Corretor de opções binárias São Vicente: A avaliação e otimização de estratégias de negociação pdf download

Um sistema de negociação de carteira adaptável: Uma otimização de portfólio de risco-retorno usando aprendizagem de reforço recorrente com redução máxima esperada.

Destaques.

Um algoritmo de negociação de aprendizado de reforço com risco de redução esperado é proposto.

O rebaixamento máximo esperado é mostrado para melhorar a geração de sinal do portfólio.

A eficácia do método é validada usando diferentes custos de transação.

Recomenda-se um sistema adaptativo de reequilíbrio de portfólios com reciclagem automatizada.

A teoria de controle dinâmico tem sido usada há muito tempo na solução de problemas de alocação ótima de ativos, e vários sistemas de decisão de negociação baseados em métodos de aprendizado de reforço foram aplicados na alocação de ativos e no rebalanceamento de portfólio. Neste artigo, ampliamos o trabalho existente em aprendizado por reforço recorrente (RRL) e construímos uma alocação ótima de carteira de pesos variáveis sob uma medida de risco descendente coerente, o rebaixamento máximo esperado, E (MDD). Em particular, propomos um método recorrente de aprendizado por reforço, com uma função objetivo de desempenho coerente e ajustada ao risco, o índice Calmar, para obter sinais de compra e venda e pesos de alocação de ativos. Usando uma carteira composta pelos fundos negociados em bolsa mais freqüentemente negociados, mostramos que a função objetiva baseada no risco máximo de saque esperado produz um desempenho de retorno superior comparado às funções objetivas de RRL anteriormente propostas (isto é, o índice de Sharpe e a taxa de Sterling), e essa variável O peso das carteiras RRL longas / curtas supera o peso das carteiras RRL longas / curtas, sob diferentes cenários de custo de transação. Além disso, propomos um sistema de decisão de rebalanceamento de RRL com base no risco E (MDD) adaptável com um mecanismo de reporte de custo de transação e de parada de perda de mercado, e mostramos que o sistema de negociação de portfólio melhor e supera os benchmarks de hedge de fundo consistentemente .

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Otimização da programação genética para uma estratégia de negociação baseada na força do feedback do sentimento.

Este estudo é motivado pelas descobertas empíricas de que as notícias e as mensagens no Twitter das mídias sociais (tweets) exibem poder preditivo persistente sobre o movimento do mercado financeiro. Com base na evidência de que os tweets são mais rápidos que as notícias revelando novas informações de mercado, enquanto as notícias são consideradas uma fonte de informação mais confiável do que os tweets, propomos uma estratégia comercial superior baseada na força de feedback entre as notícias e os tweets usando programação genérica. método de otimização. A principal intuição por trás dessa abordagem baseada na força do feedback é que o impulso conjunto das duas séries de sentimentos leva a sinais significativos do mercado, que podem ser explorados para gerar lucros comerciais superiores. Com o trade-off entre a velocidade da informação e a sua confiabilidade, este estudo visa desenvolver uma estratégia de negociação ótima usando a força de feedback do sentimento dos investidores com o objetivo de maximizar o retorno ajustado ao risco medido pelo índice de Libra Esterlina. Descobrimos que as estratégias baseadas no feedback do sentimento geram retornos de mercado superiores com redução máxima baixa no período de 2012 a 2015. Em comparação, as estratégias baseadas no indicador de retroalimentação geram mais de 14,7% de taxa de libra esterlina em comparação com 10,4% e 13,6% estratégias baseadas em indicadores técnicos e a estratégia básica de comprar e manter, respectivamente. Após considerar os custos de transação, a estratégia baseada no indicador de sentimento supera a estratégia baseada em indicadores técnicos de forma consistente. O backtesting mostra que a vantagem é estatisticamente significativa. O resultado sugere que o indicador de feedback do sentimento fornece suporte no controle da perda com menor rebaixamento máximo.

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Artelys Knitro - Solucionador de otimização não-linear.

Os problemas de otimização não-linear surgem em inúmeras aplicações comerciais e industriais: otimização de portfólio, fluxo de potência otimizado, controle preditivo de modelo não-linear, problemas de equilíbrio de Nash. Para resolver esses problemas desafiadores, os clientes em centenas de sites em todo o mundo confiam na Artelys Knitro por sua eficiência e robustez.

O Artelys Knitro foi desenvolvido pela Ziena Optimization desde 2001. Desde a aquisição da Ziena e sua equipe de desenvolvimento em 2015, a Artelys é responsável pelo desenvolvimento e distribuição de software em todo o mundo.

O solucionador mais avançado para otimização não linear.

Resolva problemas de otimização não linear com milhões de variáveis. Saber mais .

Eficiente & amp; solução robusta em problemas de grande escala.

Quatro algoritmos de ponto ativo / conjunto interno para PNL.

Três algoritmos do MINLP para otimização discreta.

Restrições de complementaridade para problemas de equilíbrio.

Método multi-start paralelo para otimização global.

Muitos recursos extras baseados nos feedbacks dos clientes.

Fácil de usar e bem documentado.

Amplamente utilizado na academia e na indústria.

Mais de 300 instituições em mais de 40 países confiam na Artelys Knitro. Saber mais .

Berkeley, Columbia, Harvard, MIT, Princeton, Stanford.

Cath. Univ. do Chile, Univ. de São Paulo.

ESSEC, ETH Zürich, LSE.

Nat. Univ. de Singapura, Tsinghua Univ.

Univ. de Melbourne, Univ. de Queensland.

Mapa de usuários do Artelys Knitro.

Consultoria econômica.

Empresas de engenharia mecânica.

Oil & amp; Empresas de gás.

Regulamentar & amp; Decisores políticos.

Desenvolvedores de software (como terceiros)

Obter Artelys Knitro.

O Artelys Knitro está disponível para empresas, fornecedores de software, acadêmicos e institutos de pesquisa. Saber mais .

O Artelys Knitro está disponível como um componente independente ou pode ser incorporado em um pacote maior (por exemplo, software OEM). Os consultores da Artelys (nível de PhD) estão acostumados a implementar soluções de otimização em toda a empresa e irão ajudá-lo a obter o melhor desempenho do Artelys Knitro. Contate-nos para mais informações.

Universidades e Centros de Pesquisa.

A Artelys oferece programas de parceria com universidades, centros de treinamento e pesquisa públicos para promover o ensino e a pesquisa em otimização. Os parceiros acadêmicos recebem o Artelys Knitro por um preço com desconto. Contate-nos para mais informações.

Se você deseja testar nosso solucionador primeiro, clique aqui para baixar uma licença de avaliação e começar a usá-la agora.

Pense um passo à frente e deixe Artelys Knitro ser sua vantagem competitiva!

As técnicas de otimização utilizadas pela Artelys Knitro oferecem a principal combinação de eficiência e robustez computacional. O Artelys Knitro é o único solucionador não linear com quatro algoritmos diferentes, permitindo resolver uma grande variedade de problemas complexos não-lineares.

Características principais.

Novos recursos do Artelys Knitro 10.3.

• A API do Python agora suporta o Python 3.

• Nova opção "cg_precond" para o pré-condicionamento dos subproblemas de gradiente conjugado nos algoritmos do ponto interior do Knitro.

• Diversas melhorias na álgebra linear interna para melhor robustez e eficiência.

• Melhorias significativas nos modelos de mínimos quadrados de grande escala.

• Melhorias na “fase de restauração de viabilidade”, levando a uma detecção mais rápida de modelos inviáveis.

• Vários aprimoramentos da interface R.

• Melhorias gerais de eficiência e robustez em modelos gerais não lineares, incluindo modelos com variáveis inteiras.

Classes de problemas resolvidos por Artelys Knitro.

• Problemas gerais não lineares (PNL), incluindo não convexos.

• Sistemas de equações não lineares.

• Problemas lineares (LP)

• Problemas quadráticos (QP / QCQP), convexos e não convexos.

• Problemas de mínimos quadrados / regressão, linear e não linear.

• Programas matemáticos com restrições de complementaridade (MPCC / MPEC)

• Problemas não lineares inteiros mistos (MIP / MINLP)

• Problemas de otimização sem derivativos (DFO)

Interfaces de programação.

Sistemas de Modelagem

Artelys Knitro e MATLAB.

O Artelys Knitro apresenta uma interface para o ambiente de computação MATLAB® que suporta todos os principais recursos do Knitro, incluindo a capacidade de modelar e resolver modelos de programação inteira mista (MIP) e programas matemáticos com restrições de equilíbrio (MPEC). Essa interface, chamada “knitromatlab”, substitui a interface de ktretch anterior fornecida pela MATLAB Optimization Toolbox. O Knitromatlab usa uma API muito semelhante à ferramenta de otimização não-linear MATLAB fmincon, fornecendo um mecanismo fácil para a portabilidade de código entre os dois, além de disponibilizar os recursos aprimorados no Artelys Knitro. Um extenso exemplo e documentação sobre o uso do knitromatlab são fornecidos com a distribuição Knitro e descritos no Manual do Usuário do Artelys Knitro.

Artelys Knitro e AMPL.

O AMPL é uma linguagem de modelagem popular para otimização que permite aos usuários representar seus problemas de otimização em um formato intuitivo, legível e fácil de usar. Artelys Knitro fornece um driver dedicado “knitroampl” para usá-lo a partir do AMPL. Knitroampl dá acesso a todos os recursos do solver. Um extenso exemplo e documentação sobre o uso do knitroampl são fornecidos com a distribuição Knitro e descritos no Manual do Usuário do Artelys Knitro.

Artelys Knitro e R.

