência, você pode literalmente ter fugido retorna. Lembre-se de como você pode dizer, fazer 40% de um lado e perder 32% do outro lado e ainda sair na frente? Bem, o que acontece quando um ETF alavancado retorna mais de 100% em um determinado período? Você não pode ganhar mais do que 100% fazendo falta de nada & # 8211; é matematicamente impossível. O que realmente aconteceria seria que seu ganho estaria no máximo em torno de 90%, enquanto o ETF alavancado descontrolado poderia subir, 200% (perda líquida de 110%). Lembre-se, quando você faz algo curto, suas perdas são infinitas. Veja abaixo como eu gerencio um mercado em fuga. Para demonstrar uma situação extremamente ruim, veja abaixo (Bad Chart) para o que aconteceu desde o pivô absoluto em março até setembro de 2009. A FAS subiu mais de 500%! Isso teria matado um investidor que permaneceu em curto sem tomar medidas evasivas.
Como reagir a uma situação de ETF curta alavancada em fuga.
O que você faz se você empreendeu a estratégia quando um índice subjacente decola, entregando ganhos de três dígitos em um lado da moeda? Existem algumas opções à sua disposição, nenhuma delas sendo ideal.
Primeiro, você poderia correr para se esconder e apenas fechar suas posições. Você terá uma perda, o que acontece na negociação. Eu aconselho a não apenas fechar sua posição perdedora e deixar o outro correr, já que não é diferente de apenas abrir uma posição curta de 1 face agora, o que é mais parecido com apenas pegar uma direção e fazer um curto como se opõe aos retornos neutros de mercado que a Estratégia de ETF Inversa Alavancada deverá oferecer.
A próxima estratégia, que é o que eu modelei e comecei a fazer para uma posição quando ela foi executada envolve o risco de perda adicional e a busca de uma nova posição neutra de mercado com opções de ações. É bastante complexo, e na frente, não é possível definir como cada cenário deve ser confrontado em um nível genérico. Depende muito do índice subjacente com o qual você está lidando, qual a volatilidade, quão longe do que estão os ETFs inversos, etc. Este será o assunto da Parte 2 (inscreva-se gratuitamente para futuros posts) desta série. sobre a estratégia Short End Leveraged Short ETF de Darwin. Resumindo, você redefine a equação com opções (opções de compra ou venda, compra ou compra [depende da situação]) de forma que, se a estratégia curta de ETF curto se esgota em você, você é compensado pela (s) posição (s) de opções sobrepostas. A linha inferior é que você tem que estar preparado para outra corrida para cima ou para baixo, porque este cenário fugitivo pode e ocorre.
Como essa estratégia se encaixa no meu portfólio?
Como mencionei anteriormente, há agora restrições de margem bastante rígidas e falta de compartilhamentos disponíveis para encurtar, de modo que você não pode, inadvertidamente, encurtar todos os tipos de pares sem garantias para respaldá-lo. No meu caso, essas posições vendidas ocupam uma parte de uma carteira de negociação mais ampla que inclui longas posições de ações, opções, spreads de crédito e outras estratégias. Como tal, uma falha de um ou dois pares curtos de ETFs inversos não seria devastadora, nem desencadearia custos de margem que eu não pudesse corrigir prontamente. Eu só quero deixar claro que você não pode abrir uma conta de negociação financiada com US $ 2.000 e gastar US $ 2.000 em pares ETF. Se você está considerando isso, considere como ele se encaixa no seu portfólio mais amplo, se é que o faz.
Por que estou dizendo ao mundo sobre essa estratégia?
Eu não sou o único cara inteligente por aí que descobriu isso. E eu não sou tão inteligente & # 8211; os traders do Goldman e os fundos quant são espertos. Provavelmente existem blocos malucos de negócios que estão sendo explorados diariamente com todos os tipos de derivativos, opções e futuros, complementando essas estratégias. Pense nisso como o fundo de hedge do homem pobre. Como tal, é apenas uma questão de tempo até que esteja lá fora, então por que não ser o primeiro a divulgar e compartilhar o que eu estou fazendo? Eu queria permitir que vários meses de dados testados mostrassem que eu estava colocando meu dinheiro onde minha boca está, e está parecendo bom neste momento. A adoção mais ampla pode resultar em menos compartilhamentos, impactando minha capacidade de continuar a fazer isso no futuro? Talvez, mas provavelmente haverá uma ampla oferta de investidores de varejo desinformados continuando a inundar posições compradas em ETFs alavancados, apesar de minhas melhores tentativas de destacar o Leveraged ETF Riscos de decadência de valor ao longo do tempo.
Isto é o que parece um bom gráfico & # 8211; quando ambos os lados do Leveraged ETF Pair perdem valor durante um curto período de tempo. Apenas deixe andar!
Quando isso acontece, você não pode ficar de braços cruzados e ver sua posição de margem de lucro decolar. De volta à marca de 50% no FAS, eu teria tomado medidas evasivas como descrito acima.
Confira os tickers nas listas abaixo, trace-os lado a lado nos gráficos do Yahoo! Finance ou do Google Finance e mova o controle deslizante. Você encontrará alguns casos em que isso funcionou muito bem e alguns em que você poderia ser pego com uma posição perdida. O truque é encontrar os pares certos e gerenciar a posição de perto, verificando pelo menos uma vez por semana.
Lista de ETF completa alavancada para sua referência. Se você precisar de ETF Tickers para setores gerais, aqui estão mais de 800 ETF Tickers por Descrição.
Este post certamente irá resultar em algumas discussões e perguntas.
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Uma estratégia legal, uma que eu considerei a mim mesmo & # 8230; mas como você disse, com um movimento eficiente, a estratégia realmente quebra rapidamente.
Eu testei esta estratégia para entradas aleatórias e resultados medidos para períodos de vários meses & # 8230; O par curto era geralmente um vencedor, mas quando não era possível olhar!
Eu gostaria de ter uma fórmula / planilha que pudesse prever lucros com essa estratégia, mas eu não era capaz de fazer isso com facilidade.
Parabéns pelo ano e obrigado pelo post!
Eu fiz exatamente essa estratégia com FAZ e FAS. E sim, o resultado líquido foi um retorno de 10% ou mais. Tentei repetir o negócio, mas é difícil encontrar as ações em curto.
Obrigado pela explicação detalhada.
Estratégia interessante. Eu olhei para pegar metade de um comércio similar & # 8211; encurtando a versão curta de um S & amp; P 500 ETF. Em retrospectiva, meus retornos pareceriam muito bons, mas isso é sempre 20/20!
Eu me pergunto quanto tempo retorna como este vai durar nos pares combinados. I feel if enough investors get in on it, it will crowd out the easier profits, or perhaps lower profits to a similar risk/return profile as the normal asset classes they are backing. I’m interested in your unscientific opinion on this one… any thoughts?
I wrote about the leveraged ETF issues in a brilliantly titled “ETFed”
Had I given it more thought, I might have stumbled on to this strategy. Excelente trabalho.
Very interesting article – still trying to wrap my head around this.
Essentially, isn’t this a play on volatility? As I understand it, the under-performance of leveraged ETFs comes primarily from the daily movements that the ETFs try to mimic – the bigger the daily swings, the bigger the deviation from the target return. So as long as you’ve got a market that’s regularly up-and-down, this should be profitable. Accordingly, the amount of leverage in these funds should increase the success of the strategy (this will be more successful with 3x funds, than 2x funds).
As you mentioned the downside risk is in market runs (either up or down). If the underlying index moves the same way in successive days, the leveraged etfs should performed as designed and this strategy would falter. But that risk is mititgated by the fact that successive runs are the outlier, not the norm.
My guess is that the biggest hurdle to this is the implementation (again as you mentioned): finding the shares to short and the margin costs.
Very, very interesting, tho.
I have been shorting FAZ/FAS since late August and I’ve achieved about a 12% return on my investment to this point. My strategy was to ensure that both shares were available in equal dollar amounts before I pulled the trigger. It took me about a week to acquire all the shares I wanted, and its been smooth sailing since.
The one thing I would say is that there are times when there will be big drops followed by big gains in the value of your investment, so it not necessarily for the conservative investor who can’t stomach extreme volatility.
LOL, this is fun - but the key still eludes me… I’m going to figure it out… It’s not really so much about volatility as it is efficiency of movement.
For example, if you initiated this strategy w/ $100k on 3/6/09 your investment would currently be worth a margin call of -$71,887, making for a -171.89% ROI!
Despite that March was incredibly volatile… This is far from risk free, I would understand the math and risks before you jump in.
Hi Johnny, you’re right about starting in March. That was about pivot bottom for the worst decline and then rebound in our generation. That’s the chart I included in the “Bad Chart” example at the end. If you just left the positions open you’d get slaughtered. That’s where the active management comes in. Here’s how you would have made a fortune…Let’s say once FAS was up 50% and running more daily, you said to yourself, “I better take some action here and protect my position from further losses”. You guy and buy puts on FAZ. If FAS continues to run, your FAZ puts come into the money. If FAS falls, the puts will expire worthless, but the double short strategy comes way back into the money because of the early volatility the other way.
While it’s not perfect and you can’t predict what to do in advance, there are ways to offset, and even benefit from a runaway market – but yes, as I stated emphatically throughout, you’ve gotta realize the risks involved and manage the positions closely.
I bought 200 shares of FAZ at 18.60. Hoping to sell off in April for $700 a shares. Sound like a good plan?
Muito interessante. Love the caveats! Clearly reasoned articles like this, with serious discussion of the risks, are a great service – we appreciate it out here.
Hmm, I’m not quite clever enough to follow 100% what you’re doing after the two glasses of red I’ve drunk tonight, but I have a feeling this falls into the ‘picket up pennies in front of a steamroller’ category of investing – I have a hunch you’ll be hit by some outlier and wiped out. But as I say, I haven’t fully digested yet (these ETFs are available here in the UK).
(Sorry, I mean “picking” up pennies)
(Sorry, again, I mean ” those etfs AREN’T available here in the UK”. The motto? Don’t comment after drinking!)
Excuse all these comments (feel free to edit first comment and delete the corrections). I’ve been mulling this over, so came back to read it and realised I’d missed out that whole section on a runaway market! Anyway, that was what I was getting at (your short not covering your long).
Good luck with it.
cool strategy you have there, and easy to apply. obrigado pelo ótimo post.
When do you rebalance the short positions? If you don’t and there is a run on one side, you will not have a market-neutral position any more.
I think rebalancing will mitigate run-away risk also.
Well, I did some testing with rebalancing and it kills the performance of the strategy (even without taking trading costs into account). If you short two leveraged ETF pairs, you express a belief that the market will be flat and volatile. Effectively, you will be swing trading since every run up in one direction will leave you with a position where you profit heavily from a move in the different direction. There are easier ways to do swing trading IMO.
Can the Inverse Leveraged Short ETF Strategy be done with option puts on opposing leveraged ETFs rather than shorting them ? Assuming the answer is yes, what would the advantages & disadvantages be compared to short selling the ETFs ?
February 11th, 2010 at 11:54 am.
@Louis Paul, Actually yes, it can. Here are some problems though. The volatility of these instruments is priced in, so the puts are VERY expensive. Looking back historically at put premiums vs. the 40-8-% decline you see on some of these over a given time period, in some cases, you made a profit, in some cases you didn’t. So, there’s no easy win there. I’d say, perhaps a good time to try it would be.
now when volatility is low (if you expect it to increase), but it would be prohibitive to employ when volatility is high. Remember, you’ve gotta double your money on at least one of the puts to offset a potential total loss on the other, assuming only one comes into the money. If both came into the money, then great.
Finally, for many of these pairs, the one side ran while the other tanked, so you’d have one ETF at $100 per share and the other at like $5. Given the bid/ask spread and small dollar movement on $5 even with say, a 30% decline, you lose a lot of money on the spread and commissions depending on structure. In an ideal situation, they’d both be priced at like $40 or more the day you start a position.
So, short answer is, it works sometimes. But with the double short, you don’t lose any of your own money due to time decay like you do with options.
Finally, (again), you could actually buy a put on the correct side if you have a runaway situation to hedge further declines. I’ll write more on that in the future with a real-life example.
Louis Paul Reply:
February 11th, 2010 at 12:58 pm.
@Darwin, THX – I am trading in retirement accounts which do not allow shorting of stocks/ETFs. But I can go long on option calls & puts.
Grande explicação. I also have been experimenting with an almost identical strategy with several ETFs since last fall. So far so good, but the thought of a runaway market still makes me worry sometimes. I’ve been thinking about buying some far-out-of-the money calls to protect myself.
One thing I’ve noticed that I would add is that for most leveraged ETF pairs, the bear one will tend to under-perform even more-so than the bull one. This is partly because the tracked indices tend to have positive nominal returns over the long term, but even if an index stays flat, it would still be true.
For example, say an index went up about 10% then dropped back to the original level (loss of 9.0909…%).
A double leveraged bull etf would return -1.82% (1.2 x 0.8181… = 0.981818…)
A double leveraged bear etf would return -5.45% (0.8 x 1.1818… = 0.945454…)
A triple leveraged bull etf would return -5.45% (1.3 x 0.72727… = 0.945454…)
A triple leveraged bear etf would return -10.91% (0.7 x 1.2727… = 0.890909…)
For this reason and because I’m a little more scared of a runaway bull market that a runaway bear market, I have in some cases initiated short positions only on the bear etfs, or made my bull positions smaller. This definitely makes things scarier day by day though since I don’t have as many losses and gains offsetting each other.
February 24th, 2010 at 11:35 pm.
@Brendan, if you did the opposite; i. e. market went down first, then up, you’d get the inverse. So, in effect, they’re mirror images and neither the bull nor bear presents an advantage over the other. They both stink to hold long term. And both are beautiful shorts.
March 5th, 2010 at 6:30 pm.
@testing2, It doesn’t matter which happens first because multiplication is commutative (1.1 x 0.909 = 0.909 x 1.1). If a stock moves around over the course of 2 days and ends up where it started, the move up will always be a greater percentage than the move down.
