IDEAS home Printed from https://ideas.repec.org/a/scn/031730/14453089.html
   My bibliography  Save this article

Application of Ensemble Learning for views generation in Meucci portfolio optimization framework

Author

Listed:
  • Didenko Alexander

    (Financial University, Moscow)

  • Demicheva Svetlana

    (Financial University, Moscow)

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, whichwill 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," Review of Business and Economics Studies, CyberLeninka;Федеральное государственное образовательное бюджетное учреждение высшего профессионального образования «Финансовый университет при Правительстве Российской Федерации» (Финансовый университет), issue 1, pages 100-110.
    • Handle: RePEc:scn:031730:14453089
    as

    Download full text from publisher

    File URL: http://cyberleninka.ru/article/n/application-of-ensemble-learning-for-views-generation-in-meucci-portfolio-optimization-framework
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Weiner, R.J., 1991. "Is the World Oil Market "One Great Pool?"," Papers 9120, Laval - Recherche en Energie.
    10. 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.
    11. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-887, September.
    19. 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.
    20. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fernando Rubio, 2005. "Eficiencia De Mercado, Administracion De Carteras De Fondos Y Behavioural Finance," Finance 0503028, University Library of Munich, Germany, revised 23 Jul 2005.
    2. Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), 2008. "Handbook on Information Technology in Finance," International Handbooks on Information Systems, Springer, number 978-3-540-49487-4, November.
    3. Gerritsen, Dirk F., 2016. "Are chartists artists? The determinants and profitability of recommendations based on technical analysis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 179-196.
    4. Sangwon Suh & Wonho Song & Bong-Soo Lee, 2014. "A new method for forming asset pricing factors from firm characteristics," Applied Economics, Taylor & Francis Journals, vol. 46(28), pages 3463-3482, October.
    5. Achim BACKHAUS & Aliya ZHAKANOVA ISIKSAL, 2016. "The Impact of Momentum Factors on Multi Asset Portfolio," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 146-169, December.
    6. Attiya Yasmeen Javid, 2000. "Alternative Capital Asset Pricing Models: A Review of Theory and Evidence," PIDE Research Report 2000:3, Pakistan Institute of Development Economics.
    7. Ikhlaas Gurrib & Mohammad Nourani & Rajesh Kumar Bhaskaran, 2022. "Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-27, December.
    8. Ikhlaas Gurrib & Firuz Kamalov & Elgilani Elshareif, 2021. "Can the Leading US Energy Stock Prices be Predicted using the Ichimoku Cloud?," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 41-51.
    9. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    10. Alexandros Milionis & Evangelia Papanagiotou, 2009. "A study of the predictive performance of the moving average trading rule as applied to NYSE, the Athens Stock Exchange and the Vienna Stock Exchange: sensitivity analysis and implications for weak-for," Applied Financial Economics, Taylor & Francis Journals, vol. 19(14), pages 1171-1186.
    11. John Y. Campbell, 2000. "Asset Pricing at the Millennium," Journal of Finance, American Finance Association, vol. 55(4), pages 1515-1567, August.
    12. Sonntag, Dominik, 2018. "Die Theorie der fairen geometrischen Rendite [The Theory of Fair Geometric Returns]," MPRA Paper 87082, University Library of Munich, Germany.
    13. Keith S. K. Lam & Liang Dong & Bo Yu, 2019. "Value Premium and Technical Analysis: Evidence from the China Stock Market," Economies, MDPI, vol. 7(3), pages 1-21, September.
    14. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.
    15. Zura Kakushadze, 2015. "Heterotic Risk Models," Papers 1508.04883, arXiv.org, revised Jan 2016.
    16. Cantillo, Andres, 2013. "Survey of Literature on Portfolio Theory," MPRA Paper 49772, University Library of Munich, Germany.
    17. Zura Kakushadze & Willie Yu, 2016. "Statistical Risk Models," Papers 1602.08070, arXiv.org, revised Jan 2017.
    18. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    19. Ikhlaas Gurrib, 2022. "Technical Analysis, Energy Cryptos and Energy Equity Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 249-267, March.
    20. Coleman, Les, 2014. "Why finance theory fails to survive contact with the real world: A fund manager perspective," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 25(3), pages 226-236.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:scn:031730:14453089. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CyberLeninka (email available below). General contact details of provider: http://cyberleninka.ru/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.