R é um ambiente de software de código aberto para computação estatística, disponível sob a Licença Pública Geral GNU. É desenvolvido e mantido pela Fundação R. Artelys Knitro fornece uma biblioteca dedicada para usá-lo em R. A biblioteca Artelys Knitro R dá acesso a todos os recursos do solver e inclui uma função dedicada para resolver os mínimos quadrados não-lineares. Um extenso exemplo e documentação sobre o uso do Knitro de R são fornecidos com a distribuição Knitro e descritos no Manual do Usuário do Artelys Knitro.

Sistemas operacionais.

Business & amp; aplicações acadêmicas.

O Artelys Knitro é atualmente usado em muitas áreas de aplicação, demonstrando assim sua versatilidade.

Esta seção detalha algumas das aplicações típicas do Artelys Knitro com referências à literatura acadêmica. Da matemática fundamental ao desenvolvimento sustentável, o Artelys Knitro foi considerado útil por uma grande variedade de profissionais de Pesquisa Operacional.

Não hesite em contactar-nos para receber mais informações sobre Artelys Knitro e suas histórias de sucesso.

Financeiro & amp; bancário.

Os métodos de otimização desempenham um papel vital na precificação de opções, seleção de portfólio e lances estratégicos. As soluções de modelagem e software oferecem assistência valiosa na tomada de decisões.

Usos típicos de Artelys Knitro:

• Otimização de portfólio com custos de transação.

• Melhor preço e gerenciamento de risco.

• Licitações e leilões estratégicos (equilíbrio de Nash)

• Byrd, J. R. e Liu, Z. (2007): "Métodos de otimização não linear com aplicações financeiras". John Birge resume as aplicações financeiras e o papel que os métodos de otimização não-linear desempenham em sua solução.

• Nocedal, J. (2008): "O ZIENA Solver para a American Options Pricing". Jorge Nocedal discute a robustez, velocidade e facilidade de uso do mecanismo de software para negociação de opções em tempo real.

Economia computacional & amp; teoria do jogo.

Usos típicos de Artelys Knitro:

• Desenho de políticas econômicas.

• Conlon, C. T. (2009): "Um Modelo Dinâmico de Custos e Margens na Indústria de TV LCD", manuscrito não publicado.

• Hanson, D. A., Kryukov, Y., Leyffer, S. e Munson, T. S. (2009): "Modelo de Controle Ótimo da Transição de Tecnologia", No 2009-E24, Working Papers da GSIA da Carnegie Mellon University, Tepper School of Business.

• Dubé, J.-P., Fox, J. T. e Su, C.-L. (2012): "Melhorando o desempenho numérico de estimativa de demanda de coeficientes aleatórios de escolha discreta agregada estática e dinâmica", em Econometrica, 80 (5), 2231-2267.

• Egesdal, M., Lai, Z. e Su, C.-L. (2012): "Estimando Jogos Dinâmicos de Escolha Discreta de Informações Incompletas", Working Paper.

Estatísticas e amp; análise de dados.

Usos típicos de Artelys Knitro:

• Análise não linear de mínimos quadrados (regressão / ajuste de dados)

• Suporte a máquinas de vetores.

• Wang, G., Zhu, Z., Du, W. e Teng, Z. (2008): "Análise de Inferência na Re-Publicação de Dados de Preservação de Privacidade", Data Mining, 2008, ICDM '08, Oito IEEE International 1079-1084.

• Fuchs, M. e Neumaier, A. (2010): "Otimização na análise de classes latentes", Relatório técnico TR / PA / 10/89, CERFACS.

• Rauchs, G. e Dumitriu, D. (2010): "Identificando a identificação de parâmetros de teste usando um procedimento de otimização baseado em algoritmos genéticos", em Proc. da Academia Romena, Série A: Matemática, 10 (2), 165-172.

O gerenciamento de redes de distribuição, operações otimizadas da planta, gerenciamento de receita e risco e precificação estratégica desempenham um papel cada vez mais importante no setor de energia.

• Problemas não-lineares de fluxo de potência (OPF) não lineares (AC).

• Problemas do OPF com restrição de segurança (SCOPF).

• Otimização de custos de geração e perdas de transmissão.

• Modelagem de efeitos de cabeça no gerenciamento ideal de reservatórios de água. Problema não linear de fluxo de potência (OPF).

• Óleo & amp; otimização da produção de gás.

• Artelys: "Cálculos ótimos do fluxo de potência usando o Knitro". Business case: Tractebel Engineering - Otimizando o gerenciamento do sistema de transmissão de energia elétrica.

• Hu, B., Cañizares, CA e Liu, M. (2010): “Regulação de Tensão Secundária e Terciária Baseada em Fluxos de Potência Ótima”, Dinâmica e Controle do Sistema de Potência em Massa (iREP) - VIII (iREP), 2010 iREP Symposium 1-6.

• Gutierrez-Martinez, VJ, Cañizares, CA, Fuerte-Esquivel, CR, Pizano-Martinez, A., e Gu, X. (2011): "Fluxo de Potência Ótimo Construído por Limite de Segurança de Rede Neural", Transações IEEE em Potência Systems, 26 (1), 63-72.

• Ferreira, E. C., Baptista, E. C., e Soler, E. M. (2012): "Uma investigação sobre a estratégia de atualização de parâmetros de barreira e a Solução de Fluxo de Potência Ótimo", EngOpt 2012, 3ª Conferência Internacional sobre Otimização de Engenharia.

• Barragan Hernandez, A., Vazquez-Roman, R., Rosales-Marines, L., Garcia-Sánchez, F.: "Uma estratégia para simulação e otimização da produção de gás e petróleo", Computadores e Engenharia Química 30, 2005, 215–227.

• Liu, Z., Wang, S. e Ouyang, Y. (2017): "Design Confiável da Cadeia de Fornecimento de Biomassa sob Sazonalidade de Matéria-Prima e Rupturas Probabilísticas da Instalação", Energies 2017, 10, 1895.

• Stock, D. S .; Sala, F .; Berizzi, A .; Hofmann, L. (2018): "Controle Ótimo de Parques Eólicos para o Gerenciamento de Energia Reativa TSO-DSO", Energies 2018, 11, 173.

Desenvolvimento sustentável.

Usos típicos de Artelys Knitro:

• Análise de população virtual.

• Gestão do crescimento populacional.

• Controle do caminho de transição.

• Artelys: "Otimização da colheita de populações de peixes". Business case: Instituto Finlandês de Pesquisa Florestal (Metla).

• Tahvonen, O. (2008): "Colheita ótima de populações de peixes estruturadas por idade", CEMARE Research Paper, P165.

• Tahvonen, O., Pukkala, T., Laiho, O., Lähde, E. e Niinimäki, S. (2010): “Manejo ótimo de povoamentos de abeto norueguês de idade desigual”, em Ecologia e Gestão Florestal, 260 ( 1), 106-115.

• Hänsela, M. C., Quaasa, M. F. (2018): "Distribuição Intertemporal, Suficiência e Custo Social do Carbono", em Ecological Economics 146, 520-535.

Controle ótimo & amp; otimização dinâmica.

Usos típicos de Artelys Knitro:

• Otimização com equações diferenciais parciais.

• Otimização com restrição de PDE com decisões discretas.

• Abdallah, L., Haddou, M. e Khardi, S. (2010): "Otimização de parâmetros operacionais de aeronaves reduzindo a emissão de ruído", em Ciências Matemáticas Aplicadas, 4 (11), 515-535.

• Nahayo, F., Khardi, S., Ndimubandi, J., Haddou, M., e Hamadiche, M. (2010): "Problema de Controle Ótimo Acústico de Duas Aeronaves: Algoritmos SQP", em ARIMA, 14, 101- 123

• Você, F. e Leyffer, S. (2011): "Otimização Dinâmica Integral e Mista para Planejamento de Resposta a Derrames de Óleo com Integração de um Modelo de Resistência Dinâmica a Óleo", em AIChE Journal, 57 (12), 3555-3564.

Telecomunicação.

Usos típicos de Artelys Knitro:

• Otimização da rede de transmissão.

• Sosa-Paz, C., Ruckmann, J. e Sánchez-Meraz, M. (2010): "Roteamento Conjunto, Agendamento de Link e Controle de Potência para Redes Wireless Multi-hop para Sistemas CDMA / TDMA", na Científica, 14 (4), 165-172.

Ótica & amp; espectroscopia.

Usos típicos de Artelys Knitro:

• Controle de polarização de luz.

• Análise conformacional de isômeros.

• Lott, GA, Perdomo-Ortiz, A., Utterback, JK, Widom, JR, Aspuru-Guzikb, A. e Marcus, AH (2011): "Conformação de dímeros de porfirina auto-organizados em vesículas de lipossomos por modulação de fase Espectroscopia de fluorescência 2D ", em Proceedings of National Academy of Sciences, 108 (40), 16521-16526.

• Tripathi, S., Paxman, R., Bifano, T. e Toussaint, KC Jr. (2012): "Matriz de transmissão vetorial para o comportamento de polarização da propagação de luz em meios de alta dispersão", em Optics Express, 20 (14). ), 16067-16076.

Matemática & amp; geometria.

Usos típicos de Artelys Knitro:

• Minimização da curvatura da forma através da regularização de contorno.

• verificação de prova independente.

• Hales, T. C. e McLaughlin, S. (2010): "A conjectura dodecaédrica", em Jornal da sociedade matemática americana, 23 (2), 299-344.

• Bretin, E., Lachaud, J.-O., e Oudet, E. (2011): "Regularização do contorno discreto pela energia de Willmore", no Journal of mathematical imaging and vision, 40 (2), 214-229.

Manual em PDF.