The only reason it might matter which happens first is that you could be forced to liquidate if you lose too much before you win.
Sweet to find this. Amazing actually. I mean I knew there were people out there evaluating these paired variations but this is great, great stuff.
I began testing a long variant of (more or less) this strategy with a covered call element for a couple months with FAS/FAZ . The initial idea was to go long on both a long & inverse set (dollar equal on each side – well, as close as you can get anyway) then sell calls on each side that are 10% or so out of the money.
With the built in decay of these vehicles I’d obviously prefer a put based strategy. Trouble is I have Louis Paul’s problem – this is an IRA I trade in. So I can only sell covered calls. Meaning I have to own 100 sh. lots of the underlying.
The goal was also to implement this in a way that minimizes market exposure time while still netting high enough premiums from the call sales to make a profit.
My arbitrary exposure time was 10-15 trading days prior to the option expiration date – Time decay seems to significantly accelerate after this.
The ratioanale for selling the calls at a certain % out of the money – 10% was kind of an arbitrary number – I’ve just started playing with this – was to be able to buy out the option (buy to close) on the losing side at maybe 20% (or significantly less depending on time decay) of what I sold it for with the hope that the 10% gain on the winning side would offset the 10% loss on the losing side and I get to keep the premiums. (minus the 20% or so buyback on the losing side’s call)
Long story short: it can work – which is to say it did turn roughly a 5% profit per trade (set) for the VERY small data set I have so far (2 expiration dates). Any serious volatility can kill it if you don’t buy to close the call and dump the underlying shares of the losing side fast.
I’d go into the math and what I’m starting to realize about when to sell the options to maximize premiums (options pricing on leveraged vehicles can be pretty inefficient and you can take advantage of this I think) and some other variables but my girlfriend is staring at me to go out to the pub. haha
VERY interested on anyones thoughts on the approach I just described (or rather typed semi-coherently to appease said girlfriend).
February 24th, 2010 at 11:44 pm.
@Chris, Glad you like. Hope your girl understands how excited guys get over annualized double-digit returns. If only they understood (LOL). I know ladies, you’re traders too – but take a look at the proportion of male commentors here…
February 25th, 2010 at 11:32 am.
@Darwin, Haha. Yeah she’s never understand my fascination with the market. There ARE some badass female traders out there though.
Good Article; and good job explaining the risks with the runaway market. I wonder if you’ve considered a similar trade with USO. My personal experience with USO was buying USO when a barrel of oil was $40, and suffered a loss even when oil recovered to over $55 a barrel. It was frustrating to lose money when my thesis was correct. USO suffers the same deterioration fate since they roll over every month (Contango degrades, backwardation enhances). I just seem to find a good proxy for the price of oil to balance out the USO short. Seus pensamentos?
February 24th, 2010 at 11:41 pm.
@tim1198, The USO issue’s a bit different. That’s a tracking error (percieved, not real) due to rolling futures contracts when oil is in contango (which it is frequently). This is different than losing value over time. Also, there’s no inverse to short with it. Good thinking though in pointing out that futures-driven ETFs have been losing track of their underlying commodities.
This is actually Darwin’s article and his explanation.
I just added some thoughts on my little variant of market-neutral ETF strategies. I also don’t understand your question. Desculpe & # 8211; what is your strategy with USO?
February 25th, 2010 at 6:36 am.
Hi: My question with USO was: Is there a strategy opportunity, given that the ETF doesn’t track its underlying commodity. I was looking for a strategy, or an oil proxy so that I can buy the proxy and short USO. But as Darwin pointed out above, there’s no inverse to short with.
I hope this clarifies my question better.
February 25th, 2010 at 11:24 am.
@tim1198, Gotcha. I still don’t understand contango – although I haven’t really tried to – which is probably why I didn’t get what you were asking. Desculpa. Commodities ETF’s seem to be different animals to me as far as how they move (they seem to trend harder, faster and longer). And I haven’t considered them for these types of strategies. Although with the right strategy those traits could present an opportunity.
Silver is particularly interesting to me. I would love it if someone could explain to me how the silver market is rigged so I could front-run the big boys. I know their are silver traders out there who have a good idea how and when it’s being manipulated. Alguém aqui? Alguém? Bueller?
February 25th, 2010 at 2:19 pm.
I learned about USO Contango about the same way I learned how to play Poker; by putting money in and losing it 🙁
There’s a short article that does a good job explaining this condition. Eu espero que isso ajude.
February 25th, 2010 at 5:32 pm.
@Chris, Tim – thanks for the link. I did execute a buy-write on a silver 3X without doing my homework. Let’s just say I paid a similiar “trader’s tuition” for a lesson on how fast and hard those markets can move if you’re only on one side. I felt like that quote from “Rounders” : “If you look around the table and can’t spot the sucker, You are the sucker”.
I just read your article and sounds quite a good strategy, all your bias about what could happen with the market is absent.
However I don’t understand why it’s impossible to make more than 100% by shorting any particular instrument. Let’s say a short FAZ at 10 and then a year latter it goes down to 0.1, wouldn’t I have done %100+ on that one?
thanks for sharing your thoughts.
February 24th, 2010 at 11:42 pm.
@Maxi, An investment can never go past 0 (into negative territory when long), so the most you could make on say, $10, would be $10 – for a 100% gain.
Hey what graphing tools are you using in the above examples. I’m looking for a way to visualize this better rather than just a series of cells in Excel.
And when are you putting up part II ? C’mon man this is good stuff!! I’m curious to see what else you’ve researched.
February 24th, 2010 at 11:45 pm.
@Chris, Chris, glad you enjoy. For charting, I simply used Google Finance and copied/pasted screenshots.
For an update, did one tonight! Check out post from 2/25 – new shorts revealed and a YTD hypothetical portfolio shows more double digit returns if starting in 2010.
February 25th, 2010 at 11:36 am.
I have been looking at this strategy and think there is a lot of promise to it. However, what worries me most is the change in the ratio of nominal prices between the opposing ETFs. Part of the beauty of the opposing ETFs is that they are a hedge for one another. However, you have to have equal positions of each to be truly hedged. If the market moves significantly (even if not a “run away” as you put it), you could end up with double exposure to one side or the other and the value of your position would be much more closely tied to that particular ETF. It seems like you would have to keep an eye on the trade and rebalance when necessary. Have you thought about rebalancing either periodically or when the ratio of the nominal values of the ETFs changes beyond a certain degree?
March 11th, 2010 at 9:38 pm.
@Andrew, It’s a tough balance. On one hand, if you keep rebalancing every time the pairs get a little out of whack you end up negating the benefit of each side dropping each time the underlying see-saws. Conversely, if you have like 90% loss on one side and 40% gain on the other, if that 40% side keeps moving in the wrong direction, the 90% loss side contributes an ever decreasing amount back to hedge so you lose money (as the “runaway market” scenario evolves).
That’s where you’ve gotta get creative and each situation’s different so I can’t outline EXACTLY what to do, but some thoughts (that I’ve used) – you can just buy the underlying index itself if it ran away long. Por quê? The underlying index is a straight 1X. It doesn’t lose value over time. If the index stays flat, both pairs continue to gain for you. If the index falls, you lose value on your 1X but make it back on that runaway side coming back to earth. If the index continues to run, you make money on the 1X while losing on the pair. It’s not a great solution because you may be “treading water” for weeks or months and you’ve gotta calculate what the right amount is to buy. However, it keeps you in check. Eventually the trend will break. They always do. That’s when you make huge gains back. The same concept can be applied with options but now you’re adding even more complexity due to volatility and time premiums. But I’ve used them too. I’ll continue to post what I’m doing with my particular positions in real time – Fique ligado!
If this hedge position held long term, seems there would be tax advantages. If the short half of the position that had lost money was covered before the end of the tax year, and the winning position held, then there would be a cap gains loss that could be used against other gains in that year.
To avoid one side exposure on the other held (winning) short position, one could buy the same position long to hold for the 30 day tax sale wash period, then sell the long after 30 days, and replace the covered short position in the side that had lost a month ago.
That way the cap loss would be realized, and the cap gain continued.
Does this sound right?
A quick question on rebalancing. How often do you reset your positions to balance one another. Say one position is up 20% and the other is down 5%. Would you rebalance? Thanks for the strategy btw? It is very interesting. BKL.
Hi All, How about this? Buy furthest out and deepest ITM put and call on two opposing 3X funds. (This would cost minimum time value loss. Replace when new further outs become available.) Using long calls for stock substitute and long puts to nullify any fund price movement, sell calls three months out and three dollars OTM. (ala covered calls) When one short call falls, say fifty cents, buy back and sell again at three dollars above current fund price and three months out. Gains result from rolling down calls and time decay on both short calls. Possible loss from sustained move in either direction. If advancing fund gets ITM, then, if short call is exercised, long call on that fund would be exercised by broker, stock bought at current price and delivered to short call buyer at his strike price, resulting in a loss. This would have to be managed. Purpose of long calls and puts being three months out and three dollars OTM is provide time and space for volatility to work favorably and also to minimize likelihood of advancing fund getting ITM and being exercised. Clive.
clive collins Reply:
Where I said “Possible loss from sustained move in either direction” should have said “from a sustained move up by either fund”. Moves down are fine. Clive.
I’m so glad I found this site. I’ve been thinking about this concept for a year, but never heard anyone else talk about it before. Obrigado!
I understand the basic concept, but I’m a bit uncertain about the math regarding balancing the ETFs. Especially when I could not buy them on the same day.
Let’s say I shorted 100 shares of SRS @ $29. It is now @30 and I’m down -$100.
Finally some URE is available to short, and it is trading at $34. How may shares of URE do I need to short to balance the 2 pairs of ETFs?
August 20th, 2010 at 10:36 am.
sell 100 of the lower priced stock and low price divided by high price times 100 of the higher priced stock.
30/34*100= 88 of URE indeed…
Are you still tracking this strategy? I haven’t seen an update in a while.
Looks like this will win most of the time, also I would suggest that we use calls to protect against massive in one direction. Example if FAS is at 20.11 when you begin to short buy a cheap call at say $ 28 or 27. This way even if the ETF goes 35% in one direction the max loss is capped off. Ideally I would suggest that short the ETF pair and buy a way out of the money call and a put. This way you are completly protected against any massive moves in the market like Mar 09 or Sep 08.
Also I think we may have to reset the position every month not sure if that is a good idea. By reset I mean get rid of the ETF pair, take profit/ loss and start over again with options. rvharry79 at yahoo.
I am not a expert please evaluate your own risk before shorting because shorting can lead to infinite loss.
September 1st, 2010 at 10:49 pm.
Could you not do the same with just placing an order to cover the short position if it goes up?
For example: I bought 200 shares of SRS at $28, now it’s at $22. I have a cover order for $27. Is that not safe? Should I try a put/call instead?
December 8th, 2010 at 10:00 pm.
I dont think I understand your question, please let me know the details if you are still interested.
mail id rvharry79@ yahoo dot com.
Hm.. it’s a very interesting topic. I had done research myself and then finally found out this topic.
I think this strategy will still work, but the key is to have the exact number of lot when shorting both positions. This will overcome the runaway market condition like on the Bad Chart mentioned by Darwin.
For example, based on the historical data from Yahoo Finance. The close price.
On Mar 6, 2009. FAZ = 104.7 FAS=2.64. Total Combined Price: 107.34.
On Sept 1, 2009. FAZ= 26.39 FAS=68.1. Total Combined Price: 94.49.
During this time, the runaway scenario is definitely happens. But because we put the same number of lots when shorting, we’ll still end up with profit.
Let’s say we’re shorting 100 lots for each FAS and FAZ.
The profit/loss for from Mar 6, 2009 – Sept 1, 2009 will be:
FAZ = 100 * (104.7 – 26.39) = 8131 (Profit)
FAS = 100 * (2.64 – 68.1) = – 6546 (loss)
Total = 8131 – 6546 = 1585 (Profit)
In this scenario, we’re making a percentage profit of (107.34-94.49)/107.34 = 11.97% within 6 months period during a runaway market.
That’s why I think this strategy will work regardless on a runaway or swing market condition.
O que vocês acham pessoal?
What happens if a stock reverse splits?
I shorted 200 SRS @ 28 = $5600. I know from history that when the price gets too low it will reverse split. What will happen to my shares and money when SRS reaches $5 and then 1:10 splits to $50?
Guys, this is simply the effect of compounding 3x daily returns. You can simulate the returns of these ETFs by going long(/short) 3x your capital of the end of the last day every new trading day.
I. e.: I have 100, I go long 300 with my 100 3x leveraged. Market does -1%, then my position goes 297, so my capital at the end of day is 97. Next day I go long 291 (=97 x 3) because I only have 97 capital.
This way you have 3x the daily returns. When you look at these ETF’s returns, it’s quite close to that strategy (same for the short ones but other way around).
Now the effect is that if there are big movements that are reversed in the underlying, this will indeed make the ETFs lose money. If there is a clear trend without big mean-reverting moves, you’ll not be seeing that effect.
What you guys are doing is basically speculating on mean reversion of some market. This is quite dangerous when you consider the secular bull/bear moves that markets can make for long times. Never forget Keynes: market will stay irrational longer than you stay solvent.
September 10th, 2010 at 3:58 am.
Um, I don’t think that’s the same thing. I think the difference is that we’re playing with the built in decay of every ETF.
Por exemplo. If the stocks the ETF is tracking goes up 5%, then the ETF itself will only go up 4.9%, and if the stocks the ETF is tracking goes down 5%, then the ETF itself will go down 5.1%.
Meaning the ETF will always perform slightly worse then the stock it is based on. And by shorting the ETF over time, you can capitalize on this decay.
September 10th, 2010 at 6:47 am.
@Ajdedo, No, it’s because you compound 3x daily returns, look:
Underlying does : 3%, 5%, -2%, -3%. Total over 4 days : 2.8%
ETF does : 9%, 15%, -6%, -9%. Total over 4 days : 7.2%, while you’d expect it to do 3*2.8% = 8.4%.
