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Application of Ensemble Learning for Views Generation in Meucci Portfolio Optimization Framework

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  • Didenko, Alexander
  • Demicheva, Svetlana

Abstract

Modern Portfolio Theory assumes that decisions are made by individual agents. In reality most investors are involved in group decision-making. In this research we propose to realize group decision-making process by application of Ensemble Learning algorithm, in particular Random Forest. Predicting accurate asset returns is very important in the process of asset allocation. Most models are based on weak predictors. Ensemble Learning algorithms could significantly improve prediction of weak learners by combining them into one model, which will have superiority in performance. We combine technical fundamental and sentiment analysis in order to generate views on different asset classes. Purpose of the research is to build the model for Meucci Portfolio Optimization under views generated by Random Forest Ensemble Learning algorithm. The model was backtested by comparing with results obtained from other portfolio optimization frameworks.

Suggested Citation

  • Didenko, Alexander & Demicheva, Svetlana, 2013. "Application of Ensemble Learning for Views Generation in Meucci Portfolio Optimization Framework," MPRA Paper 59348, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:59348
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    References listed on IDEAS

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    1. Roll, Richard & Ross, Stephen A, 1980. "An Empirical Investigation of the Arbitrage Pricing Theory," Journal of Finance, American Finance Association, vol. 35(5), pages 1073-1103, December.
    2. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
    3. Wing-Keung Wong & Meher Manzur & Boon-Kiat Chew, 2003. "How rewarding is technical analysis? Evidence from Singapore stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 13(7), pages 543-551.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. Tanaka-Yamawaki, Mieko & Tokuoka, Seiji, 2007. "Adaptive use of technical indicators for the prediction of intra-day stock prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 125-133.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. Chauvet, Marcelle & Piger, Jeremy, 2008. "A Comparison of the Real-Time Performance of Business Cycle Dating Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 42-49, January.
    8. Ki-Yeol Kwon & Richard Kish, 2002. "Technical trading strategies and return predictability: NYSE," Applied Financial Economics, Taylor & Francis Journals, vol. 12(9), pages 639-653.
    9. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "The crude oil market and the gold market: Evidence for cointegration, causality and price discovery," Resources Policy, Elsevier, vol. 35(3), pages 168-177, September.
    10. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    11. Ling, David C & Naranjo, Andy, 1997. "Economic Risk Factors and Commercial Real Estate Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 283-307, May.
    12. Solnik, B H, 1974. "The International Pricing of Risk: An Empirical Investigation of the World Capital Market Structure," Journal of Finance, American Finance Association, vol. 29(2), pages 365-378, May.
    13. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 25(2), pages 65-86.
    14. Lance J. Bachmeier & James M. Griffin, 2006. "Testing for Market Integration: Crude Oil, Coal, and Natural Gas," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 55-72.
    15. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    16. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    17. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    18. Weiner, R.J., 1991. "Is the World Oil Market "One Great Pool?"," Papers 9120, Laval - Recherche en Energie.
    19. Robert J. Weiner, 1991. "Is the World Oil Market "One Great Pool"?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 95-108.
    20. Mei, Jianping & Liu, Crocker H, 1994. "The Predictability of Real Estate Returns and Market Timing," The Journal of Real Estate Finance and Economics, Springer, vol. 8(2), pages 115-135, March.
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    More about this item

    Keywords

    Random Forest; Ensemble Learning; Meucci portfolio optimization; fundamental analysis; technical analysis;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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