Documento portátil de arquivo pdf grátis.

química do cabo após a unidade de papel 2 2008.

coberto e exemplos são usados que são típicos de perguntas de papel passado com cada passo totalmente & # 8230 ;. 424 pp. Paperback. 978-0-521-70306-2. Química: AS Level e A Level & # 8230 ;. Resumo dos principais conceitos e atribuições no final de cada unidade & # 8230; .. Cape Town, África do Sul. Telefone: +27 21 412 7800. Fax: +27 21 419 0594 & # 8230; cambridge / pt-br / educação / catálogo / 15797_72dpi. pdf.

Demanda & amp; Fornecimento para professores de ciência e tecnologia na & # 8230;

Neste artigo, propomos soluções viáveis e investigamos a dinâmica & # 8230 ;. oferecido como dois exames separados, Unidade 1 e Unidade 2 e é feito no final & # 8230 ;. do programa de química CAPE; aqueles que os professores geralmente têm mais problemas & # 8230; Ao longo dos últimos anos, as equipes formam a Faculdade de Ciências e o & # 8230; ieeexplore. ieee/iel5/4760334/4760335/04760372.pdf? arnumber=4760372.

PAPEL DE POSIÇÃO SOBRE A REFORMA DA TERRA NA CIDADE DA CIDADE DO CABO.

O objetivo deste artigo é analisar a situação atual na Cidade do Cabo em relação à terra & # 8230; Despojados de terra após 19 de junho de 1913, como resultado de leis discriminatórias passadas e ". 3.3.2 Falta de Unidade Central ou Carteira para Reforma Agrária & # 8230 ;. Final de Junho de 2008. •. Submissão enviada ao conselho para a restauração. & # 8230; capetown. gov. za/en/IDP/Documents/Stadutory Compliance Plans / F1.Position Paper on Land Reform. Jan 2007.pdf.

Avaliação da preparação de estudantes de química do primeiro ano & # 8230;

JUNHO 2008 Southern Cape Old Car Club.

papel e é difícil convencer alguém a escrever algo para publicação. & # 8230; 2 a 3 de agosto de 2008 & # 8211; Festival de Oakdale em Riversdale. & # 8230; scocc. co. za/pdfdocs/JUNE 2008 páginas únicas rev1.pdf.

para o próximo LeveL.

alunos excelentes do passado. É muito gratificante ver para onde alguns foram desde o seu CXC. Papel entre eles falta de habilidades precisas e interpretação de questões. & # 8230; .. The Caribbean Examiner cxc SEpteMBeR 2008 21. CABO UNIT & # 8230; Prin Fundamental. de Química 2. 4.00. Estudos de Comunicação & # 8230; cxc / SiteAssets / CXC_examiner printery copy. pdf.

20607 CXC AnnualReportInside. indd.

Uma série de livretos de papel do CAPE foram publicados e distribuídos em toda a região para venda. & # 8230; & # 8230; Estudos de Comunicação, Unidade de Química 1, Unidade de Química 2, & # 8230; .. Setembro de 2007 e exames de maio / junho de 2008. & # 8230; cxc / SiteAssets / 20607 CXC AnnualReportInside. indd. pdf.

Cabo Verde & # 8211; Relatório do National Composite Policy Index (NCPI) de 2008.

c) Documentos de Estratégia de Redução da Pobreza: b) País Comum. Avaliações / Nações Unidas & # 8230; 2. O país possui um AIDS multisetorial nacionalmente reconhecido & # 8230; atividades implementadas pela sociedade civil no ano passado? & # 8230; .. A unidade de M & amp; E / Departamento gerenciar um banco de dados nacional central? & # 8230; data. unaids / pub / Report / 2008 / capeverde_2008_ncpi_en. pdf.

S & amp; T Seco 2 Pt1 PRINT. indd.

Unidade, uma unidade de DRFN. Este programa tem treinado professores em ambiente & # 8230; A Namíbia produziu um total de 480 documentos entre os anos de 2001 e 2007 & # 8230; Há um claro aumento na produção nos últimos três anos, que "# 8230; Química. Universidade do Botswana; Universidade da Cidade do Cabo; Universidade de Witwatersrand; & # 8230; sarua / files / publications / ST_country_Namibia. pdf.

Manual do Catálogo e Manual do Aluno da Cape Fear Community College 2008-2009.

famílias transferidas por empresas nos últimos 12 meses (até 1% do total de estudantes de fora do estado), estudantes com & # 8230; & # 8230; Duas (2) unidades de estudos sociais (incluindo uma unidade de estudos e artigos de pesquisa dos EUA; revisão de artigos; crítica de livros; áreas: agricultura, astronomia, biologia, química, engenharia - & # 8230; 8230; cfcc. edu/cat/cat0809/Catalog2008.pdf.

Boletim informativo de setembro de 2008.

Bem-vindo à nossa primeira edição do boletim de 2008 CAPE. & # 8230 ;. Este mês, nossa turma está estudando uma unidade baseada em ciência: a Química de Sala de Aula. Para enriquecimento, & # 8230; capeisgreat / docs / newsletter / boletim informativo de setembro de 2008.pdf.

Departamento de Química.

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Os artigos abordarão três aspectos da pesquisa em educação química: (1) a motivação & # 8230; de Química, Universidade do Sudeste do Estado de Missouri, RH 201G MS6400, Cape & # 8230; de Química, Universidade de Connecticut, 55 North Eagleville Road, Unit & # 8230; .. Durante os últimos anos tem havido um aumento no interesse em internet & # 8230; divched / DivCHED / 2008SpringACSSymposia. pdf.

Os efeitos do ï¿½ter fenetï¿½ico de ï¿½ido cafeico (CAPE) em induzidos por TNBS & # 8230;

Durante os últimos anos, muitos pesquisadores relataram "###"; Dig Dis Sci (2008) 53: 1609-1617. DOI 10.1007 / s10620-007-0056-2 & # 8230 ;. de formazan foi medido a 560 nm e 25 ° C. Uma unidade & # 8230 ;. 2 Efeitos do CAPE (10 e 30 mg / kg) administrados intraperi-. Grisham MB, McCord JM (1986) Química e citotoxicidade de & # 8230; springerlink / index / 345UG647255H22J3.pdf.

Core Biology, Core Chemistry e Core Physics são voltados para a ciência separada. & # 8230 ;. Exercícios adicionais de trabalhos anteriores e exemplos claros e trabalhados. & # 8230; para o CAPE. Unidade 1. 352 pp. 978-0-521-69699-9 Paperback GBP 18,95. Unidade 2 & # 8230; .. Reino em papel totalmente livre de cloro contendo 80% de fibras recicladas. 2008. 128.232.233.16/uk/education/order/catalogue/caribbean_2009.pdf.

JULHO NEWSLETTER 2008 FINAL. pmd.

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DEZEMBRO NEWSLETTER 2008 PMB. pmd.

discussão sobre as tendências passadas, presentes e futuras na Agricultura Africana e & # 8230; Page 2 & # 8230; .. Química onde ele foi mostrado as novas instalações de pesquisa e interagiu com a Unidade & # 8230; .. Professor e Mike Watkeys apresentou dois trabalhos na Bienal & # 8230; Cabo Ocidental. O melhor cartaz do. Prémio de Conferência foi atribuído a & # 8230; sciag. ukzn. ac. za/documents/DECEMBER NEWSLETTER 2008.pdf.

theBulletin RHIC, Usuários de AGS & # 8217; Reunião reflete sobre passado, aparência & # 8230;

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por LK Whalley & # 8211; 2010 (SOAPEX-2) foi realizado no Cabo Grim, no Noroeste de Tas - & # 8230; representante da área circundante (Read et al., 2008; Lee & # 8230; Neste trabalho, apresentamos OH e HO2 & # 8230 ;. tubo de vidro de quartzo de 60 cm de comprimento (diâmetro interno 22 cm) após a & # 8230; .. área de face por unidade de volume,? é o coeficiente de absorção, R é "atmos-chem-phys / 10/1555/2010 / acp-10-1555-2010.pdf".

ÍNDICE.

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PERFIS RESUMIDOS DE CADEIRAS SARCÍSIAS PARA 2007 Prof Vladimir B. Bajic & # 8230;

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NOVEMBRO 2008 vol. 2

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TOp dicas para ajudar você passar MaTric.

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Livro Branco da Defesa da RPC 2008.

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beth moore.

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EM MEMORIAM SALLIE BETH MOORE.

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SUPERINTENDENTE Dr. Beth Moore SUPERINTENDENTE ASSISTENTE Lisa Prevatt.

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Library Mailing List.

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Morning Service Morning Service DOUBLE THE FUN. Beth Moore’s …

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Women’s Retreat: “Loving Well” by Beth Moore ADULT 4 Ladies.

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NCN Library Master Inventory – 2009

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“Believing God” Women’s Bible Study By Beth Moore First Session …

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Video Curriculum Kit Catalog (As of 7/9/09)

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Reach out and your tickets are free! Beth Moore simulcast in …

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Beth Moore, Internationally Known Bible Teacher and Author, To …

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Beth Moore, Internationally Known Bible Teacher and Author, To …

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Women’s Ministry Resources.6.08.

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Federal and State Agencies 8/3/2005 Town of Pahrump and Local …

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Loving Well by Beth Moore.

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Selecting the Best Materials for Growth Group Study.

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BIBLE STUDIES INVENTORY.

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with Beth Moore with Beth Moore.

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SUNDAY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY.

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Intimacy with God (Women’s Studies)

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Music by Travis Cottrell.

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GRACE MEDIA BETH MOORE.

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lasts anatomy regional and applied.

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Anatomy, Regional and Applied.

to workers beginning in the field. Anatomy, Regional and Applied. R. J. LAST, M. B., B. S., F. R.C. S. Third Edition. Pp. xvi + 840, with 406 illustrations. & # 8230; pmj. bmj/content/39/458/730.4.full. pdf.