This is the “decay” you guys talk about, but it’s just a mathematical property of compounding. Now look at what happens in a “secular bull” market:
Underlying does : 3%, 5%, 2%, 3%, so : 13.6%
ETF does : 9%,15%, 6%, 9%, so : 44.8%.
This is MORE that 3*13.6% = 40.8%. Over long periods of time this effect is huge of course, so beware, if you start this strat at some wrong point, you’ll be killed by margin calls on your short of the trending fund that gains huge amounts in that sort of environment.
This strategy is of course not to be dismissed, but you must understand that you’re actually speculating on mean reversion. This can be rational of course, because if you know some volatile market that cannot trend off too far from some fundamental value, you can do this, but make sure you can survive some move until the correction comes that will make you’re positions profitable. Anyway there is more risk in this strategy than you think.
Okay, so what would you do?
I shorted SRS @ 26.94 and it is now at 21.91. That’s a 22.5% gain. I have a $5 VTSO just in case the price spikes.
1) Stay the course with SRS only… keeping the VTSO.
2) Short some URE to balance it off… dropping the VTSO.
3) Do something else….
Yeah well depends what you want to do… I don’t know anything about the US real estate market/companies that SRS tracks… Basically you’re long real estate 3x more or less now, while being short this “decay” if there’s a mean reverting market, but also short this amplifying effect if there’s a major down move in real estate stocks. Now this no problem because you got this stop order that will stop you out whenever real estate drops.
To me this strategy seems ok if you’re bullish/neutral on real estate and not expecting rapid down moves in real estate so that you can profit from mean reversion over the long time without being stopped out. The problem I think is that you will tend to be stopped out, and that’s very expensive, because if theres a correction after the move, you would have made a lot of money on the reversion, but you got stopped out…
Thanks for the interesting article and trading concept. I’m a fan of the 3x ETFs, but there must be a more efficient way to play the degradation in share price. It almost seems you could just use a covered call and accomplish the same.
I am currently working on a speculative trading strategy that only trades 3x Leveraged ETFs for smaller account appreciation. If anyone is interested you can check it out at my website.
Good article, but investors really need to consider the costs involved. The SEC mandates that when triple-leveraged ETFs are sold short, an investor has to keep 90% of the cost to cover the short position in cash in a margin account. If the investor already has enough cash in his account to meet this 90% value, then this isn’t going to be much of an issue. However, if the investor does not have sufficient cash, the investor’s brokerage will borrow the shortfall on the margin, and those margin rates could be 8 or 9% or higher, depending on the amount borrowed and the particular brokerage.
Instead of shorting both the bull and the bear leveraged ETFs, why not simply short the bear leveraged ETF? I have done this myself and found that this strategy works best with leveraged ETFs that track indexes having a high beta relative to the overall market. For example, a triple leveraged bear ETF that tracks a volatile index such as an emerging markets index or even a small cap index.
One important observation I have made is that for practically every major equity index, there are more days on which the index rises than there are days on which the index falls. I also noticed that magnitude of the drop on negative days tends to be larger than the magnitude of the rise on positive days. So even in a bear market where the overall index is dropping over time, there may be more up days than down days. The inverse, of course, is necessarily true for triple leveraged bear ETFs.
This observation is relevant because triple leveraged ETFs appear to decay much more quickly when there are more negative days than positive days. I cannot really explain why this is the case, but even if a market that is relatively flat for a long period of time, the bear triple leveraged ETFs seem to decay more quickly than the bull triple leveraged ETFs because there are more down than up days for the bear triple leveraged ETFs even if the underlying index finishes at the same level as where it started.
One caveat of only shorting the triple leveraged bear ETFs is that during short periods of time, the ETF can quickly move against you, although over sufficiently long periods of time, the ETF will revert to its long-term downward trend. Investors also need to make sure than they won’t be forced to buy to cover a short position at an inopportune time – so investors should consider the total number of shares outstanding and the current number of shares sold short prior to implementing a shorting strategy themselves.
I follow the basic tenet of this post, but nothing is quite as certain as it appears, in life. As noted previously, the key issue is one of reversion from the mean and of compounding of either positive or negative returns, to produce most of the times yields that are lower than the corresponding index yield.
One point about the FAZ vs. XLF plot during the period March 2009 onwards. First, the index corresponding to FAZ is the Russell 1000 Financial Services (RGS) Index ($RIFIN. X). Comparing FAZ to XLF is not strictly correct.
The sharp decay during the April and May month corresponds to a sharp drop in volatility – as rightly said – determined by a change in accounting rules (i. e., mark to market vs. mark to fantasy) – a historical, one of a kind event.
Conversely, to a rise in volatility should correspond a higher risk that the underlying index would decrease, with higher expected prices for FAZ. It all depends on the magnitude and intensity of the change in volatility and of the corresponding market drop.
I firmly believe that, should a rise in volatility reoccur, with herding behavior on the selling side of the index, instruments like the FAZ would have a sudden, certainly temporary but however extremely significant upward jolt in price.
The compounding works in two ways – it erodes returns when volatility drops, but it enhances returns when volatility increases – making the current market price relative to volatility attractive.
Nobody can rightfully predict the upward movement should such an event occur, since it is more an issue of mass psychology and herding than anything else. I would however expect that FAZ would move in an exponential fashion (caused by the herding phenomenon) to upwards of several hundreds USD – albeit for the relatively short amount of time of the fear outbreak.
Ludo, I would short a bear leveraged ETF tracking a broad index, not some niche sector. I read somewhere that a lot of people couldn’t short FAZ because there were insufficient shares available for shorting. FAZ had an incredibly large volume of shares trading hands every day – the number of shares traded on a daily basis was much larger than the total number of shares outstanding (because day traders were trading the same shares over and over in a day).
Look at how other triple leveraged bear ETFs have decayed, such as EDZ, TZA, and BGZ. I suspect that over the next ten years, as we have a longer track record for daily movements of theses leveraged ETFs, we will see that some of the leveraged bear ETFs may gain 200% or more during bad bear markets, but they quickly lose those gains and revert to their downward trend over time.
Has anyone been shorting these long term and holding? I am looking for the most stable, most reliable broker, once that will not subject me to buy-in’s. I have been with IB and they have forced me to buy in numerous times.
Sim. I’ve held a short position of SRS with Questrade for about 5 months now. I periodically add to (short) my position several times within that time. I have never been asked to cover. Probably because the position has always been in the black.
Hi, The same problem with buy in with IB.
I had sold a in the money call and one day I saw that I was shorting the stock.
Is there a list of leveraged ETF’s that are ‘shortable’? I have tried many times and every time I hear ‘not enough shares available to lend’.
I get ‘not enough shares available to lend’ with SRS 19 out of 20 times. I just keep trying day after day after day. Eventually about 1 day a month the order with go through. On that day I just pile up as much as I can afford regardless of the price.
I have been thinking about this strategy for a couple of years now, but everytime i try to test it out i can’t get the shares to borrow. I definitely think you need to rebalance ocassionally based on pre-set rules. I ran some regressions and have the rules all set, I just can’t seem to get the shares to short…
Hey Procure A good Locality Connected with how to rid belly fat.
thank you for this article. i have been thinking about the double short on these, though never followed through (had the time to make money day trading them). you did a remarkable job putting together an article with charts for an idea many people have been thinking for some time. this is a almost two years since you published your work, and in that time it has become increasingly easier to short these etf’s (due to far larger volume) thanks again. easy way to pay my mortgage, every month, gaurenteed. anyone who doesn’t understand it, or is too scared because of volitile markets need to wake up. if you got 20k like me. you will on average make 7-8k with little work. thanks again buddy! way to spot something few see.
Truman Waldrup Reply:
February 2nd, 2012 at 5:06 pm.
@Ian Stack, what time frame do you use to day trade etf and do you have a favorite etf and technical indicator you like? Thanks Truman.
Ian Stack Reply:
March 13th, 2012 at 9:27 pm.
Re: A few months in to the double etf strategy, and I have picked up on a few etf pairs that seem safer than others. Right now the market has been on a complete roar and I can see how a runaway market could be a huge risk with etf’s that strongly correlate with the broader market. I would stick with multiple pairs of commodity 3x etf’s. To answer your question, I havent day traded many etf’s this year. When i do, my time frame is a few days at most. technical indications seem to have eroded lately due to the large volume of computer trading as well as low fed interest rates. if you use this strategy, go with gold or energy etf’s. I like Dust-Nugt, Erx-Ery. Boa sorte.
Can’ t you just buy a put option instead so your losses are limited to the premium paid for it or am I missing something?
I don’t see why a runaway market should be a risk if you continually rebalanced the long and short ETFs.
Another way to mitigate risk without the trouble of rebalancing would be to go short a 3x ETF and go long 3 times that amount of a similar, but un-levered, ETF.
Am I failing to understand something?
January 4th, 2015 at 4:29 pm.
@Mark Uzick, continuously rebalancing would mitigate the runaway market risk but also counteract the decay that is the source of returns for this stategy.
Buying 3x an unleveraged etf would not work forever as a hedge because once things start to run away from you, your position in the unlevered ETF will no longer be the right size compared to the leveraged ETF. And if you kept rebalancing to make it so, you would destroy your source of returns just like in the earlier case.
Mark Uzick Reply:
January 4th, 2015 at 11:20 pm.
I understand that you’d eventually have to rebalance a 3 times 1x long to a 3x short, but only if they move in a bearish direction for an extended amount or when there’s enough decay in the 3x (I. e., profits.) to put them out of balance.
What I don’t understand about your reply is why you believe that your individual actions in your own account would have any effect on the continuous decay of leveraged etfs – their overall, combined value would continue to shrink, but without the risk of the runaway effect. Think about it: you must eventually take your profit and establish a new position – that’s no different from rebalancing, except that you are taking more frequent, smaller profits and don’t allow your losing side to get big enough to overpower your winning side. It also has the benefit of allowing the continuous reinvestment of profits, so that your position scales up, allowing your gains to increase instead of diminishing.
January 5th, 2015 at 1:57 pm.
@Mark Uzick, I think an example would best illustrate what I mean. I’ll using pretty extreme swings with a single rebalancing to illustrate the point, but it also remains true with more realistic scenarios of smaller swings and longer time frames…
Say you short 100 shares of both FAZ and FAS when they are both $100. You collect a total of $20,000 cash and are short $20,000 ETF value.
Say the 1x financial index XLF drops 10% per day for 3 straight days (30% moves for the ETFs), then rises 11% per day for 3 straight days (33% moves for the ETFs):
Day 1: XLF $90, FAS $70, FAZ $130.
Portfolio value: 100 x $70 +100 x $130 = $20,000.
Day 2: XLF $81, FAS $49, FAZ $169.
Portfolio value: 100 x $49 + 100 x $169 = $21,800.
Day 3: XLF, $72.9, FAS $34.3, FAZ $219.7.
Portfolio value: 100 x $34.3 + 100 x $219.7 = $25,400.
If you don’t rebalance, your portfolio will benefit from the decay that occurs when financial stocks recover:
Day 4: XLF $80.92, FAS $45.62, FAZ $147.20.
Portfolio: 100 x $45.62 +100 x $147.20 = $19,282.
Day 5: XLF $89.82, FAS $60.67, FAZ $98.62.
Portfolio: 100 X $60.67 + 100 x $98.62 = $15,929.
Day 6: XLF $99.70, FAS $80.70, FAZ $66.08.
Portfolio: 100 x $80.70 + 100 x $66.08 = $14,678.
In 6 days, the portfolio gained 26.61% ($5322) by capturing the decay. However, if you had rebalanced the $25400 equally after day 3, your results would look like this:
End of Day 3: Switch to 370.24 shares FAS ($12699.23), and 57.81 shares FAZ ($12700.86):
Day 4: XLF $80.92, FAS $45.62, FAZ $147.20.
Portfolio: 370.24 x $45.62 +57.81 x $147.20 = $25,399.98.
Day 5: XLF $89.82, FAS $60.67, FAZ $98.62.
Portfolio: 370.24 X $60.67 + 57.81 x $98.62 = $28,163.68.
Day 6: XLF $99.70, FAS $80.70, FAZ $66.08.
Portfolio: 370.24 x $80.70 + 57.81 x $66.08 = $33,698.45.
In this case, rebalancing after day 3 causes the worst possible result. If you run the same scenario rebalancing every single day, you end up just sitting at $20,000 the whole time (minus transaction costs). There’s no scheme that gets you a risk-free gain.
You are only showing the runaway effect – NOT DECAY. Your examples are using a perfect conversion of 10% to 30%, but with decay it will actually be greater than 30% to the downside and less than 30% to the upside.
What you’ve shown is that the runaway effect cause losses in a one way market, but ends up with gains in the case of a retracment – you don’t need a pair of levered etfs for this effect; a pair of 1x etfs will do the same thing, but slower.
Playing the runaway effect is a gamble; rebalancing eliminates this risk, and would seem – though hard to believe – to make profit a certainty.
January 6th, 2015 at 3:04 pm.
>You are only showing the runaway effect – NOT DECAY. Your examples are.
& gt; using a perfect conversion of 10% to 30%, but with decay it will actually be.
& gt; greater than 30% to the downside and less than 30% to the upside.
I’m not sure where exactly you think the decay comes from. The daily 2x or 3x behavior of these fund may not translate perfectly, but it is close enough to not be a major source of decay (perhaps 1 or 2% per year at most). The daily reset / rebalancing done behind the scenes in these funds IS THE SOURCE of what people called “leveraged decay.” If you do your own rebalancing, you work against the cause of that decay. Try any example yourself and you will see. You can put the up and down days in any order.
What you are calling “decay” is actually just the effect on a portfolio that is simultaneously holding both bullish and bearish etfs of the same underlying equities. As you have skillfully illustrated, the path of the underlying is what determines profit or loss – this is just an effect of math, not the decay caused by leverage; proof of this is shown by the fact that the path dependent effect you have illustrated will happen in non-levered etfs. Any levered product will undergo time decay – you can think of a levered etf as something not so different from an option; and the time decay of an option is far greater than the 1 or 2% that you cite – it’s of an order of magnitude comparable to the decay of levered etfs; and that makes perfect sense – there is simply no way to evade time decay in a levered product. That the etf must rebalance to maintain it leverage ratio or whether you rebalance your portfolio to neutralize the effect of price path will do nothing at all to affect the time decay that’s inherent to all leveraged products.
