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Portfolio optimization using local linear regression ensembles in RapidMiner

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  • Gabor Nagy
  • Gergo Barta
  • Tamas Henk

Abstract

In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfolio selection strategies that optimize the growth rate of the capital. We investigate the effect of algorithm parameter m: the number of selected stocks on achieved average annual yields. Results suggest the algorithm's practical usefulness in everyday trading.

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  • Gabor Nagy & Gergo Barta & Tamas Henk, 2015. "Portfolio optimization using local linear regression ensembles in RapidMiner," Papers 1506.08690, arXiv.org.
    • Handle: RePEc:arx:papers:1506.08690
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    1. Vijay Desai & Rakesh Bharati, 1998. "A comparison of linear regression and neural network methods for predicting excess returns on large stocks," Annals of Operations Research, Springer, vol. 78(0), pages 127-163, January.
    2. László Györfi & András Urbán & István Vajda, 2007. "Kernel-Based Semi-Log-Optimal Empirical Portfolio Selection Strategies," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 10(03), pages 505-516.
    3. Härdle,Wolfgang, 1992. "Applied Nonparametric Regression," Cambridge Books, Cambridge University Press, number 9780521429504.
    4. Elias Masry, 1996. "Multivariate Local Polynomial Regression For Time Series:Uniform Strong Consistency And Rates," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(6), pages 571-599, November.
    5. Thomas M. Cover, 1991. "Universal Portfolios," Mathematical Finance, Wiley Blackwell, vol. 1(1), pages 1-29, January.
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