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Anatomy for pain medicine.

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BASIC SURGICAL TRAINEE (BST) TEXTS.

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Microscopic Anatomy of Inferior Medullary Velum Of Cerebellum.

Accessory Lingual Foramen in Adult Indian Mandibles.

Conjunctiva-Upper Respiratory Tract Irrigation for Early Diagnosis …

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Anatomy, Regional and Applied.

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Anatomy: Regional and Applied.

22 Feb 2010 … Anatomy: Regional and Applied, Seventh Edition, R. J.. Last. Pp. 395. illustrated. Churchill Livingstone. Edin - burgh. Londres. Melbourne. & # 8230; hwmaint. pmj. bmj/cgi/reprint/61/718/761.pdf.

Designing Anatomy Program in Modern Medical Curriculum: Matter of …

S hakespeare was right — the fountain of youth.

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Anatomy. A general text such as. Lasts Anatomy: Regional and Applied. Ou. Clinically Oriented Anatomy (K Moore). An Atlas such as. Colour Atlas of Anatomy … arrmos. co. nz/media/50457/radiology v3.pdf.

Basic Surgical Training Anatomy Course – Propsed.

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The right inferior lung margin: anatomy and clinical implication.

Clinical Anatomy, 7th edn (Blackwell. Scientific, Oxford), pp. 5-6. LAST, R J, 1990. Anatomy Regional & Applied, 8th edn. (Churchill Livingstone, Edinburgh) … bjr. birjournals/cgi/reprint/66/786/503.pdf.

every medical and scientific library and should adorn the shelves of every biologist. A. A. Sandosham. ANATOMY: REGIONAL AND APPLIED. By Last, R. J. 2º & # 8230; smj. sma. sg/0102/0102smj8.pdf.

THE INSERTION OF THE ILIOPSOAS AS A DESIGN FAVOURING LATERAL …

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Lumbosacral Plexuses Learning Diagrams.

The following are Recommended Basic Texts for Medical Students OR …

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Distal Biceps Tendon Anatomy: A Cadaveric Study.

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Distal biceps brachii tendon anatomy revisited from a surgical …

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ROYAL AUSTRALASIAN COLLEGE OF DENTAL SURGEONS.

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Resource List: Anatomy and Physiology.

Last’s anatomy: regional and applied. 10 ed. Edinburgh: 1999. REF. QS 4/12. B. C. McMinn: A colour atlas of human anatomy. London: 1988. … sswahs. nsw. gov. au/CCQ/Library/pdfs/Resources/Anatomy – Resource List. pdf.

Multiple anomalies of the brachial plexus: a case report.

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Last’s Anatomy : Regional and Applied /. QM 531.L34 1999. Lippincott’s Essentials for Nursing Assistants / P. Carter. RT 84.C37 2007 … centralgatech. edu/library/ResLists/Pharmacy Technology-Library Resources-2008.pdf.

Last’s Anatomy. Regional and Applied. London: Churchill Livingstone, 2001: pp-115. 11. Standring S (2005). Gray’s Anatomy. The Anatomical Basis of Clinical … lfhk. cuni. cz/Data/files/Casopisy/2007/AM4_07-6.pdf.

The additional tendons of the extensor digitorum muscle of the …

Last’s Anatomy. Regional and Applied. 10th Edi - tion, Edinburgh, Churchill Livingstone, 2001, p. 71—72. 3. von Schroeder HP, Botte MJ. Anatomy of the … bmj. sk/2008/10912-12.pdf.

Unusual venous sinuses.

Anatomical variations of the plantaris muscle and a potential role …

Last’s Anatomy. Regional and Applied. 10th. Ed. London: Churchill Livingstone. p 141. Standring S. 2004. Gray’s Anatomy. 9th Ed. London: Churchill Liv - … aucmed. edu/pdf/CLINICAL-ANATOMY-JOURNAL. pdf.

The great saphenous vein in nigerians, a pilot study.

by OA Ayannuga – 2009Last’s Anatomy, Regional and Applied. 10th edition. [S. l.]: Churchill Livingstone, 1999. p. 108-110. ENGELHORN, CA., ENGELHORN, AL., CASSOU, MF. and … jms. br/PDF/v26n2a05.pdf.

Extra-pelvic compression of the sciatic nerve.

Last R. J., Anatomy Regional & Applied, 7th Ed, Churchill Livingston Fig 3.28 pg 147. Moore. K. L., “Clinically oriented anatomy” Baltimore. & # 8230; fulltext. ausport. gov. au/fulltext/2001/acsms/papers/TAYL2.pdf.

Anomalous extensor tendons of hand: a case report with clinical …

Thirty cruciate ligaments were retrieved from either.

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We carried out a cadaver study of 16 iliolumbar.

Sinnatamby C. Last’s anatomy regional and applied. Tenth ed. Edinburgh: Churchill Livingstone, 1999:270. 10. Crock HV, Yoshizawa H. The blood supply of the … jbjs. uk/cgi/reprint/84-B/7/1046.pdf.

Conservative surgical management of ruptured interstitial pregnancy.

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FRCS, FRACS, HonFMAA (1903-1993)

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Chiropractic Manipulative Reflex Technique (CMRT).

Posterior Slope of Tibial Plateau in Adult Nigerian Subjects.

Accessory Tendon and Tripartite Insertion Pattern of Fibularis …

Brachialis Muscle Anatomy. A Study in Cadavers.

New York: Elsevier Churchill Livingstone; 2005. 2. Sinnatamby CS. Last’s anatomy: regional and applied. 10 ed. Edinburgh: Churchill Livingstone; 1999. … ejbjs/cgi/reprint/89/6/1293.pdf.

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Vermont Board of Nursing.

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Nurse without Exam Application.

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May 15, 2009 – News for Member LPNs.

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OASIS, Coding & Exam Brochure. p65.

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Page 1 of 7 South Carolina Board of Nursing October 2009 Insert …

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January 2010 Revised.

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OASIS, Coding & Exam Brochure. p65.

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NAME M. Susan Jones TITLE Professor ADDRESS School of Nursing …

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INDIANA STATE BOARD OF NURSING Thursday, April 16, 2009 Indiana …

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The Board of Examiners for Nursing held a meeting on September 2 …

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Can a LPN or LPTN do assessments? Refresher Course: Do you need it …

Kentucky. • Nebraska. • Nevada. • New Mexico. • North Carolina. • North Dakota …. BOARD MEETING DATES. The public is invited to attend ASBN Meetings. Groups of more than five should contact Carmen … Program until the year 2010. •Granted continued full approval to …. night studying for exams, while, for … arsbn/pdfs/asbn-update-2005/asbnv9n6.pdf.

States That Do NOT Require CBCs.

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REQUIREMENTS FOR LICENSURE INTO ALABAMA.

Columbia, Florida, Idaho, Iowa, Indiana, Kentucky, Louisiana (RN only), Maine, Maryland, …. date with the Board office. This can be done under the online services … An LPN license that is between now and 08/31/2010 will receive an active … SBTPE OR NCLEX DATA. DATE OF EXAM. DATE OF EXAM. DATE OF EXAM. SUBJECT … abn. state. al. us/main/downloads/applications/APPLICATION FOR LICENSURE BY ENDORSEMENT. pdf.

Kansas State Board of Nursing N-STAT.

Peggy Fishburn, LPN, board member, Kentucky Board of. Nursing, received the Exceptional … through 2010 . ? Adoption of the Guiding Principles of. Nursing Regulation . & # 8230; (State exam to be scheduled at a later date). Fee: … ksbn/nursingnewsletter/2008/KS032008.pdf.

the multitude of students, parents, alumnae, faculty, staff, board members, … the exam schedule. Both students and parents are encouraged to read this publication thoroughly. & # 8230; The All-Sports Banquet will be held on May 16, 2010 at 5:00 …. ton, Kentucky. It opened Friday, February 5, and ran until … motherofmercy/s/746/images/editor_documents/Mercy Minutes/2009-10/marchnoartmm. pdf.

Certified Orthodontic Assistant.

DANB accepts 2010 exam applications through March 31, 2011 ….. 60-day window to schedule and take exam 3-4 weeks from exam date. Exam Prerequisites …… Carol Oeder, CDA, COA, CDPMA, FADAA, LPN (Board Liaison). John Olsen, DDS, MAGD …. 0881 West Kentucky Tech College. MAINE. 0846 University of Maine Augusta/ … danb/PDFs/2010COA. pdf.

KENTUCKY EMPLOYABILITY CERTIFICATE (KEC)

2010 new public agenda in July 2005. As part of this plan, CPE has placed a … The Kentucky Workforce Investment Board (KWIB) endorsed the KEC and ….. To date, KCTCS has issued 2726 Gold and Silver Kentucky Employability …. g LPN … nationaloccorg. siteprotect/crc/PDFs/Kentucky. pdf.

#9912 2nd Qtr Newsletter 2004.

Regional Medical Center for over thirty years and is currently an LPN … The Board of Trustees and Medical Staff of Highlands Regional Medical Center … Dr. Hieronymus earned his medical degree from the University of Kentucky College …. to-date facility that includes six private labor, delivery, and recovery … hrmc/pdfs/2010_winter_vital_signs. pdf.

The West Virginia Center for Nursing.

Kentucky. 3%. $56780. *Pennsylvania … Examiners for Licensed Practical Nurses (LPN Board). The Chairman ….. RN students and $500 for LPN students during FY 2010. …. Without the examination of employment location of each nursing …. To date, the Center for Nursing has not developed an effective strategic … legis. state. wv. us/Joint/PERD/perdrep/CenterNursing_12_2009.pdf.