January 6th, 2015 at 6:34 pm.
OK, I guess we are just referring to different types of “decay”. I am using it the way many articles on 2x and 3x ETFs do when illustrating the ways losses occur.
I agree I appear to be low on the 1%-2% estimate – I just checked the last 3 months of daily FAS/FAZ moves and the daily leveraged decay of the pair (as you use the term) seems to be more like 6% annualized. How much of this is due to the time decay of options/futures, and how much is due to the transaction costs of rebalancing, I couldn’t say.
One thing to watch for though is that if you use a margin account to short the ETFs and rebalance frequently, you may incur your own set of margin and transaction costs that eat into that (6%?) return pretty heavily. Maybe to the point where just holding a bond or CD would be just as good depending on interest rates.
Mark Uzick Reply:
January 6th, 2015 at 8:05 pm.
@Brendan, Can the annual decay of a 3x levered etf really be so low? That would mean that it would be easy money to simply sell yearly puts of a strike that gives 3x leverage against the underlying, hedged by the reverse levered 3x etf. The puts, while they may not consist of 100% time value, would have an overall time decay of far greater than 6%, as their time value portion would have a decay of 100% annually.
Also: in your “pairs strategy portfolio” your examples would tend to support the notion of time decay as something far greater than 6%.
Mark Uzick Reply:
January 6th, 2015 at 7:39 pm.
@Mark Uzick, BTW: to say this in a way that might be more clear: the 1 – 2% of decay caused by rebalancing is a management and transaction expense that’s ubiquitous to all portfolios – it has nothing to do with leverage. While the rebalancing that you do in your account will negate the advantage of shorting to capture the rebalancing expenses of an etf, it’s the inherent time decay of all levered products (Probably between 20% and 60% anually.) that’s the real issue here.
Hi Darwin and all.
I have been trading this strategy using FAS and FAZ since 2010/11 with some variations, including letting the ratio between the leveraged ETF pairs run somewhat and hence introducing an element of directional trading, in addition to profiting from the leveraged decay which was the original point of this strategy. I have been documenting my progress since late last year at my site so you are all welcome to check it out.
January 22nd, 2016 at 2:03 am.
@RetailTrader, It would be very interesting for me to have a Look to your Resultat. Unfotunately there is no functioning link behind your Name “realtrader”.
What about just using standard PUT options rather than shorting?
I’m trying this now with an equal value of PUT options on both FAS and FAZ that are 6 months out and about 15% in the money. This brought the delta of each option to around .50. The volatility is a bit different at around 50 on FAS and 60 on FAZ.
Decay on the options should be minimal since they’re 6 months out (January ’16).
Does anyone have any thoughts on this strategy?
Apart from fees and differences in when rebalancing, shorting a short etf should be equivalent to being long on a long etf.
Thus does this strategy exploit “collecting” fees instead of paying them as you are shorting?
An interesting thing to note is that long term negative effects of leveraged etfs is a myth. See this brilliant paper: papers. ssrn/sol3/papers. cfm? abstract_id=1664823.
The point of the paper is that although volatility drag is a short term downside, having a high leverage is still worth it due to long term rising market trend, which is precisely what is the risk in your strategy. So over the long term the “black swans” will according to this paper outweigh the short term benefits of shorting the volatility drag.

Mean reversion strategy ssrn

Rather than posting a new topic every time, may as well just post papers and links here. Please keep it to concrete strategy ideas, the more explicit the better, and preferably those that could be implemented in Quantopian!
Good idea and nice paper.
The VIX Futures Basis: Evidence and Trading Strategies should be able to implement something similar using the VIX short-term futures vs medium-term-futures ETFs, and pulling in the VIX prices with fetcher.
EDIT: corrected link, h/t Dennis C.
I've never seen that Turnkey Alpha site before. O que é isso?
And do you mean the Academic Alpha section? (link requires sign-in)
Yes I did, my mistake. It's a free sign-in. They are one half of the guys who wrote Quantitative Value (the other half is Empiritrage), and they regularly write little papers about the sort of exploitable opportunities people here might be interested in. I am not affiliated with them.
Not sure if you've seen it. if not:
If you search the Quantopian Community forum with the keyword "OLMAR" you'll find several threads with Quantopian implementations. I also have some code that I can share.
The author's website: cais. ntu. edu. sg/
Not a trading strategy itself, but interesting ideas about trade sizing.
Some random links.
Tons of strategies here to try out.
Obrigado. would you be willing to provide some specific recommendations from the list above? What are the top 3 you'd recommend reading through carefully, that could be coded in Quantopian (without a heroic effort)?
Sorry, I haven't read them all! Whatever suits your temperament I guess!
I think good old trend following is always fun. It's very practical. In case you haven't checked it out, I noticed that Claus Herther has a great starting point. I'd like to add in measurement of the slope of a trend, momentum, and williams to help add some "trend anticipation" into a standard trend following system.
Just stumbled upon this goldmine of hundreds of papers, most with pdf links, on a variety of topics:
Also, the Kaggle tutorials / free Books are well worth a look:
Note he doesn't actually give the formula for this indicator, so one would have to do some work to try and figure out what he's talking about.
Hi everyone, is ist possible to program the black litterman approach with Quantopian? Tips are highly welcom. Agradeço antecipadamente por sua ajuda.
It should be possible, someone wrote a minimum variance portfolio re-balancing algorithm a few months ago. You'd need to use fetcher to get your index weights for your prior, make sure to fetch them "as-of" the date you are at in the back-test. Then you "just" need to do all the bayesian matrix manipulations, along with your input market views/shades, come up with the target weights, then submit orders to move from your current portfolio to your target portfolio.
It would be an excellent demonstration and example, perhaps you can get the quantopian folks to code it up!
Thank Simon for your comment. I wrote my last thesis about BL so I have the theoretical background. But to be honest with you, I am not quite good in programming. Nevertheless I will try and let the community know.
Cheers Grant. You made my day. I would appreciate more of articles like that.
Thomas Wiecki posted the article first on quantopian/posts/interesting-papers. I just copied the link here. If you have comments on the article, I suggest posting them to Thomas' fio.
Mebane Faber has a few interesting papers at Cambria Investments' website cambriainvestments/research/, especially one of Relative Strength strategies papers. ssrn/sol3/papers. cfm? abstract_id=1585517.
Several of Mebane's systems were implemented on quantopian six to twelve months ago, global TAA, relative value, relative value + TAA. I also wrote some picloud+zipline brute force optimization of the TAA model. If you search for Mebane you should find them. I don't know if they still work in the backtester.
The gist: implement a market-neutral high vs low momentum strategy, but trim the shorts as the market drops. This will, of course, add a strong long-term long-biased mean reversion factor to the system.
Being on the Field When the Game Is Still Under Way. The Financial Press and Stock Markets in Times of Crisis.
This paper looks at the relationship between negative news and stock markets in times of global crisis, such as the 2008/2009 period. We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation. Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets. We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility. Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
In response to Simon's post of Profitable Mean Reversion after Large Price Drops: A Story of Day and Night in the S&P 500, 400 Mid Cap and 600 Small Cap Indices , has anyone coded an algo that replicates the strategy outlined in this paper that they wouldn't mind sharing? There is a clear and consistent dropoff in return as years progress from 2000 toward 2010, and I'm curious to see if this trend has continued in the three years since.
I've noticed that the many cryptocurrency exchanges out there have a significant spread. The spread between Mt. Gox and BTC-e, for example, is typically $100, and can go even higher if Mt. Gox has a surge. That's not even getting into the opportunities for arbitrage trading BTC to LTC (litecoin) and other cryptocurrencies that largely follow the BTC market trends. Personally I'm fascinated by it.
I found this overview of quant investing by Max Dama decal/file/2945.
At page 16 he very briefly explains a possible trading idea through the exploitation of the "first day of the month concept".
"The First Day of the Month. Its probably the most important trading day of the month, as inflows come in from 401(k) plans, IRAs, etc. and mutual fund have to go out there and put this new money into stocks."
Trading idea one:
"Over the past 16 years, buying the close on SPY (the S&P 500 ETF) on the last day of the month and selling one day later would result in a successful trade 63% of the time with an average return of 0.37% (as opposed.
to 0.03% and a 50%-50% success rate if you buy any random day during this period)."
Trading idea two:
"Various conditions take place that improve this result significantly . For instance, one time I was visiting Victors office on the first day of a month and one of his traders showed me a system and said, If you show this to anyone we will have to kill you.
Basically, the system was: If the last half of the last day of the month was negative and the first half of the.
first day of the next month was negative, buy at 11a. m. and hold for the rest of the day. This is an ATM machine.
the trader told me. I leave it to the reader to test this system.""
So e. g. if at 31th of march at 12:am the choosen equity has a negative return for the day and the day after it has a negative return until 11 a. m.
then buy and hold until close.
I tried this using excel and intraday data I got from a russian website giving away free historical prices for the 40 most traded stocks in the US, but obviously.
quantopia is a much better way of trying this simple strategy.
The few stocks that actually had this pattern of negative-negative->buy-hold until close showed a small positive gain.
I didn't calculate the sharpe ratio, but my thinking is that if the sharpe ratio is high and you do this 12 mths a year and use a healthy amount of leverage.
you can make a nice stat arb payoff.
I'm a novice to coding so I haven't made an attempt yet at coding this, so if any of u guys who are fast at this feel free to try it and post a backtest.
Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
This one looks particularly easy to implement in Quantopian, since it's basically just technical analysis.
Looks promising, but probably requires a tick-level backtester/microstructure simulator.
There's a followup or two, and this feels like a repeat but just in case.
Vast collection of academic papers related to quant trading.
Do ETFs Increase Volatility?
We study whether exchange traded funds (ETFs)—an asset of increasing importance—impact the volatility of their underlying stocks. Using identification strategies based on the mechanical variation in ETF ownership, we present evidence that stocks owned by ETFs exhibit significantly higher intraday and daily volatility. We estimate that an increase of one standard deviation in ETF ownership is associated with an increase of 16% in daily stock volatility. The driving channel appears to be arbitrage activity between ETFs and the underlying stocks. Consistent with this view, the effects are stronger for stocks with lower bid-ask spread and lending fees. Finally, the evidence that ETF ownership increases stock turnover suggests that ETF arbitrage adds a new layer of trading to the underlying securities.
Seems ideal for a quantopianification.
Wow excellent, I had not seen this paper. Classic!
I found this pretty interesting, seems relevant.
I don't know how this page hasn't made up here yet, unless I missed it.
Being in/out of the market on certain weeks according to the FOMC meeting calendar. Looks promising, and simple for someone to implement!
Man is it ever hard to find this thread every time, searching doesn't work well. Anyway, not a strategy per se, but a great paper on the VIX ETPs:
EDIT: I was wrong, there is a trading strategy in the second half!
Is there anything in this thread that would be particularly interesting to code in Quantopian and backtest?
I just came across this, Critical Line Algorithm for Portfolio Optimization, it includes a Python implementation. I would check out quantpapers, there's hundreds of papers on there.
Grant, I think that's really a personal question, what sort of trading strategy does someone want to deploy, and how does it fit in with their existing trading strategies? For purely academic interest, I am not sure I would be doing quant trading :)
@Grant, Simon. Anything dealing with Vix, Vix term structure, Vix etn’s would be of much interest.
Well, let me put the question another way. Have any of the ideas listed in this thread been launched as paper/live trading algos at IB? If so, what has been the result? --Grant.
Darell: volatilitymadesimple/ follows a dozen or so VIX ETP strategies, and their own one of course.
Grant: sorry, I haven't done any work in Quantopian for about a year. Can't speak for others.
Hello all, can anyone point me in the direction of an end of day / swing system for the S&P or Dow or Nasdaq? Something with a good win loss ratio would be ideal.
I would appreciate it.
Anyone know if you can import Futures data?
From volatility Made Simple.
"comparing first and second month VIX futures. Traders often use this simple approach to determine whether the VIX futures term-structure is in contango (favoring XIV) or backwardation (favoring VXX)"
Mainly, if we can import front and back month VIX futures to initiate positions on XIV and VXX respectively?
@Sam, I don't know about getting the data from volatility made simple, but you can use Quandl to import the data, or get it directly from CBOE.
Update: You can also get the daily composition of front/back month holdings of the ipath ETNs on their website, that might help you refine your strategy a bit more too. I believe they have the historical holdings as well. This link is for VXX, the others are available as well though. ipathetn/US/16/en/details. app? instrumentId=259118.
Their concept of "Dual Momemtum" is very intriguing. As well, extending it in the manner which is described here:
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
Campbell Harvey's website is also a useful site for financial glossaries and papers on risk. people. duke. edu/
It's not clear if this is a mean-reversion strategy on this cointegrated basket, or whether it's a static investment portfolio somehow optimized for low variance.
Simon - have you looked through the "premium" offerings on Quantpedia at all? Am curious whether they are worth the fee or not.
I haven't, no, I was just planning on going through their free stuff to see what anomalies and papers look interesting and suitable.
I really love Tony Cooper's papers, so clear and readable.
Identifying small mean reverting portfolios:
A Simple Way to Estimate Bid-Ask Spreads from Daily High and Low Prices.
Great piece with lots of implementable ideas on co-integration, High frequency implementation etc.
Some ideas about improving pairs trading.
Obrigado pelo artigo! It's a good read.
Both by Jonathan Kinlay.
Not sure where else to put this. I won't classify this as strategy but it's good to know the bid/ask spread % of companies. Useful for HF algo development.
Jonathan Kinlay writes some of the best stuff out there, thanks!
Totally agree with you Simon on Jonathan Kinlay.
Videos and PDFs are available.
Amazing overview of the mathematics available to design quantitative strategies.