2008–2010 Catalog please note: dates are subject to change. 2008-2009 …… students are required to take a LPN articulation course that is a transition … tacomacc. edu/upload/files/academics/catalog/2008-10_Catalog. pdf.

CU 2009 Catalog February 5 2010 FINAL RevEP.

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Affording Higher Education 2009–2010. 215. KHEAA. Statewide …… Board of Education, 202 West Washington. Street, Glasgow, KY 42142, phone … kheaa/pdf/pubs/ky/ahe/ahekyscholar. pdf.

Colorado Division of Registrations. Office of Licensing–Nursing …

Arizona, Arkansas, Delaware, Idaho, Iowa, Kentucky, Maine, Maryland, Mississippi, … If you are an LPN with IV authority, you must submit a separate application after ….. to other state regulatory agencies, testing and examination vendors, …. Encontro. PART 2: To be completed by the LICENSING BOARD of the state of … centralcoahec/documents/LPN_ReinstatmentApplication. pdf.

HEALTH For All in 2010: Confirming Our Commitment.

April L. Vestal, WV University Office of Rural Health CCPH Board Member); ….. Northeast Kingdom, Lamoille County and the Middlebury areas, although the lack of LPN’s is apparent …… Director of the South Central Kentucky Area Health Education Center … medical history, and as much of the physical exam as is … depts. washington. edu/ccph/pdf_files/confdescrip01.pdf.

foundations of financial management, 8th canadian edition.

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Foundations of Financial Management 12e By Stanley B. Block …

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BOOK LIST FALL 2008.

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Comprehensive Test Banks and solution manuals.

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Propfolio Management.

Quantitative Finance | Portfolio Management | Negociação Sistemática.

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Beat the Market with Meucci and Markowitz.

Introdução.

I am very excited to finally share some of my research exploring Meucci’s ( Meucci (2005) ) portfolio optimization methods, and how the resulting portfolios compare to the use of historical data. For those unfamiliar with Attilio Meucci, he runs an annual Advanced Risk and Portfolio Management Bootcamp in New York City every summer. The bootcamp attracts academics and professionals within the industry, and over 6 intense days, topics and techniques in Risk Management and Portfolio Management are discussed in depth. I was fortunate to attend the bootcamp during the summer of 2014. In this post, I will be exploring portfolio optimization of the 9 Sector SPDR ETFs, using Meucci’s methods, specifically his framework outlined in “The Checklist” ( Meucci (2011) ), and I will be verifying if Meucci’s methods add value over the use of historical data. Lastly, I will be using Markowitz’s ( Markowitz (1952) ) quadratic utility framework for portfolio optimization, and will be comparing results using Minimum Variance and Mean Variance optimizations.

Fundo.

Meucci’s core methods differ from the standard practice insofar as he prefers the use of Profit-and-Loss [P&L] over returns, and he prefers Monte Carlo Simulation over the direct use of historical data. Details of his framework can be found in “The Checklist” ( Meucci (2011) ), however I will provide a brief summary here:

For each instrument, one needs to identify the “invariants”, or the drivers of the changes in price that are “Identically and Independently Distributed” [IID]. For equities, this is usually log-returns. Once the invariants are identified (e. g. log-returns for equities), this yields a matrix of invariants. To reduce estimation error, dimension reduction is performed by computing the eigenvalues and eigenvectors. Then, a subset of eigenvalues/eigenvectors are identified as the “signal”, whereas the remaining ones are classified as “noise”. For the “signal” eigenvalues and eigenvectors, these are converted to their Principal Components [PCs] (i. e. the product of the invariants with the signal-eigenvectors). The distribution for each of the PCs is estimated, as well as the dependence between them (the copula). For the noise components, these are assumed to be distributed with mean 0, and standard deviation equal to the corresponding noise-eigenvalue, and are assumed to be independent/uncorrelated with each other. Next, the Monte Carlo Simulation is conducted by drawing N trials, for P periods, from the meta-distribution estimated in the previous step. The idea is that the optimization should occur at some future investment horizon, and thus prices should be projected to where they likely will be . Finally, these returns need to be converted to P&Ls, thus the distribution of N invariants at the investment horizon is multiplied by the latest security prices to obtain a distribution of P&Ls. Lastly, the portfolio optimization is conducted on the P&Ls, instead of percent returns. Thus, the solution yields holdings (i. e. number of shares for each security) instead of the traditional asset weights.

In order to simplify this process, a number of assumptions were made for this study.

Premissas.

Only equities are considered, whereas Meucci’s framework includes Fixed Income and Derivatives All signal-PCs are assumed to have a skew-student-t distribution All noise-PCs are assumed to either be normally distributed, or have student-t distribution, and are selected based on the Anderson-Darling goodness of fit test Given that there are 9 sector spdr ETFs, it is assumed that there are always 3 signal PCs, and 6 noise PCs The dependence between the 3 signal PCs is assumed to have a Vine Copula ( Kramer and Schepsmeier (2011) ) structure The dependence between the 6 noise PCs is assumed to be uncorrelated within the 6 PCs, as well as uncorrelated with the 3 signal PCs Transaction costs are ignored for rebalancing The optimization is long-only, fully-invested with a max-weight constraint on the 2 highest volatility ETFs For each period, the historical volatility is computed, and for the 2 highest volatility ETFs, their weights are constrained by the inverse-volatility weight calculated for that period All other “non-high-volatility” ETFs can be fully-invested (100% of wealth) For the Auto-Regressive Moving-Average [ARMA] trials, the ARMA model is fit if the Ljung-Box test indicates autocorrelation with 2.5% statistical significance I am aware of criticisms of the Ljung-Box test for autocorrelation Weekly data is used to calculate the invariants, eigenvalues/eigenvector and to ultimately solve for holdings The rationale is that this eliminates some of the noise from the daily prices, yet provides enough data to draw conclusions Performance is evaluated using daily data Covariance matrices are estimated using “robust” methodologies robust::covRob() was used to estimate covariance matrices 3 Windows are selected to evaluate historical horizons 18 months is assumed to be a short-horizon window 36 months is assumed to be a medium-horizon window 60 months is assumed to be a long-horizon window Portfolios are rebalanced monthly, thus the Meucci investment horizon is 4-weeks ahead The invariants are projected 4 periods ahead, and then re-priced and optimized at this 4-period-ahead horizon.

It should also be noted that, I have made many assumptions above. If anyone happened to read my first post on the Low-Volatilty Trading Strategy, then they may wonder why I did not take the hypothesis-driven approach outlined by Peterson (2014) . Work on this post started in Q1 2015, months after I had attended Meucci’s bootcamp. Much of the work, assumptions, and results were obtained throughout 2015. I had taken Brian Peterson’s CFRM561 course in the spring/summer of 2016, after much of the work was completed. Due to the number of assumptions, all of which can arguably be converted to testable hypotheses, this would significantly delay the release of this information. I believe I have some valuable conclusions to share based on the work that has already been completed, and thus decided to write this post. Ideally though, all assumptions I have made above would be tested using the methods outlined in Peterson (2014) , to ensure that the distributional and dependence assumptions are sound, and to reduce the risk of overfitting.

Result Replication.

To replicate the results, two of my packages, titled propfolio and tsconv need to be installed. The following lines of code will install the packages, provided that the devtools library is already installed. Warning: propfolio has a lot of dependencies, so installing it will install many additional packages. Please review the description of the package prior to installing. Code to replicate the results can be found in the appendix.

ARMA Approach.

Throughout 2015, at times during the analysis, I noticed that some of the sectors exhibited autocorrelation (e. g. XLU at times appears to have significant autocorrelation). During the “quest for invariance” step ( Meucci (2011) ), the result should yield no autocorrelation once the invariant has been identified. Given that I observed autocorrelation, this implied that there was information in the return series that was not being captured. In an effort to obtain uncorrelated residuals (invariants), I decided to verify if there is statistically significant autocorrelation using the Ljung-Box test at 2.5% significance. If the null hypothesis was rejected, then I used forecast::auto. arima() to fit an ARMA model. The only time this additional step is conducted is for the 18 month window. It is an extension of the Monte Carlo approach, and technically more accurate as it ensures that the invariants are IID. I did not do this for the 36 month and 60 month windows, since I did test this previously in 2015, and found that using forecast::auto. arima() did not make performance improvements; if anything it negatively impacted performance.

Optimization Criteria, Objectives and Choice of Benchmark.

Objective Function Criteria.

The objective in this study is to maximize risk-adjusted returns, or put anther way, obtain the highest level of returns for the lowest amount of risk. Thus, the Sharpe Ratio will predominantly be used to assess strategy performance, however this will be evaluated in the context of the annualized return and annualized volatility. E. g., if the Sharpe Ratio of strategy 1 is 2.0, yet the annualized return is 3%, and if the Sharpe Ratio of strategy 2 is 1.5, yet the annualized return is 9%, then strategy 2 may be preferred over strategy 1.

The objective can be illustrated with the chart below. Once the portfolio or strategy performance is obtained, and the annualized risk and return is calculated, it can be plotted on a risk-return scatter plot to visually compare strategies. The goal is to select the strategy that is the highest to the top-left; this is the lowest risk and highest return strategy. In practice, there are typically no points in this space, and all points usually tend towards the right (i. e. higher risk). If two points are on the same horizontal line (same return), the strategy on left is the best (the “Good” arrow), since it has the same return but lower risk. If two points are on the same vertical line, the strategy on top (the “Better” arrow) is the best, since it has more return for the same level of risk. Any strategy to the top-left of another point is better (the “Great” arrow) since it has higher return and lower risk. Any strategies that fall on some positive-slope line in relation to one another will need to be evaluated on a case-by-case basis, since if they have the same Sharpe Ratio, then they are roughly “equivalent”, however, if the point is significantly higher, and only slightly to the right, then it is equivalent to the effect in the “Better” line, where a significant amount of return is gained for a small increase in risk.

Visualization of Objectives: Maximizing the Sharpe Ratio.

Choice of Benchmark.

The benchmark for this study will be the equal-weight portfolio of all 9 sector spdrs, rebalanced monthly. It is tempting to select the S&P500 as the benchmark, as it is easier to outperform, however with an equal weight benchmark, not only does it outperform the cap-weighted S&P500 index, it makes no model assumptions and thus is a fair benchmark to be evaluated against.

This outperformance of the equal-weight portfolio over cap-weighted portfolio is clear in the charts and table below, where the equal-weight benchmark exhibits higher annualized returns, lower annualized risk, and better tail characteristics (lower VaR and ETL). The optimizations in this study may outperform the cap-weighted index, however, if they cannot outperform the equal-weight portfolio, then they are not useful, further justifying the use of the equal-weight portfolio as the benchmark.

Cumulative Returns of the S&P 500 (Cap-Weighted) Index vs. Equal-Weighted Sector Portfolio.

Risk-Return Scatter of the S&P 500 (Cap-Weighted) Index vs. Equal-Weighted Sector Portfolio.

Performance Statistics of the S&P 500 (Cap-Weighted) Index vs. Equal-Weighted Sector Portfolio.

(June 2000 – December 2016)

Overall Structure and Process.

First, the Minimum Variance Optimization will be evaluated for each time window, where Meucci’s Monte Carlo approach will be compared to the historical method. The stronger strategy, i. e. Historical vs. Monte Carlo, will be identified for each window length.

Next, the same comparison (Historical vs. Monte Carlo) will be evaluated for the Optimal Sharpe criterion, within each window length.

Lastly, a summary of outperforming strategies relative to the benchmark will be identified, and then it can be assessed whether or not the Monte Carlo [MC] approach adds value over the benchmark and the historical method.

Historical vs. Monte Carlo Performance for Minimum Variance Optimization.

Performance Comparison.

60 Month Window.

In this case, performance of the MC approach vs. the historical approach are virtually indistinguishable from one another. Both strategies exhibit comparable risk and return characteristics. Drawdowns are lower for both MC and historical approaches vs. the benchmark. On the risk-return scatter, we can see that both strategies are in the “Good” scenario, where they appear to exhibit lower risk vs. the benchmark. This is confirmed in the t-test and F-tests below, where the MC and historical methods are statistically indistinguishable, whereas the historical method has statistically significantly lower risk vs. the benchmark.

I do not interpret this to conclude that the MC approach adds no value here. One possible interpretation is that the MC approach confirms that a “global minimum variance” portfolio has been identified, insofar as both strategies select the same sector ETFs with comparable weights across time. However, there is not enough information here to conclude whether or not this is true, or if the MC approach adds no value over the historical method.

Given the choice between the two methods, since the historical approach is more simple, I would likely use this approach, given that it is indistinguishable from the MC approach.

Historical vs. Monte Carlo Cumulative Returns.

Historical vs. Monte Carlo Drawdown.

Historical vs. Monte Carlo Risk-Return Scatter.

Performance Statistics of Historical vs. Monte Carlo Approaches.

(60 Month Window)

(December 2003 – December 2016)

36 Month Window.

Here, we have a very similar result to the 60 month window, except it appears as though the mean return is worse than the benchmark, especially when we look at the risk-return scatter plot below. However, given that the Sharpe Ratio for both the MC and historical approach are comparable to the Sharpe Ratio of the benchmark, and, given that the risk for both strategies is statistically significantly lower (confirmed in the F-test below), if the strategies were to be levered up to the volatility of the benchmark, then comparable returns would be generated. Furthermore, the mean difference for the return distributions of the strategies is not statistically significantly different from that of the benchmark (confirmed with the t-test below), therefore, my interpretation is that this is a similar case to the 60 month window, where the performance of MC and historical vs. the benchmark are comparable, and a statistically significant reduction of risk in the portfolios is obtained.

Again, given the simplicity of the historical approach, I would select the historical method over the MC approach.

Historical vs. Monte Carlo Cumulative Returns.

Historical vs. Monte Carlo Drawdown.

Historical vs. Monte Carlo Risk-Return Scatter.

Performance Statistics of Historical vs. Monte Carlo Approaches.

(36 Month Window)

(December 2001 – December 2016)

18 Month Window.

In this instance, it appears as though the results are indistinguishable from the benchmark, both for the historical and MC approaches. However, upon further inspection of the risk characteristics, what we have is the same return for both the historical and MC approaches for statistically significantly less risk vs. the benchmark (confirmed in the F-test below). Upon inspection of the risk-return scatter plot, the “Good” scenario can be observed, where all 3 strategies (historical, MC and ARMA) exhibit statistically significantly lower risk vs. the benchmark.

Again, since the historical approach is comparable to both the MC and ARMA approaches, given a choice I would select the historical approach due to its simplicity.

Historical vs. Monte Carlo Cumulative Returns.

Historical vs. Monte Carlo Drawdown.

Historical vs. Monte Carlo Risk-Return Scatter.

Performance Statistics of Historical vs. Monte Carlo Approaches.

(18 Month Window)

(June 2000 – December 2016)

Section Results.

In all the window lengths, both the historical method and MC approach exhibited statistically significantly lower risk vs. the benchmark, however between the two strategies, they were virtually indistinguishable.

My interpretation is that, since minimum variance optimization effectively ignores any return inputs, either the MC (and ARMA) approaches are confirming that the historical method is finding the global minimum variance portfolio, or it adds no value. What is needed is return information to verify if adding return information as an input into the optimization enables a distinction in the performance between MC and historical approaches. This will be investigated in the subsequent section.

Historical vs. Monte Carlo Performance for Optimal Sharpe Ratio Optimization.

Performance Comparison.

60 Month Window.

In this particular case, the MC approach is a poor performing strategy, when compared to both the benchmark and the historical method. The historical method manages to maintain the same level of return, with a statistically significantly lower risk, however the MC approach performs very poorly, especially since the t-test comparing the return distribution between both approaches is fairly low at 0.13, indicating that there is a chance that the returns are statistically significantly different.

It appears as though the MC approach at this window length is unable to capture the correct return characteristics, which yields the extremely poor performance vs. both the benchmark and historical method.

Thus, the historical approach is still preferred at the 60 month window length.

Historical vs. Monte Carlo Cumulative Returns.

Historical vs. Monte Carlo Drawdown.

Historical vs. Monte Carlo Risk-Return Scatter.

Performance Statistics of Historical vs. Monte Carlo Approaches.

(60 Month Window)

(December 2003 – December 2016)

36 Month Window.

In this scenario, it almost appears as though similar results are obtained as the minimum variance optimization case, insofar as the returns are lower (yet not statistically significantly different), and the risk is statistically significantly lower, both for the MC and historical approaches vs. the benchmark. However, in the minimum variance case, the Sharpe Ratio for both strategies was comparable to the Sharpe Ratio of the benchmark, yet in this case, it is much lower (0.45 vs. 0.38 and 0.37, benchmark vs. historical and MC, respectively). Thus, theoretically levering the strategies to the volatility of the benchmark would yield portfolios that exhibit comparable returns with higher risk.

Thus, both the MC and historical approaches are poor performers vs. the benchmark in this scenario, and the benchmark should be used over any approach.

What is interesting in this case, is that the MC approach exhibits statistically significantly lower risk vs. the historical approach. This implies that the MC approach is capturing the risk and return characteristics better than the historical approach at this window length, which is a positive characteristic of the MC approach, and can be used in future studies to potentially improve the performance over the benchmark.

Historical vs. Monte Carlo Cumulative Returns.

Historical vs. Monte Carlo Drawdown.

Historical vs. Monte Carlo Risk-Return Scatter.

Performance Statistics of Historical vs. Monte Carlo Approaches.

(36 Month Window)

(December 2001 – December 2016)

18 Month Window.

Here, the MC approach begins to exhibit strong performance over the historical approach, especially when the ARMA method is used. This is clear from the cumulative return chart, where ARMA and MC are the long-run strong performers, as well as the drawdown chart, where the benchmark appears to have the worst drawdowns, and the historical approach appears to have a sluggish post-financial-crisis recovery vs. the MC and ARMA strategies. Upon examining the risk-return scatter, the “Great” scenario can be observed, where the ARMA strategy is to the top-left of the benchmark.

Here, the historical, MC and ARMA approaches all have statistically significantly lower risk vs. the benchmark, however, all of the return distributions are statistically indistinguishable. What is interesting about the ARMA strategy, is that the average drawdown-recovery is lower than that of the historical approach. Lastly, both VaR 100 (i. e. worst loss) are lowest for the MC and ARMA approaches.

My own concern with the ARMA approach, is that I did not test the quality of the signal process, nor did I conduct any tests for overfitting, thus I am cautious with these results. However, it is the more correct approach for Meucci’s methods insofar as it ensures that the invariants are IID.

Therefore, it appears that at lower window lengths, the power of Meucci’s MC approach is quantifiable, even if only the MC approach is used (i. e. not the ARMA approach), insofar as it yields lower risk with a higher Sharpe Ratio vs. both the benchmark and historical method. This is supported when evaluating the 36 month window, where the MC approach exhibited better risk characteristics vs. the historical approach.

Historical vs. Monte Carlo Cumulative Returns.

Historical vs. Monte Carlo Drawdown.

Historical vs. Monte Carlo Risk-Return Scatter.

Performance Statistics of Historical vs. Monte Carlo Approaches.