Matthieu, that looks like a great resource indeed. The link seems to have changed, here is an updated one:
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
Folks, whilst all these seem to be great resources, they need a certain amount of knowledge in Statistics. What is the base amount of statistical knowledge from where one can kick on? Any books or resources for the uninitiated?
Preliminiaries are (I think) basic single and multivariable analysis (maybe some real analysis and intermediate combinatorics), linear algebra then get into basic probability theory and after that statistical inference, stochastic processes (and simulation) and econometrics and after that look at financial mathematics and optimization theory and stochastic partial differential equations. Just Google or go to Amazon etc to find books (with solutions).
I pretty much agree with the order of Patrick.
You can grab the basics on probabilities and statistics on statlect.
Then you can follow the good introduction machine learning class from Andrew Ng on Coursera.
If you want to move to more advanced understanding of learning algorithms you may want to have a look at The Elements of Statistical Learning.
After that (and maybe some stochastic calculus and time series analysis) you should be able to understand most of the articles you are interest in or at least know what to Google to fill the gap.
Market neutral portfolio construction with excel implementation.
Expected skewness and momentum portfolios. Some bonkers-good results in there.
Portfolio Optimization for strategies using sort information on expected returns.
Edit: Subbed to this twitter feed a long time ago and rediscovered it today. They post quant papers from SSRN.
Veja isso! Most of the papers have been mentioned by you guys above.
A couple of good tutorial style resources I found recently:
* "AHL explains", a couple of videos going over key concepts like momentum trading: man/DE/ahl-explains Would be cool to implement them in Quantopian (although we don't have futures yet).
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
There are lot of papers and detailed litratures in this link.
I ust came across this 130 30 stretagy thought it would be good place to.
Interesting article about ETF liquidity and the liquidity of underlying securities:
Haven't read the paper yet, but seems to have promising applications to trading.
Not sure if this article is really a strategy, but I found it interesting.
This is a paper by Michael Gayed, CFA and Charlie Bilello, CMT that visits the idea of beta rotation.
The paper was a 2014 Dow Award Winner.
A bunch of articles/papers written by Cliff Asness of AQR. Pretty interesting.
There's already some posts about end of the month stock behavior above, but here's a detailed paper about it:
From the intro: "we find that since July 1926, one could have held the US value-weighted stock index (CRSP) for only seven.
days a month and pocketed the entire market excess return with nearly fifty percent lower volatility.
compared to a buy and hold strategy."
The equity curve graph on page 22 of the paper is eye opening.
A form "risk parity" using Differential Evolution to optimize portfolio contributions to risk.
Another D'Aspremont paper.
Another pair trading algorithm using 2-stage correlation and co-integration based approach on 15 minute OHLC intra-day data on oil sector stocks. They claim monthly 2.67 Sharpe ratio and an annual 9.25 Sharpe ratio for the period between 2012-13. Will be interesting to see if this can be replicated in Quantopian.
Claims that acceleration (difference of returns) has more explanatory power than simple momentum.
Not a 'trading strategy' per se, but an interesting site with some python related code, and some clear thinking.
Olá a todos. I'm at academic finance conferences quite often these days as part of our academic outreach. I see a lot of interesting papers and would be happy to make some best-of lists the next time I'm at a conference. Would people be interested in lists like this for potential ideas?
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
That sounds great Delaney, I'd definitely be interested.
Yup, I read about 5-10 papers a week, always need more!
Great, I'm at FMA in Orlando next week. I'll start up a forum thread and post live once I'm there.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
I hope I'm not being presumptuous but I think everyone following this thread is interested.
Another statistical arbitrage paper but using step-wise regression and variance ratio tests to identify co-integrated baskets. Paper claims a sharpe of 7+ with 50 basis points transaction costs. Quite old paper though.
I've read it (Mean reversion after price drops) multiple times because I'm testing some Josef Rudy's research for my thesis to see if his findings hold water.
Not implementable in Quantopian yet, but perhaps soon. ? :)
Looks interesting! Thank you, Simon.
Delaney that would be fantastic. I've been working on converting the ideas from this paper into Python code.
I'll be adding papers over the next few days.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
Simple idea - buy negative EV stocks and hold for a year! In the microcap segment, it allegedly has mean returns of 60% per trade over the holding period (one year). Probably a wicked drawdown though.
Stocks on Thursdays and Bonds on Fridays.
A simple learning system. Good for learning about market behavior and over-fitting.
The idea is to simulate the composition logic of any ETF/Index stock picks and invest in stcock to be added/deleted from it. Keeping it from the announcement date till 14 days later (when the actual action is done) will result positive retunrs for going long on added stocks and short on deleted ones. The idea is that once stock is announced to be added/deleted to an index , then the index must buy/sell it around 14 days after and the market reacts. buying it before, and sell it at the end of the 14 days announcement.
There are plenty of ETF's so lots of arbitrage is available.
This is the S&P composition logic as example.
if someone did something or wants to work on it together.
This paper has a collection of strategies that may be helpful. Looking through the list and although some are simple there are several that look interesting.
From the Abstract:
We present explicit formulas - that are also computer code - for 101 real-life quantitative trading alphas. Their average holding period approximately ranges 0.6-6.4 days. The average pair-wise correlation of these alphas is low, 15.9%. The returns are strongly correlated with volatility, but have no significant dependence on turnover, directly confirming an earlier result by two of us based on a more indirect empirical analysis. We further find empirically that turnover has poor explanatory power for alpha correlations.
great thread, thanks for this!
Could you please tell me what does 'alpha' significar?
For example, there is simple mean-reversion alpha −ln(today􏰑s open / yesterday􏰑s close)
How to trade it??
Or it is just useful signal (=feature) for learning algorithm?
Alpha is a commonly used metric of how much new information is contained in another signal. It is found by performing a linear regression between the return stream generated by the new signal, and existing factors such as the market. The equation might look like this.
R_new = alpha + beta * R_market + beta * R_oil + .
By seeing how much of your returns are historically explained by each of the other factors, you can make an estimate for how much of your returns are coming from new information, which is what is left over in the alpha. For more info on this see lectures 4, 13, and 14 in the Quantopian Lecture Series.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
Not a new one but have been digging in to short-related data lately and found this interesting.
Not really a paper but this is an excellent quandl post on the general process to test trading ideas:
important -!Read the comments.
Forecasting forex using entropy encoding.
Entropy theory of mind. Numerically derives the link between Entropy in physics and finance. Also builds a quantitative model framework that blends entropy, value of judgement/bias, trading decisions and volume. The only paper I've read that models market volume in a somewhat intuitive way.
The link to the PDF is in the first paragraph. Written by Jonathan Kinlay, he lays out the framework for the ARFIMA-GARCH method of volatility estimation and comes to the conclusion that traditional Option Pricing by Black-Scholes is inefficient and proves it by testing a simple options strategy based on the results of his volatility forecasts.
A few studies of mine these models actually traded real money for a long time like 20 years, not hypothetically.
Here is the link to Li-Xin Wang latest paper Modeling Stock Price Dynamics with Fuzzy Opinion Networks. pdf.
Built to illustrate the idea of trading standard deviation, here is the link to a simple Crude Oil strategy with a z of 1.5.
Built to illustrate the ideas of trading relationships, fundamentals, yesterday and seasonals, here is the link to a second simple Crude Oil strategy. This one has a z of 2.2.
Built to illustrate the ideas of trading a seasonal, trading volatility, and trading yesterday, here is the link to a third simple Crude Oil strategy. This one has a z of 2.3.
Built to illustrate the ideas of trading the tails of a candlestick and trading volatility, here is the link to a fourth simple Crude Oil strategy. This one has a z of 3.0.
Built to illustrate the ideas of portfolios of systems and reusing systems, here is the link to the portfolio of the four previously described Crude Oil strategies. The portfolio has an annual return of 13.6%, a max drawdown of 9.2% and a Sharpe of 1.4 from years 2006 thru February 2016.
Built to show the idea of trading the tail of a candlestick instead of the body when volatility leaves a big tail after the natural gas supply report on Wednesday, here is the link to the first simple Natural Gas strategy. This strategy has a z of 2.8.
Built to illustrate the ideas of trading other traders and trading a fundamental, this Natural Gas system trades the positioning prior to the Wednesday supply report. Aqui está o link. This strategy has a z of 1.8.
does this site have a vocabulary section. like what is a z score.
A z-score is a statistics term, it measures how many standard deviations a value is from the mean of a set of values. Z = 0 means same as the mean, Z = 1 means the value is 1 standard deviation above the mean, etc.
I promise I'm not trying to be snarky, but you can learn that yourself in about 3 seconds by searching "z score" in Google. That will probably be true for most of the finance and statistics terms you see here. Some of them will be complex (like how a GARCH process works) but most will not.
It's normally just the (innovation - mean) / standard deviation, but I think Henry has made up his own definition, I am not sure what he is referring to.
z score is the statistical significance of the test/system. Greater than 1.6 means roughly 95% chance results aren't random.
Thank you both. i conclude that the z score is a way of quantifying the quality of a back test so you can know if you do the same thing by flipping a coin (or not). Sorry i have to reduce everything to some oversimplified format.
I used to trade a a local on the NYFE and now live in Colombia S America. Medellin to be exact. I own a coffee farm called Finca Milena and will put you up if you come down here and get me caught up on quants, algos, thoery etc etc. By the way Mat I did google z score and it came up as a theory for quantifying a company´s future chances of filing for bankruptcy and no offense taken. I wonder what the z score is for that algo.
Fair point - a guy named Edward Altman didn't really do anyone any favors when he also named his bankruptcy prediction model the "z score".
@William, Here is a simple example of zscore of an asset, others will comment if its wrong in any way.
i assume from this that you would want to see a z score of 1 or better to conclude that the system is better than just any random approach. i. e. coin toss.
You should change.
zscore = (series - mean) / std.
Backtest of Darrell's z-score algo w/ z = (series-mean)/std dev.
This third Natural Gas system illustrates the ideas of trading relationships, trading change and trading rate of change between Natural Gas and Crude Oil. This strategy has a z of 2.2. Rules and results are Here.
It's not clear how dependent this strategy is on the recent regime.
We've actually already done a bunch of work implementing the paper you posted, Pravin. Figured linking to it might be useful to some folks.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
We've actually already done a bunch of work implementing the paper you posted, Pravin. Figured linking to it might be useful to some folks.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
Can't be traded with Quantopian, but looks legit.
Looking forward to the actual talk, to find out what the method is! :) (Marcos Lopez de Prado of Guggenheim Partners at Global Derivatives 2016)
Thanks for sharing, Simon.
Dr. Lopez del Prado's website is here.
Knowing de Prado's stuff, which is very good, he'll be making the point that mean variance analysis doesn't work in practice any more. It's easy to overfit it to some historical period by naively optimizing, but will have little correlation to out of sample performance. This is similar to Thomas Wiecki's recent paper on how sharpe ratio also has no correlation between in and out of sample performance.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
I am curious what his suggestion/replacement is. Bootstrapping works great for avoiding overfitting, but you end up with pretty average portfolios.
I suspect just not using mean-variance and using other more sophisticated portfolio selection techniques. Correlation reduction filters, sector neutrality filters, etc.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
The book, Systematic Trading , by Robert Carver, was recommended to me by Simon, and I just finished an entire chapter dedicated to over-fitting. There is a quantitative discussion of relevant backtest time scales to distinguish one approach from another. And approaches to avoiding fitting to a single historical period. Etc. Flipping ahead in the book, bootstrapping is covered, as well. The author seems to be very sober and realistic and is not promoting particular strategies, per se (althoug he does distinguish styles of trading). The focus is on the process and the pitfalls. It is very approachable from a technical standpoint. No fancy math/statistics. It might be a good starting point for many Quantopian users who are aspiring quants.
Agree with you on that book Grant. Must say there are parts that I have difficulty getting my head around. A practitioner's book. His blog is excellent as well.
@Vladimir, I was trying to understand how the z-score can be applied to the simple XLP+TLT portfolio algo you posted elsewhere. Would you be able to add the z-score code to it and repost here?
Also, if we are looking for a z-score of >1.6, what are we looking for? That the z-score curve stays above 1.6 most of the time? Or something different? Thanks in advance..
@rb rb, z-score is really just a measurement of how "rare" an event is in terms of it's distribution. So if you have a z-score >1.6 it would mean that it has a roughly 5% chance of occurring, so a relatively "rare" event indeed (for those who are not old enough to have used this in math class #throwback, the z-table is a great way to illustrate a z-score for normal distribution. In this case, this is a positive table so one would do 0.50 - p(z = 1.6) = 0.50 - 0.4452 = 0.0548 access-excel. tips/wp-content/uploads/2015/09/z-score-02.png).
Applied to any trading strategy, z-scores are a common way to assign a statistical probability value of something occurring, which can act as a "confidence" interval. Using Henry Casten's quick z-score example from above, the attached is an algorithm that shorts SPY when the z-score > 1.6 and long when z-score < -1.6, and closes out positions when -1.4<zscore<1.4, based on the assumption that it is "rare" event and SPY will revert to it's mean price over time.
Z scores can only be interpreted as a measure of event rarity when the underlying distribution of data is known. In most cases distributions in finance are not normally behaved, so assuming normality will not be a good estimator of the rarity of an event. It is better often to think of a z score as a measure of extremity, and only convert to actual rarity when you know more about the data generating process.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
Yes thats right. Pardon my oversimplification :)
No problem, it's a super common and easy to miss mistake that shows up a lot in professional finance practice. Can lead to nasty surprises when you get hit with way more extreme events than you expect.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
One of my favourite papers that had a huge impact on my FX trading, unfortunately quantopian doesn't have FX (or FX futures) yet as this doesn't apply to equities.
Stop-Loss Orders and Price Cascades in Currency Markets.
I also found this paper quite interesting.
I'm also not sure if this has been posted here.
Grant, nice paper - no surprise that downside returns are followed by positive returns - buy and hold an its simplest and best (if not buy and hold, then long bias "algos" are affected by the general market to such extent that they end up resembling buy and hold, less transaction costs)! The more subtle issue is that upside returns contain no information about future returns, which means that they 1. are not skilled at taking profit, or 2. taking profits results in subsequent poor decision making. both of which make sense.
Here's a strategy idea/exploration called Ebb and Flow. It trades ES and Bonds when both are at extremes and is Interesting because it goes long stocks and bonds.
The idea, rules and results are here (henrycarstens): wp. me/p6O8fA-aT.
101 Trading Ideas.
I am thinking about implementing a macro trading strategy that will produce trading signals based on changes in measures such as: risk premium, interest rates, margin requirements and haircuts of pledged collateral.
At the moment for the universe of stocks to trade that I have in mind is (can be expanded): shadow banking ETFs, safe asset bond ETFs, clearing houses, financial institutions in the repo business, derivatives trading hedge funds and other heavily OTC involved companies.
I am not sure where to find data on haircuts and margin requirements, but I've seen an announcement from IB that they will be offering OTC data:
The idea comes from my master thesis which is titled: "The Decline of Safe Assets and Shortage of Collateral". I've been heavily engaged with this topic for years now and I think that it explains the modern macro world pretty well, so a trading strategy based on it should be profitable.
I am looking for comments, suggestions or questions from other Quantopian traders. This is still just an idea, there are some questions still to be answered like: whats going to be the universe of stocks, where will I find data, how will signals be interpreted etc. but I think that there's a lot of potential and I haven't seen many macro strategies on Quantopian.
Here's a strategy idea called Silver and Gold and trades Gold based on momentum, pullbacks and Silver. It might be really interesting to adapt to silver and gold equities.
The idea, rules and results are here (henrycarstens): [wp. me/p6O8fA-b5][1]
101 Trading Ideas.
Here's a strategy idea called Corn Predator-Prey that trades corn based on the agriculture ecosystem viewed as a predator-prey model. Wheat and soybeans are the prey and the dollar is the predator.
The idea, rules and results are here (henrycarstens): [wp. me/p6O8fA-b8][1]
101 Trading Ideas.
Here's a strategy idea called Effectiveness that trades the dollar based on its relative ease of movement vs bonds.
The idea, fully disclosed rules and results are here (henrycarstens): wp. me/p6O8fA-bh.
101 Trading Ideas.
Here's another dollar strategy that tries to find the beginning of a trend in the dollar.
The idea, fully disclosed rules and results are here (henrycarstens): wp. me/p6O8fA-bn.
101 Trading Ideas.
Here's a strategy idea called Econ101 based on the Krebs Cycle idea from 101 Trading Ideas. Econ101 uses the employment report and the dollar to trade bonds. Strategy idea with rules.
101 Trading Ideas.
Here's a strategy idea based on camoflage: How does the market camouflage it's moves? When crude oil and natural gas move in opposite directions is it a signal or camouflage?
Idea, rules and notes are here (henrycarstens): wp. me/p6O8fA-bv.
101 Trading Ideas.
Here's a strategy idea based on trading tomorrow: How does gold react when bonds go the opposite direction?
Idea, rules and notes are here (henrycarstens): wp. me/p6O8fA-c5.
101 Trading Ideas.
Here's a strategy idea for gold based on fear: How does gold react to fear?
Idea, rules, and notes are here (henrycarstens): wp. me/p6O8fA-cn.
101 Trading Ideas.
Everything you need to know about trading ideas:
How to measure when you need new trading ideas,
Ways to create trading ideas,
Ways to measure the effectiveness of trading idea creation,
Ways to measure the effectiveness of trading ideas.
101 Trading Ideas.
arxiv/pdf/1212.2129v2.pdf Mostly posting this so I don't forget about it lol.
I had posted this in the public forum, but here might be more beneficial.
I just been introduced to Robinhood and caught wind of the Quantopian intergration.
I do not know Python at all, but I am an options trader that uses the MACD using the values of 9, 20, 6 for my entries and an 11 MA as my exit position.
I would like to take this strategy and turn it into an algorithm and have it running in Robinhood.
The strategy would work like this:
A entry uses 20% of available buying power (if a robinhood instant account, PDT counter should be no greater than 1 for safety purposes)
A buy order is triggered when MACD has a crossover and stock price is above 11MA.
And when stock price falls below 11 MA, liquidates position.
If MACD signals buy, but stock price is below 11MA it's ignored.
I have attached a photo, for a visual description - imgur/a/nI6X6.
So stocks that are high liquidity, high momentum like FB, AAPL, NFLX, GOOG/GOOGL, BABA, PCLN, AMZN, TSLA, etc, waits to meet criteria, rinse and repeat.
The reason for the 9, 20, 6 is this triggers on the first candle, and the 11 MA minimizes the potential loss incurred.
Qualquer ajuda seria muito apreciada. Obrigado.
This thread has gotten a bit off-topic; can we please keep it to simple links to actual papers detailing a trading strategy, rather than links to personal/promotional websites, requests for help, or other clutter.
EDIT: not to be rude, but there is an entire forum wherein one can post such things. I created this thread to be a focused place to find academic & practitioner research.
Sorry, I thought this would fall under a strategy idea.
If managers use non-public information or misvaluation to time a.
firm’s corporate actions, it is likely that equity issues will precede.
bad earnings while buyback announcements will precede good earnings.
Consistent with this expectation, we find evidence of earnings.
predictability: the market reaction to earnings following buyback.
announcements is higher by 4.56% than the reaction to earnings.
following equity issuance over a 25-trading day window (-10, 15). O.
difference in market reactions to earnings is smaller at 1.85% when a.
5-day window (0, 5) is considered. Short-term stock returns reported.
in this paper are more meaningful and sidestep the sensitivity of.
long-term returns to benchmarking concerns documented in the.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
An implementation of an idea triggered by the Clustering Illusion from List of Biases using crude oil etf's.
101 Trading Ideas.
An intraday trading model based on Artificial Immune Systems.
it looks promising.
Do you have a PDF source for this paper? I can't find it via my usual sources? It looks interesting and I may implement it, but like to keep original sources around for reference.
@ Steven Shack sorry i don't have.
i was wondering how to implement the futures based ideas. is it possible in quantopian? i know theyve been talking about futures for a while. are there other resources similar to quantopian that have some sort of backtesting like quantopian, that allows for algo-trading futures? or options?
OPTIMAL EXECUTION HORIZON.
by Easley, López de Prado, and O'Hara.
This approach may be a strong complement to any short-term trading strategy.
The authors do a good job of laying out their intent:
"Execution traders know that market impact greatly depends on whether.
their orders lean with or against the market. We introduce the OEH.
model, which incorporates this fact when determining the optimal.
trading horizon for an order, an input required by many sophisticated.
and apparent result:
"Our empirical study shows that OEH allows traders to achieve greater.
profits on their information, as compared to VWAP. If the trader’s.
information is right, OEH will allow her to capture greater profits on.
that trade. If her information is inaccurate, OEH will deliver smaller.
losses than VWAP. OEH is not an investment strategy on its own, but.
delivers substantial “execution alpha” by boosting the performance of.
Authors: Eric C. So of MIT and Sean Wang of UNC.
This study documents a six-fold increase in short-term return.
reversals during earnings announcements relative to non-announcement.
períodos. Following prior research, we use reversals as a proxy for.
expected returns market makers demand for providing liquidity. Nosso.
findings highlight significant time-series variation in the magnitude.
of short-term return reversals and suggest that market makers demand.
higher expected returns prior to earnings announcements because of.
increased inventory risks that stem from holding net positions through.
the release of anticipated earnings news. Collectively, our findings.
suggest that uncertainty regarding anticipated information events.
elicits predictable increases in expected returns to liquidity.
provision and that these increases significantly affect the dynamics.
and information content of market prices.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
Just wanted to let you know that we've been putting together a curated list of trading strategy and research ideas from the community. At the moment, it's research that folks from Quantopian have published, but we're hoping to feature some from you. Send suggestions to [email protected]
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian. Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
NONLINEAR MARKET DYNAMICS BETWEEN STOCK RETURNS AND TRADING VOLUME: EMPIRICAL EVIDENCES FROM ASIAN STOCK MARKETS.
Can Google predict the stock market?
Deviations from Put-Call Parity and Stock Return Predictability.
"Using the difference in implied volatility between pairs of call and put options to measure these deviations we find that stocks with relatively expensive calls outperform stocks with relatively expensive puts by 51 basis points per week"
Upon first-glance, appears particularly germane to the Q program of long-short algos:
"Extending Rules-Based Factor Portfolios to a Long-Short Framework"
Note the section "The Costs and the Risks of Shorting" which is not captured yet (as I understand) in the Q backtester.
Has anyone tried a long/short using estimize's new weekly top10 long/shorts?
Not necessarily a strategy but a paper on decomposition of risk into various factors that can be used for hedging. Anyone volunteers to port this octave code to Python?
@Aqua interesting paper on decomposition of risk. The code is copyrighted; it has a disclaimer but does not state the protections. Can it really be ported to Python AND shared? Not a lawyer here .
Does anyone know any new (or alternative) trading strategy for forex currency market ?
The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides)
45 Pages Posted: 6 Sep 2017.
Marcos Lopez de Prado.
Guggenheim Partners, LLC; Lawrence Berkeley National Laboratory; Harvard University - RCC.
Date Written: September 2, 2017.
The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are 7 critical mistakes underlying most of those failures.
Nice/devastating article Grant. Has anyone here used familiar fractional differentiation when looking at price changes?
Ahead of Print: 2 October 2017.
Estimating Time-Varying Factor Exposures by Andrew Ang, Ananth Madhavan, and Aleksander Sobczyk.
Does anyone try to backtest candle engulfing pattern on forex (or crude oil future) ? I tested using engulfing pattern by pulling historical data from IB but the result is not that good. I am just wondering how to make a better guess on engulfing pattern.
Not a trading paper, but would seem to be relevant in pairs searching and perhaps factor analyses:
Maximal information coefficient (MIC) is an indicator to explore the correlation between pairwise variables in large data sets, and the accuracy of MIC has an impact on the measure of dependence for each pair. To improve the equitability in an acceptable run-time, in this paper, an intelligent MIC (iMIC) is proposed for optimizing the partition on the y-axis to approximate the MIC with good accuracy. It is an iterative algorithm on quadratic optimization to generate a better characteristic matrix. During the process, the iMIC can quickly find out the local optimal value while using a lower number of iterations. It produces results that are close to the true MIC values by searching just.
times, rather than computations required for the previous method. In the compared experiments of 169 indexes about 202 countries from World Health Organization (WHO) data set, the proposed algorithm offers a better solution coupled with a reasonable run-time for MIC, and good performance search for the extreme values in fewer iterations. The iMIC develops the equitability keeping the satisfied accuracy with fast computational speed, potentially benefitting the relationship exploration in big data.
Any good strategy database for crypto trading? or any link where I can study a bit more about it. Muito obrigado.
Desculpe, algo deu errado. Tente novamente ou entre em contato enviando feedback.
Você enviou com sucesso um ticket de suporte.
Nossa equipe de suporte entrará em contato em breve.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian.
Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.
O material deste site é fornecido apenas para fins informativos e não constitui uma oferta de venda, uma solicitação de compra ou uma recomendação ou endosso para qualquer segurança ou estratégia, nem constitui uma oferta para fornecer serviços de consultoria de investimento pela Quantopian.
Além disso, o material não oferece opinião com relação à adequação de qualquer investimento específico ou de segurança. Nenhuma informação aqui contida deve ser considerada como uma sugestão para se envolver ou se abster de qualquer ação relacionada ao investimento, já que nenhuma das empresas da Quantopian ou de suas afiliadas está prestando consultoria de investimento, atuando como consultora de qualquer plano ou entidade sujeita a o Employee Retirement Income Security Act de 1974, conforme alterado, conta de aposentadoria individual ou anuidade de aposentadoria individual, ou dar conselhos em uma capacidade fiduciária com relação aos materiais aqui apresentados. Se você for um investidor individual ou outro investidor, entre em contato com seu consultor financeiro ou outro fiduciário não relacionado com a Quantopian sobre se qualquer ideia, estratégia, produto ou serviço de investimento descrito aqui pode ser apropriado para suas circunstâncias. Todos os investimentos envolvem risco, incluindo perda de principal. A Quantopian não garante a exatidão ou integridade das opiniões expressas no site. As opiniões estão sujeitas a alterações e podem ter se tornado não confiáveis ​​por várias razões, incluindo mudanças nas condições de mercado ou circunstâncias econômicas.

Two Centuries of Momentum.
A momentum-based investing approach can be confusing to investors who are often told that “chasing performance” is a massive mistake and “timing the market” is impossible.
Yet as a systematized strategy, momentum sits upon nearly a quarter century of positive academic evidence and a century of successful empirical results.
Our firm, Newfound Research, was founded in August 2008 to offer research derived from our volatility-adjusted momentum models. Today, we provide tactically risk-managed investment portfolios using those same models.
Momentum, and particularly time-series momentum, has been in our DNA since day one.
In this Foundational Series piece, we want to explore momentum’s rich history and the academic evidence demonstrating its robustness across asset classes, geographies, and market cycles.
Índice.
1. What is Momentum?
Momentum is a system of investing that buys and sells based upon recent returns. Momentum investors buy outperforming securities and avoid – or sell short – underperforming ones.
The notion is closely tied to physics. In physics, momentum is the product of the mass and velocity of an object. For example, a heavy truck moving at a high speed has large momentum. To stop the truck, we must apply either a large or a prolonged force against it.
Momentum investors apply a similar notion. They assume outperforming securities will continue to outperform in absence of significant headwinds.
2. The Two Faces and Many Names of Momentum.
2.1 Relative Momentum.
The phenomenon of relative momentum is also called cross-sectional momentum and relative strength.
Relative momentum investors compare securities against each other’s performance. They favor buying outperforming securities and avoiding – or short-selling – underperforming securities.
Long-only relative momentum investors rotate between a subset of holdings within their investable universe. For example, a simple long-only relative strength system example is “best N of.” At rebalance, this system sells its current holdings and buys the top N performing securities of a basket. In doing so, the strategy seeks to align the portfolio with the best performing securities in hopes they continue to outperform.