(18 Month Window)

(June 2000 – December 2016)

Section Results.

It appears as though, at shorter window lengths, the power of the MC approach begins to surface insofar as the the risk of the strategies is statistically significantly lower. In order to assess if the MC approach is truly optimal, a comparison of the strongest strategies across window periods and optimization methods is required to assess if it is truly a superior strategy. This will be the goal in the next section.

Overall Comparison and Identifying the Optimal Strategy.

Upon comparing the results above, the clear performers are:

Historical Minimum Variance (60 Months) Monte Carlo Optimal Sharpe (18 Months) ARMA Optimal Sharpe (18 Months)

Which is summarized in the table below.

The 36 month window is excluded since it could not outperform the benchmark in all tests.

18 Month vs. 60 Month Windows.

In order to identify the true top performer, the results for 18 month window are re-run with the same window length as the 60 month window. Upon inspecting the cumulative returns, it is clear that both the 18 month MC and ARMA approaches exhibit strong returns. The t-test below confirms that the 18 month ARMA Optimal Sharpe has relatively statistically significantly different returns vs. the 60 month Historical Minimum Variance strategy. Both 18 month strategies (ARMA and MC) exhibit statistically significantly lower risk vs. the 60 month Minimum Variance strategy. Thus, based on these results, and the constraints within this study, Meucci’s methods appear to add significant value over historical data, if they are used with relatively shorter time windows (i. e. < 60 months).

Top Performing Strategy Cumulative Returns.

Top Performing Strategy Drawdown.

Top Performing Strategy Risk-Return Scatter.

Performance Statistics of Top Performing Strategies.

(18 Month vs. 60 Month Windows)

(December 2003 – December 2016)

Final Thoughts and Overall Conclusion.

There is one additional consideration that needs attention. I assumed no transaction costs, and given the shorter time horizon of the 18 month strategies, as well as the optimization method (Optimal Sharpe vs. Minimum Variance), when the weights are plotted for each of the strategies, it is clear that the 18 month ARMA and MC strategies are not really portfolio management strategies; they exhibit characteristics of tactical trading strategies due to the aggressively changing portfolio weights.

Thus, if the objective is for stable portfolio weights, then a long horizon window and minimum variance optimization appears to be a better choice under this particular study, due to the relatively stable weights. However, if there is appetite for complexity, and individual transaction costs are low, then there is potential to add more value using Attilio Meucci’s methods.

It may be possible to stabilize the weights under the 18 month scenarios, however it is out of scope for this study, and may be something valuable to investigate at a later date.

Rolling Weights for Minimum Variance Optimal Strategy.

Rolling Weights for Optimal Sharpe Optimal Strategy.

Opportunities for Future Research.

Based on the work in this study, I think there is a lot of opportunity to explore the power of Meucci’s methods. I do think there is a lot of potential for horizons.

Start hypothesis testing from first-principles to reduce the number of assumptions (e. g. signal-PC distribution) Add additional well-thought-out bias to assist with out-of-sample performance Use Cross Validation to evaluate distribution and copula fitting Verify predictive ability of the ARMA process Add concentration constraints to Optimal Sharpe optimization to stabilize asset weights, especially in the 18-month window Identify different regimes (e. g. High vs. Low volatility) and apply different models/hypotheses/assumptions to each regime.

Please contact me if you have any questions/comments/feedback or you have any ideas. I hope you found this interesting and useful.

Appendix: Code to Replicate Results.

Historical strategy code:

Monte Carlo Simulation strategy code.

Monte Carlo with ARMA strategy code:

Calculating results after running one of 3 scenarios above.

Referências.

Kramer, Nicole, and Ulf Schepsmeier. 2011. “Introduction to Vine Copulas.” Technische Universitat Munchen. statistics. ma. tum. de/fileadmin/w00bdb/www/veranstaltungen/Vines. pdf.

Markowitz, Harry. 1952. “Portfolio Selection.” Journal of Finance 7 (1). Wiley for the American Finance Association: 77–91. jstor/stable/2975974.

Meucci, Attilio. 2005. “Risk and Asset Allocation.” Springer Finance Textbooks. arpm. co/book/.

Replicating CRSP Volatility Decile Portfolios in R.

Introdução.

In this post, I provide R code that enables the replication of the Center for Research in Security Prices (CRSP) Volatiliy Deciles using Yahoo! Finance data. This post is related to my last blog post in that it will generate the CRSP low volatility decile portfolio, thereby facilitating the replication of the associated EMA trading strategy.

There are a few caveats to this replication:

There will be survivorship bias since Yahoo! Finance does not contain de-listed stocks This code takes a long time to run (+2 hrs on my machine, excluding downloading the symbols), and so it should not all be executed at once The parallel processing libraries in this code run in linux For Windows, “library(snow)”” should be used instead of “library(parallel)” and “library(doSNOW)” should be used instead of “library(doParallel)” A SOCK cluster will likely need to be used instead of a FORK cluster Consequently, objects, functions and libraries will have to explicitly be exported to the cores, thereby consuming more RAM There are +5,000 securities to download from Yahoo! Finance, and any one of them can fail randomly, thereby yielding different results each time the code is executed It is up to the individual user to decide if they want to download the failed symbols The quality control [QC] for this code is not exhaustive , i. e. my goal was to quickly get data for a time-sensitive paper-replication project, thus there is potential for more QC, which I will indicate in the post.

This code is related to a paper replication project for a course I took over the summer of 2016 (Advanced Trading System Design). I will be sharing the project in a subsequent post.

To run the following script, one of my packages, titled tsconv (which stands for “Time Series Convenience”) needs to be installed. The following line of code will install the package, provided that the devtools library is already installed.

Download Security Tickers.

In order to determine the universe of stocks to include, a list of security tickers that trade on the NYSE and NASDAQ is required. This was located by reading the relevant post by Louis Marascio, and as per the discussion, the list of stock tickers that trade on the NYSE and AMEX was then obtained from nasdaqtrader/trader. aspx? id=symboldirdefs on July 8th, 2016.

The file format is a pipe-delimited text file. It contains preferred shares and ETFs, which should be excluded from the replication. Therefore, the file was imported into Microsoft Excel, and then filtered for tickers that were not ETFs, and where the “ACT Symbol” excluded the “$” character, as these were predominantly preferred shares. Lastly, the exchange needed to be part of the NYSE, or NASDAQ, and the only exchange code listed on NASDAQ which is not a part of these exchanges is “Z”, which is the label for BATS Global Markets ; therefore any stock with an exchange code “Z” was excluded. After these exclusions, 6,157 ticker symbols remained in the file. The symbols from this cleaned list were exported to a Comma-Separated-Value [CSV] file.

Load the Tickers and Download the Data.

Next, the tickers are loaded into the global environment, and then downloaded one by one from Yahoo! Finance to avoid getting blocked by the website. It may be possible to download more symbols at a time, thereby speeding up the process, however I did not test that to ensure I was not cut off. These are stored in a separate environment titled “stockPriceEnv”. I have added code to save and load the downloaded tickers, since it is time-consuming, taking almost 2 hrs on my machine.

The following symbols failed during the download. This is important, since the failed symbols appear to change each time this code is run, which ultimately yields slightly different volatility decile performance. This means that to replicate the numbers in this post, the exact same symbols will need to fail/be present.

Failed Download Symbols.

Controle de qualidade.

Here, there are 2 known issues I have come across while working on this code. One, is that there’s a symbol with the name “TRUE”, which not surprisingly, causes issues. The fix is to rename it to “TRUE.”.

The second issue is that the ticker OHGI appears to have “garbage” data, thus it is excluded completely from the replication.

OHGI Adjusted Closing Prices.

It is possible that there are more anomalies within the data, however, from my perspective, there was a time-sensitive deliverable, and thus given that the numbers were sensible, insofar as they were comparable to results generated by the actual CRSP volatility decile indices, they were used for the replication project. Any additional investigations and QC should be performed at this stage.

This next block of code uses roughly 17 GB of RAM on my machine using a fork cluster with 8 cores, therefore, please ensure that there is adequate memory to run the code. The code checks that each stock has at least 80% of trading days populated within a year (as per CRSP’s requirements), and then calculates the annualized volatility for that stock for each year. Fortunately, the code only takes 1.5 minutes to run using parallel processing.

Volatiliy Decile Replication.

Next, the data is checked for continuity, i. e. that there are no missing years.

The plot below indicates a break before 1973, indicating that there are gaps in the data prior to that year.

Thus, the data is filtered to ensure that the time series begins in 1973.

Next, stocks are assigned to deciles. In order to accomplish this, the code below extracts all volatilities for the stocks for each year…

By summarizing the results, outliers can clearly be observed in the more recent years. This indicates that the high volatility stocks can very likely also benefit from additional QC, however to keep this post brief, no additional QC will be conducted.

Volatility Decile by Year (1995 and onward)

Now that volatility has been assigned to deciles for each year, stocks can be assigned deciles based on their volatilities for each year. This takes roughly 37 minutes on my machine using all 8 cores.

So far, we have lost 212 stocks from a failure to download, and after running the code block above, we have also lost an additional 392 symbols due to NA or NULL values, which ultimately results in a loss of 9.81% of symbols.

Based on the results in the previous block of code, decile portfolios can be constructed for each year. The following code takes roughly 24 minutes to run on my machine (using 8 cores).

The code above generates a matrix of 1/0 flags, where the rows are the years, and the columns are the stocks. Two things now need to happen to proceed. First, the date needs to be converted to an end-of-year date, since CRSP assigns the portfolio weights in the subsequent year (later, PerformanceAnalytics::Return. portfolio() will then assign the weights to the subsequent year). Second, the 1/0 flags need to be converted to weights, so that they sum to 1 for each year (each row).

Some checks are performed to ensure that the weights sum to 1 across all years.

Verification of Weight-Sums Across Deciles and Years.

Here is another check, where the number of stocks for each decile by year is computed. What we should observe, and do ultimately see, is an approximately equal distribution of stocks across all deciles within a year. Or in other words, the number of stocks across each row (year) should nearly all be equal.

Count of Stocks Across Deciles and Years.

We are almost ready to compute the volatility decile portfolios. The column names across all volatility deciles are verified to ensure that they are equal across all years. This will be used to match weights to the stock returns when computing a portfolio.

It returns an empty result, indicating that any one of the list item’s names can be used as a reference.

Next, the “stockNames” variable contents are matched to the stock price names so that matrix multiplication can be performed later (i. e. the order of the weights match the order of the stock returns).

Now, a very large matrix of prices, in the same order as the stock weights, will be created. Ideally, the xts::merge() function would be used. However, since there is a large volume of data, my machine cannot handle it. The way around it, is to loop through all the stocks and join them together, very slowly. The code below runs in sequence (one core) and takes just over 30 minutes on my machine.

So we have a matrix of prices, in the same order as the weights that were calculated. The code below verifies that the order is the same across all decile portfolios.

Returns then need to be calculated from the matrix of prices.

Finally, the decile portfolio returns can be calculated, using the stock returns and weights that were calculated previously.

Below, the cumulative returns are plotted on a log-scale, which facilitates a visual comparison of the decile performance. We can see that the higher volatility deciles exhibit higher cumulative returns, which is what is expected as the volatility increases.

Cumulative Returns of Volatility Decile Portfolios (Log Scale)

Numeric results can be generated using the following line of code.

The Sharpe Ratio declines as the volatility increases, which is consistent with the low-volatility anomaly. However, the peculiar result is that the Sharpe ratio then increases at the high volatility portfolios. One reason for this could be the embedded survivorship bias, since riskier stocks in the high volatility portfolios would be excluded, thereby increasing risk-adjusted returns. However, if we compare the Sharpe-Ratio-trend against actual CRSP data results, seen in the table below, they exhibit the initial decline in Sharpe Ratio, and subsequent rise in Sharpe Ratio for the higher volatility deciles, which conflicts with the low-volatility anomaly results. Overall, replicated volatility decile returns, risk, and tail risk increase as the volatility increases, which is consistent with expectations, and which is observed in the actual CRSP volatility decile performance.

Replicated Volatility Decile Results.

Actual Volatility Decile Results.

Table extracted from:

Han, Yufeng, Ke Yang, and Guofu Zhou. 2013. “A New Anomaly: The Cross-Sectional Profitability of Technical Analysis.” Journal of Financial and Quantitative Analysis 48 (5). Cambridge University Press: 1433–61. dx. doi/10.1017/S0022109013000586.

Conclusão.

This post presents R code which uses free Yahoo! Finance data to replicate the performance of CRSP volatility deciles. Though the replicated portfolios contain survivorship bias, the overall risk and return characteristics match those exhibited by actual CRSP volatility decile portfolios.

An EMA Trading Strategy for a Low Volatility Portfolio.

Introdução.

The process I’m going to follow is based on content from the University of Washington’s CFRM561 course Advanced Trading System Design. “Hypothesis driven development” is the core principle of this course, where each step in the development process involves hypothesizing testable ideas, and verifying these ideas before proceeding to the next stage. As etapas envolvem a identificação de um ou mais indicadores de mercado, testando se os indicadores realmente medem os fenômenos pretendidos do mercado, hipotetizando sinais de entrada e saída baseados no (s) indicador (es) de mercado, confirmando se os sinais têm poder preditivo e definindo entrada e saída regras baseadas nos sinais.

I am personally a fan of low volatility investing, insofar as it provides enhanced risk-adjusted returns vs. traditional cap-weighted market indices. Thus, I have reconstructed the CRSP lowest volatility decile portfolio using data from Yahoo! Finance, and will use this index as the baseline for evaluating the trading strategy. The goal here is to verify if a Exponentially-Weighed Moving Average [EMA] trading strategy can further enhance risk-adjusted returns by either increasing returns without increasing risk, decreasing risk without impacting returns, or both.

This post is a significantly condensed version of the full report. The full-version report, which is the actual assignment I submitted, can be found here. Ele contém todos os detalhes de cada etapa, resultados dos testes de hipóteses, incluindo a intensidade do sinal, bem como as etapas de otimização de parâmetros provisórios.

A primeira questão relacionada ao indicador que precisamos nos perguntar é: "o que achamos que estamos medindo?"

It can be argued that the EMA is measuring the “true de-noised price level”, where the thinking is that the moving average “averages” the noise away, thereby revealing the true level.

It can also be argued that, assuming the EMA is measuring the “true” level, it is also measuring the price trend. The rationale here is that, if there’s a true level revealed, then the trend can be inferred from the time series of “true levels”.

In order to test that EMA is measuring the true level, the correlation between the low-vol index level and EMA indicator is tested for strength and statistical significance. To test the trend, it is compared against a measure of “true” slope by testing for high and statistically significant correlation and cointegration.

Prior to defining any signal, the main prediction that might be possible with an EMA is stating whether or not the future index level will be greater than or less than the current index level, instead of generating a point-forecast. For this tool, confusion matrices are used to measure if the future-index-level-forecasts are correctly classified as higher or lower than current index levels. The statistical significance of the confusion matrices are also verified to ensure that there is some information content.

In terms of the actual signal, the thought behind it is that if the current index level exceeds the EMA level, this will increase the subsequent EMA value (which was verified as a measure of “true” price level), which will ultimately increase the EMA trend. This would then be interpreted as a potential signal that the index level will rise.

The rationale for the converse stands, in that, if the current index level is less than the EMA level, this will decrease the subsequent EMA value (which is a measure of “true” price level), thereby indicating that the index level will potentially decrease.

The rules are simple: if it is likely that the price will increase based on the relationship between indicator and index, (i. e. the index level exceeds the EMA level) then a long position should be initiated. Conversely, if it is likely that the price will decrease based on the same relationship, then any long position(s) should be closed.

Referências e Objetivos da Estratégia.

Objetivos.

The main goal is to verify if the risk-adjusted returns for the low volatility index can be enhanced. Assim, a maximização de uma medida de desempenho ajustado ao risco é o objetivo dessa estratégia, já que a alavancagem sempre pode ser aplicada para aumentar os retornos absolutos.

Given that the objective is to maximize risk-adjusted returns, the following statistics can be used to quantify risk-adjusted returns:

During the trading simulations, PMD will be used to assess risk-adjusted returns as it is more sensitive to the tails than the SR. However, in order to evaluate the viability of the trading strategy, the SR will be used.

Referências.

Results of the EMA trading strategy will be compared against a 100% long version of the low volatility index. This portfolio is the ‘buy-and-hold’ [BH] version of the index, whereas the trading strategy will be referred to as the Moving-Average Portfolio [MAPortfolio]. The BH portfolio is the “no effort” portfolio with which to measure if the trading signals are adding value. Success is not defined on absolute returns; it may be possible to observe absolute lower returns, however exhibit higher risk-adjusted returns.

Signal Strength Results.

The Heidke Skill Score [HSS] is used to quantify signal strength, as it is more unforgiving than the “Probability of Detection”.

HSS for the up signal indicates that the skill level is relatively low for lag days of 15 or greater. Moreover, the skill level is highest for 1 day-forward with a 5 day lag, and drops off relatively rapidly for a 5-day lag when the number of forward days exceed 3 days.

Os HSSs são muito mais altos para o sinal de descida e parecem persistir para 1 & # 8211; 5 forward days, and lag days up to 60 days, assuming that less than 1% skill is not enough to make useful predictions. To clarify, the 60-day-persistence isn’t visible on the contour plot, however numerically the HSSs are more persistent for the down signal vs. the up signal. So far, this implies that the down signal contains more predictive power than the up signal.

All skill scores determined to be “strong” are also statistically significant, both for up and down signals.

Trading Strategy Results.

After testing various lag-lengths exhibiting high HSSs for both up and down signals, as well as performing a parameter optimization, the conclusion detailed in the full report was that it is likely more reliable to set the lag length based only on the HSS strength instead of the backtest results due to poor out-of-sample performance [OOS]. The OOS results, as well as the code, for the high HSS lag lengths (5 days for the up signal and 5 days for the down signal) are displayed below.

One key observation is that the performance during the 2008 financial crisis was relatively spectacular, with a drawdown of only approximately 10%, vs. 40% for the BH index. Durante o início do desempenho do OOS, houve algum desempenho abaixo do esperado, até o crash das pontocom no final de 1999 e início de 2000, onde os rebaixamentos para o portfólio de MA foram mínimos, permitindo que os retornos fossem mantidos.

Os retornos médios não são significativamente diferentes estatisticamente, no entanto, a variância é estatisticamente significativamente menor, resultando no Índice de Sharpe superior de 2,43, contra 1,14 para o Índice BH. Unfortunately, the trading strategy does not exhibit positive skew, whereas, surprisingly, the BH Index exhibits positive skew. However, excess kurtosis, though high for both portfolios, is significantly higher for the BH index. It appears as though the trading strategy is significantly reducing the impact of negative market events, allowing the preservation of accumulated returns, with the drawback of potentially missing out on any upside. Given that the objective is to enhance risk-adjusted returns, the biased parameter combination appears to successfully meet this criteria. One additional factor is the transaction costs, which were not considered in this study. Estes precisariam ser levados em conta nos backtests para verificar se os retornos ajustados ao risco são mantidos.

Corretor de opções binárias São Vicente

Tuesday 1 May 2018

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