2.2 Absolute Momentum.
Absolute momentum is also referred to as time-series momentum or trend following.
Absolute momentum investors compare a security against its own historical performance. The system buys positive returning securities and avoids, or sells short, negative returning securities.
The primary difference is that relative momentum makes no distinction about return direction. If all securities are losing value, relative momentum will seek to invest in those assets that are going down least. Absolute momentum will seek to avoid negative returning assets.
3. A Brief History of Momentum.
3.1 Early Practitioners.
Momentum is one of Wall Street’s oldest investment strategies.
In 1838, James Grant published The Great Metroplis, Volume 2 . Within, he spoke of David Ricardo, an English political economist who was active in the London markets in the late 1700s and early 1800s. Ricardo amassed a large fortune trading both bonds and stocks.
According to Grant, Ricardo’s success was attributed to three golden rules:
As I have mentioned the name of Mr. Ricardo, I may observe that he amassed his immense fortune by a scrupulous attention to what he called his own three golden rules, the observance of which he used to press on his private friends. These were, “Never refuse an option* when you can get it,”—”Cut short your losses,”—”Let your profits run on.” By cutting short one’s losses, Mr. Ricardo meant that when a member had made a purchase of stock, and prices were falling, he ought to resell immediately. And by letting one’s profits run on he meant, that when a member possessed stock, and prices were raising, he ought not to sell until prices had reached their highest, and were beginning again to fall. These are, indeed, golden rules, and may be applied with advantage to innumerable other transactions than those connected with the Stock Exchange.
The rules “cut short your losses” and “let your profits run on” are foundational philosophies of momentum.
Following in Ricardo’s footsteps are some of Wall Street’s greatest legends who implemented momentum and trend-following techniques.
Charles H. Dow (1851 – 1902) was the founder and first editor of the Wall Street Journal as well as the co-founder of Dow Jones and Company. In his Wall Street Journal column, he published his market trend analysis, which eventually developed into a body of research called Dow theory. Dow theory primarily focuses on the identification of trends as being the key signal for investing.
Jesse Livermore (1877 – 1940) was a stock market speculator in the early 1900s who famously made – and subsequently lost – two massive fortunes during the market panic of 1907 and crash of 1929. He is attributed (by Edwin Lefèvre, in Reminiscences of a Stock Operator) to saying,
[T]he big money was not in the individual fluctuations but in the main movements … sizing up the entire market and its trend.
Livermore claimed that his lack of adherence to his own rules was the main reason he lost his wealth.
In the same era of Livermore, Richard Wyckoff (1873 – 1934) noted that stocks tended to trend together. Thus he focused on entering long positions only when the broad market was trending up. When the market was in decline, he focused on shorting. He also emphasized the placement of stop-losses to help control risk.
He was personally so successful with his techniques, he eventually owned nine and a half acres in the Hamptons.
Starting in the 1930s, George Chestnutt successfully ran the American Investors Fund for nearly 30 years using relative strength techniques. He also published market letters with stock and industry group rankings based on his methods. He wrote,
[I]t is better to buy the leaders and leave the laggards alone. In the market, as in many other phases of life, ‘the strong get stronger, and the weak get weaker.’
In the late 1940s and early 1950s, Richard Donchian developed a rules based technical system that became the foundation for his firm Futures, Inc. Futures, Inc. was one of the first publicly held commodity funds. The investment philosophy was based upon Donchian’s belief that commodity prices moved in long, sweeping bull and bear markets. Using moving averages, Donchian built one of the first systematic trend-following methods, earning him the title of the father of trend-following.
In the late 1950s, Nicholas Darvas (1920 – 1977), trained economist and touring dancer, invented “BOX theory.” He modeled stock prices as a series of boxes. If a stock price remained in a box, he waited. As a stock price broke out of a box to new highs, he bought and placed a tight stop loss. He is quoted as saying,
I keep out in a bear market and leave such exceptional stocks to those who don’t mind risking their money against the market trend.
Also during the 1950s and 1960s was Jack Dreyfus, who Barron’s named the second most significant money manager of the last century. From 1953 to 1964, his Dreyfus Fund returned 604% compared to 346% for the Dow index. Studies performed by William O’Neil showed that Dreyfus tended to buy stocks making new 52-week highs. It wouldn’t be until 2004 that academic studies would confirm this method of investing.
Richard Driehaus took the momentum torch during the 1980s. In his interview in Jack Schwager’s The New Market Wizards, he said he believed that money was made buying high and selling higher.
That means buying stocks that have already had good moves and have high relative strength – that is, stocks in demand by other investors. I would much rather invest in a stock that’s increasing in price and take the risk that it may begin to decline than invest in a stock that’s already in a decline and try to guess when it will turn around.
3.2 Earliest Academic Studies.
In 1933, Alfred Cowles III and Herbert Jones released a research paper titled Some A Posteriori Probabilities in Stock Market Action . Within it they specifically focused on “inertia” at the “microscopic” – or stock – level.
They focused on counting the ratio of sequences – times when positive returns were followed by positive returns, or negative returns were followed by negative returns – to reversals – times when positive returns were followed by negative returns, and vice versa.
It was found that, for every series with intervals between observations of from 20 minutes up to and including 3 years, the sequences out-numbered the reversals. For example, in the case of the monthly series from 1835 to 1935, a total of 1200 observations, there were 748 sequences and 450 reversals. That is, the probability appeared to be .625 that, if the market had risen in a given month, it would rise in the succeeding month, or, if it had fallen, that it would continue to decline for another month. The standard deviation for such a long series constructed by random penny tossing would be 17.3; therefore the deviation of 149 from the expected value of 599 is in excess of eight times the standard deviation. The probability of obtaining such a result in a penny-tossing series is infinitesimal.
Despite the success of their research on the statistical significance of sequences, the next academic study on momentum was not released for 30 years.
In 1967, Robert Levy published Relative Strength as a Criterion for Investment Selection . Levy found that there was “good correlation between past performance groups and future … performance groups” over 26-week periods. Ele afirma:
[…] the [26-week] average ranks and ratios clearly support the concept of continuation of relative strength. The stocks which historically were among the 10 per cent strongest (lowest ranked) appreciated in price by an average of 9.6 per cent over a 26-week future period. On the other hand, the stocks which historically were among the 10 per cent weakest (highest ranked) appreciated in price an average of only 2.9 per cent over a 26-week future period.
Unfortunately, the scope of the study was limited. The period used in the analysis was only from 1960 to 1965. Thus, of the 26-week periods tested, only 8 were independent. In Levy’s words, “the results were extensively intercorrelated; and the use of standard statistical measures becomes suspect.” Therefore, Levy omitted these statistics.
Despite its promise, momentum research went dark for the next 25 years.
4. Dark Days of Momentum Research.
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Despite the success of practitioners and promising results of early studies, momentum would go largely ignored by academics until the 1990s.
Exactly why is unknown, but we have a theory: fundamental investing, modern portfolio theory, and the efficient market hypothesis.
4.1 The Rise of Fundamental Investing.
In 1934, Benjamin Graham and David Dodd published Security Analysis . Later, in 1949, they published The Intelligent Investor . In these tomes, they outline their methods for successful investing.
For Graham and Dodd, a purchase of stock was a purchase of partial ownership of a business. Therefore, it was important that investors evaluate the financial state of the underlying business they were buying.
They also defined a strong delineation between investing and speculating. To quote,
An investment operation is one which, upon thorough analysis, promises safety of principal and an adequate return. Operations not meeting these requirements are speculative.
Speculative was a pejorative term. Even the title of The Intelligent Investor implied that any investors not performing security analysis were not intelligent.
The intelligent investor began her process by computing a firm’s intrinsic value. In other words, “what is the business truly worth?” This value was either objectively right or wrong based on the investor’s analysis. Whether the market agreed or not was irrelevant.
Once an intrinsic value was determined, Graham and Dodd advocated investors buy with a margin of safety. This meant waiting for the market to offer stock prices at a deep discount to intrinsic value.
These methods of analysis became the foundation of value investing.
To disciples of Graham and Dodd, momentum is speculative nonsense. To quote Warren Buffett in The Superinvestors of Graham-and-Doddsville:
I always find it extraordinary that so many studies are made of price and volume behavior, the stuff of chartists. Can you imagine buying an entire business simply because the price of the business had been marked up substantially last week and the week before?
4.2 Modern Portfolio Theory and the Efficient Market Hypothesis.
In his 1952 article “Portfolio Selection,” Harry Markowitz outlined the foundations of Modern Portfolio Theory (MPT). The biggest breakthrough of MPT was that it provided a mathematical formulation for diversification.
While the concept of diversification has existed since pre-Biblical eras, it had never before been quantified. With MPT, practitioners could now derive portfolios that optimally balanced risk and reward. For example, by combining assets together, Markowitz created the efficient frontier: those combinations for which there is the lowest risk for a given level of expected return.
By introducing a risk-free asset, the expected return of any portfolio constructed can be linearly changed by varying the allocation to the risk-free asset. In a graph like the one on the left, this can be visualized by constructing a line that passes through the risk-free asset and the risky portfolio (called a Capital Allocation Line or CAL). The CAL that is tangent to the efficient frontier is called the capital market line (CML). The point of tangency along the efficient frontier is the portfolio with the highest Sharpe ratio (excess expected return divided by volatility).
According to MPT, in which all investors seek to maximize their Sharpe ratio, an investor should only hold a mixture of this portfolio and the risk free asset. Increasing the allocation to the risk-free asset decreases risk while introducing leverage increases risk.
The fact that any investor should only hold one portfolio has a very important implication: given all the assets available in the market, all investors should hold, in equal relative proportion, the same portfolio of global asset classes. Additionally, if all investors are holding the same mix of assets, in market equilibrium, the prices of asset classes – and therefore their expected returns – must adjust such that the allocation ratios of the assets in the tangency portfolio will match the ratio in which risky assets are supplied to the market.
Holding anything but a combination of the tangency portfolio and the risk-free asset is considered sub-optimal.
From this foundation, concepts for the Capital Asset Pricing Model (CAPM) are derived. CAPM was introduced independently by Jack Treynor, William Sharpe, John Lintner, and Jan Mossin from 1961-1966.
CAPM defines a “single-factor model” for pricing securities. The expected return of a security is defined in relation to a risk-free rate, the security’s “systematic” risk (sensitivity to the tangency portfolio), and the expected market return. All other potentially influencing factors are considered to be superfluous.
While its origins trace back to the 1800s, the efficient market hypothesis (EMH) was officially developed by Eugene Fama in his 1962 Ph. D. thesis.
EMH states that stock prices reflect all known and relevant information and always trade at fair value. If stocks could not trade above or below fair value, investors would never be able to buy them at discounts or sell them at premiums. Therefore, “beating the market” on a risk-adjusted basis is impossible.
Technically, MPT and EMH are independent theories. MPT tells us we want to behave optimally, and gives us a framework to do so. EMH tells us that even optimal behavior will not generate any return in excess of returns predicted by asset pricing models like CAPM.
Markowitz, Fama, and Sharpe all went on to win Nobel prizes for their work.
4.3 Growing Skepticism towards Technical Analysis.
Technical analysis is a category of investing methods that use past market data – primarily price and volume – to make forward forecasts.
As a category, technical analysis is quite broad. Some technicians look for defined patterns in price charts. Others look for lines of support or resistance. A variety of indicators may be calculated and used. Some technicians follow specific techniques – like Dow theory or Elliot Wave theory.
Unfortunately, the broad nature of technical analysis makes it difficult to evaluate academically. Methods vary widely and different technical analysts can make contradictory predictions using the same data.
Thus, during the rise of EMH through the 1960s and 1970s, technical analysis was largely dismissed by academics.
Since momentum relies only on past prices, and many practitioners used tools like moving averages to identify trends, it was categorized as a form of technical analysis. As academics dismissed the field, momentum went overlooked.
4.4 But Value Research Went On.
Despite CAPM, EMH, and growing skepticism towards technical analysis, academic research for fundamental investing continued. Focus was especially strong on value investing.
For example, in 1977, S. Basu authored a comprehensive study on value investing, titled Investment Performance of Common Stocks in Relation to their Price-Earnings Ratios: A Test of the Efficient Market Hypothesis . Within, Basu finds that the return relationship strictly increases for stocks sorted on their price-earnings ratio. Put more simply, cheap stocks outperform expensive ones.
Unfortunately, in many of these studies, the opposite of value was labeled growth or glamor. This became synonymous with high flying, over-priced stocks. Of course, not value is not the same as growth. And not value is certainly not the same as momentum. It is entirely possible that a stock can be in the middle of a positive trend, yet still be undervalued. Nevertheless, it is easy to see how relatively outperforming and over-priced may be conflated.
It is possible that the success of value research in demonstrating the success of buying cheap stocks dampened the enthusiasm for momentum research.
5. The Return of Momentum.
Fortunately, decades of value-based evidence against market efficiency finally piled up.
In February 1993, Eugene Fama and Kenneth French released Common Risk Factors in the Returns on Stocks and Bonds . Fama and French extended the single-factor model of CAPM into a three-factor model. Beyond the “market factor,” factors for “value” and “size” were added, acknowledging these distinct drivers of return.
Momentum was still nowhere to be found.
But a mere month later, Narasimhan Jegadeesh and Sheridan Titman published their seminal work on momentum, titled Returns to Buying Winners and Selling Losers: Implication for Stock Market Efficiency . Within they demonstrated:
Strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3- to 12-month holding periods.
The results of the paper could not be explained by systematic risk or delayed reactions to other common factors, echoing the results of Cowles and Jones some 60 years prior.
In 1996, Fama and French authored Multifactor Explanations of Asset Pricing Anomalies . Armed with their new three-factor model, they explored whether recently discovered market phenomena – including Jegadeesh and Titman’s momentum – could be rationally explained away.
While most anomalies disappeared under scrutiny, the momentum results remained robust. In fact, in the paper Fama and French admitted that,
“[momentum is the] main embarrassment of the three-factor model.”
6. Overwhelming Evidence for Momentum.
With its rediscovery and robustness against prevailing rational pricing models, momentum research exploded over the next two decades. It was applied across asset classes, geographies, and time periods. In chronological order:
Asness, Liew, and Stevens (1997) shows that momentum investing is a profitable strategy for country indices .
Carhart (1997) finds that portfolios of mutual funds , constructed by sorting on trailing one-year returns, decrease in monthly excess return nearly monotonically, inline with momentum expectations.
Rouwenhorst (1998) demonstrates that stocks in international equity markets exhibit medium-term return continuations. The study covered stocks from Austria, Belgium, Denmark, France, Germany, Italy, the Netherlands, Norway, Spain, Sweden, Switzerland, and the United Kingdom.
LeBaron (1999) finds that a simple momentum model creates “unusually large profits in foreign exchange series.”
A graph from Rouwenhorst (1998)
Moskowitz and Grinblatt (1999) finds evidence for a strong and persistent industry momentum effect.
Rouwenhorst (1999) , in a study of 1700 firms across 20 countries, demonstrates that emerging market stocks exhibit momentum.
Liew and Vassalou (2000) shows that momentum returns are significantly positive in foreign developed countries but there is little evidence to explain them by economic developments.
Griffin, Ji, and Martin (2003) demonstrates momentum’s robustness , finding it to be “large and statistically reliable in periods of both negative and positive economic growth.” The study finds no evidence for macroeconomic or risk-based explanations to momentum returns.
Erb and Harvey (2006) shows evidence of success for momentum investing in commodity futures .
Gorton, Hayashi, and Rouwenhorst (2008) extends momentum research on commodities, confirming its existence in futures but also identifying its existence in spot prices .
A chart from Erb and Harvey (2006)
Jostova, Niklova Philopov, and Stahel (2012) shows that momentum profits are significant for non-investment grade corporate bonds .
Luu and Yu (2012) identifies that for liquid fixed-income assets , such as government bonds, momentum strategies may provide a good risk-return trade-off and a hedge for credit exposure.
7. Academic Explanations for Momentum.
While academia has accepted momentum as a distinct driver of return premia in many asset classes around the world, the root cause is still debated.
So far, the theory for rational markets has failed to account for momentum’s significant and robust returns. It is not correlated with macroeconomic variables and does not seem to reflect exposure to other known risk factors.
But there are several hypotheses that might explain how irrational behavior may lead to momentum.
7.1 The Behavioral Thesis.
The most commonly accepted argument for why momentum exists and persists comes from behavioral finance. Behavioral finance is a field that seeks to link psychological theory with economics and finance to explain irrational decisions.
Some of the popular behavioral finance explanations for momentum include:
Herding: Also known as the “bandwagon effect,” herding is the tendency for individuals to mimic the actions of a larger group.
Anchoring Bias: The tendency to rely too heavily on the first piece of information received.
Confirmation Bias: The tendency to ignore information contradictory to prior beliefs.
Disposition Effect: Investors tend to sell winners too early and hold on to losers too long. This occurs because investors like to realize their gains but not their losses, hoping to “make back” what has been lost.
Together, these biases cause investors to either under - or over-react to information, causing pricing inefficiencies and irrational behavior.
Cumulative Advantage: Momentum Beyond the Markets.
There is strong evidence for momentum being a behavioral and social phenomenon beyond stock markets.
Matthew Salganik, Peter Dodds, and Duncan Watts ran a 14,000 participant, web-based study designed to establish independence of taste and preference in music.
Participants were asked to explore, listen to, and rate music. One group of participants would be able to see how many times a song was downloaded and how other participants rated it; the other group would not be able to see downloads or ratings. The group that could see the number of downloads (“social influence”) was then sub-divided into 8 distinct, random groups where members of each sub-group could only see the download and ratings statistics of their sub-group peers.
The hypothesis of the study was that “good music” should garner the same amount of market share regardless of the existence of social influence: hits should be hits. Secondly, the same hits should be hits across all independent social influence groups.
What the study found was dramatically different. Each social-influence group had its own hit songs, and those songs commanded a much larger market share of downloads than songs did in the socially-independent group.
Introducing social-influence did two things: it made hits bigger and it made hits more unpredictable. The authors called this effect “cumulative advantage.” The consequences are profound. To quote an article in the New York Times by Watts,
It’s a simple result to state, but it has a surprisingly deep consequence. Because the long-run success of a song depends so sensitively on the decisions of a few early-arriving individuals, whose choices are subsequently amplified and eventually locked in by the cumulative-advantage process, and because the particular individuals who play this important role are chosen randomly and may make different decisions from one moment to the next, the resulting unpredictability is inherent to the nature of the market. It cannot be eliminated either by accumulating more information — about people or songs — or by developing fancier prediction algorithms, any more than you can repeatedly roll sixes no matter how carefully you try to throw the die.
7.2 The Limits of Arbitrage Thesis.
EMH assumes that any mis-pricing in public markets will be immediately arbitraged away by rational market participants. The limits to arbitrage theory recognizes that there are often restrictions – both regulatory and capital based – that may limit rational traders from fully arbitraging away these price inefficiencies.
In support of this thesis is Chabot, Ghysels, and Jagannathan (2009), which finds that when arbitrage capital is in short supply, momentum cycles last longer.
Similarly, those investors bringing good news to the market may lack the capital to take full advantage of that information. So if there has been good news in the past, there may be good news not yet incorporated into the price.
7.3 The Rational Inattention Thesis.
Humans possess a finite capacity to process the large amounts of information they are confronted with. Time is a scarce resource for decision makers.
The rational inattention theory argues that some information may be evaluated less carefully, or even outright ignored. Or, alternatively, it may be optimal for investors to obtain news with limited frequency or limited accuracy. This can cause investors to over - or under-invest and could cause the persistence of trends.
Chen and Yu (2014) found that portfolios constructed from stocks “more likely to grab attention” based on visual patterns induces investor over-reaction. They provide evidence that momentum continuation is induced by visually-based psychological biases.
8. Advances in Cross-Sectional Momentum Research.
Much like there are many ways to identify value, there are many ways to identify momentum. Recent research has identified methods that may improve upon traditional total return momentum.
52-Week Highs: Hwang and George (2004) shows that nearness to a 52-week high price dominates and improves upon the forecasting power of past returns (i. e. momentum). Perhaps most interestingly, future returns forecast using a 52-week high do not mean-revert in the long run, like traditional momentum.
Liu, Liu, and Ma (2010) tests the 52-week high strategy in 20 international markets and finds that it is profitable in 18 and significant in 10.
Residual Momentum: Using a universe of domestic equities, covering the period of January 1926 to December 2009, Blitz, Huij, and Martens (2009) decomposes stock returns using the Fama-French three-factor model. Returns unexplained by the market, value, and size factors are considered to be residual. The study finds that momentum strategies built from residual returns exhibit risk-adjusted profits that are twice as large as those associated with total return momentum.
Idiosyncratic Momentum: Similar to Blitz, Huij, and Martens, Chaves (2012) uses the CAPM model to correct stocks for market returns and identify idiosyncratic returns. Idiosyncratic momentum is found to work better than momentum in a sample of 21 developed countries. Perhaps most importantly, idiosyncratic momentum is successful in Japan, where most traditional momentum strategies have failed.
9. Using Momentum as a Risk-Mitigation Technique.
(Pssst … sorry to interrupt, but if want to learn more about how we put this theory into practice to create risk-managed portfolios, check out our strategies!)
While most research in the late 1990s and early 2000s focused on relative momentum, research after 2008 has been heavily focused on time-series momentum for its risk-mitigating and diversification properties.
Some of the earliest, most popular research was done by Faber (2006) , in which a simple price-minus-moving-average approach was used to drive a portfolio of U. S. equities, foreign developed equities, commodities, U. S. REITs, and U. S. government bonds. The resulting portfolio demonstrates “equity-like returns with bond-like volatility.”
Hurst, Ooi, and Pedersen (2010) identifies that trend-following, or time-series momentum, is a significant component of returns for managed futures strategies. In doing so, the research demonstrates the consistency of trend-following approaches in generating returns in both bull and bear markets .
Going beyond managed futures specifically, Moskowitz, Ooi, Hua, and Pedersen (2011) documents significant time-series momentum in equity index, currency, commodity, and bond futures covering 58 liquid instruments over a 25-year period.
Perhaps some of the most conclusive evidence comes from Hurst, Ooi, Pedersen (2012), which explores time-series momentum going back to 1903 and through 2011 .
The study constructs a portfolio of an equal-weight combination of 1-month, 3-month, and 12-month time-series momentum strategies for 59 markets across 4 major asset classes, including commodities, equity indices, and currency pairs. The approach is consistently profitable across decades . The research also shows that incorporating a time-series momentum approach into a traditional 60/40 stock/bond portfolio increases returns, reduces volatility, and reduces maximum drawdown.
Finally, Lempérière, Deremble, Seager, Potters, and Bouchard (2014) extends the tests even further, using both futures and spot prices to go back to 1800 for commodity and stock indices . It finds that excess returns driven by trend-following is both significant and stable across time and asset classes .
10. Evidence & Advances in Time-Series Momentum.
While the evidence for time-series momentum was significantly advanced by the papers and teams cited above, there were other, more focused contributions throughout the years that helped establish it in more specific asset classes.
Wilcox and Crittenden (2005) demonstrates that buying stocks when they make new 52-week highs and selling after a prescribed stop-loss is broken materially outperforms the S&P 500 even after accounting for trading slippage.
ap Gwilym, Clare, Seaton, and Thomas (2009) explores whether trend-following can be used as an allocation tool for international equity markets . Similar to Faber (2006), it utilizes a 10-month price-minus-moving-average model. Such an approach delivers a similar compound annual growth rate to buy and hold, but with significantly lower volatility, increasing the Sharpe ratio from 0.41 to 0.75.
Szakmary, Shen, and Sharma (2010) explores trend-following strategies on commodity futures markets covering 48 years and 28 markets. After deducting reasonable transaction costs, it finds that both a dual moving-average-double-crossover strategy and a channel strategy yield significant profit over the full sample period.
Antonacci (2012) explores a global tactical asset allocation approach utilizing both relative and absolute momentum techniques in an approach called “dual momentum.” Dual momentum increases annualized return, reduces volatility, and reduces maximum drawdown for equities, high yield & credit bonds, equity & mortgage REITs, and gold & treasury bonds.
Dudler, Gmuer, and Malamud (2015) demonstrates that risk-adjusted time series momentum – returns normalized by volatility – outperforms time series momentum on a universe of 64 liquid futures contracts for almost all combinations of holdings and look-back periods.
Levine and Pedersen (2015) uses smoothed past prices and smoothed current prices in their calculation of time-series momentum to reduce random noise in data that might occur from focusing on a single past or current price.
Clare, Seaton, Smith and Thomas (2014) finds that trend following “is observed to be a very effective strategy over the study period delivering superior risk-adjusted returns across a range of size categories in both developed and emerging markets .”
11. Unifying Momentum and Technical Analysis: Moving Averages.
Despite their similarities, trend-following moving average rules are often still considered to be technical trading rules versus the quantitative approach of time-series momentum. Perhaps the biggest difference is that the trend-following camp tended to focus on prices while the momentum camp focused on returns.
However, research over the last half-decade actually shows that they are highly related strategies.
Bruder, Dao, Richard, and Roncalli (2011) unites moving-average-double-crossover strategies and time-series momentum by showing that cross-overs were really just an alternative weighting scheme for returns in time-series momentum. To quote,
The weighting of each return … forms a triangle, and the biggest weighting is given at the horizon of the smallest moving average. Therefore, depending on the horizon n2 of the shortest moving average, the indicator can be focused toward the current trend (if n2 is small) or toward past trends (if n2 is as large as n1/2 for instance).
We can see, left, this effect in play. When n2 << n1 (e. g. n2=10, n1=100), returns are heavily back-weighted in the calculation. As n2 approaches half of n1, we can see that returns are most heavily weighted at the middle point.
Marshall, Nguyen and Visaltanachoti (2012) proves that time-series momentum is related to moving-average-change-in-direction. In fact, time-series momentum signals will not occur until the moving average changes direction. Therefore, signals from a price-minus-moving-average strategy are likely to occur before a change in signal from time-series momentum.
Levine and Pedersen (2015) shows that time-series momentum and moving average cross-overs are highly related. It also find that time-series momentum and moving-average cross-over strategies perform similarly across 58 liquid futures and forward contracts.
Beekhuizen and Hallerbach (2015) also links moving averages with returns, but further explores trend rules with skip periods and the popular MACD rule. Using the implied link of moving averages and returns, it shows that the MACD is as much trend following as it is mean-reversion.
Zakamulin (2015) explores price-minus-moving-average, moving-average-double-crossover, and moving-average-change-of-direction technical trading rules and finds that they can be interpreted as the computation of a weighted moving average of momentum rules with different lookback periods.
These studies are important because they help validate the approach of price-based systems. Being mathematically linked, technical approaches like moving averages can now be tied to the same theoretical basis as the growing body of work in time-series momentum.
12. Conclusion.
As an investment strategy, momentum has a deep and rich history.
Its foundational principles can be traced back nearly two centuries and the 1900s were filled with its successful practitioners.
But momentum went long misunderstood and ignored by academics.
In 1993, Jegadeesh and Titman published “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.” Prevailing academic theories were unable to account for cross-sectional momentum in rational pricing models and the premier market anomaly was born.
While momentum’s philosophy of “buy high, sell higher” may seem counterintuitive, prevailing explanations identify its systemized process as taking advantage of the irrational behavior exhibited by investors.
Over the two decades following momentum’s (re)introduction, academics and practitioners identified the phenomenon as being robust in different asset classes and geographies around the globe.
After the financial crisis of 2008, a focus on using time-series momentum emerged as a means to manage risk. Much like cross-sectional momentum, time-series momentum was found to be robust, offering significant risk-management opportunities.
While new studies on momentum are consistently published, the current evidence is clear: momentum is the premier market anomaly.
Did you spot a typo? Did we miss some key history, an influential person, or even a critical research paper? Contate-Nos!
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