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Equity style timing using support vector regressions

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  • Georgi Nalbantov
  • Rob Bauer
  • Ida Sprinkhuizen-Kuyper
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    Abstract

    The disappointing performance of value and small cap strategies shows that style consistency may not provide the long-term benefits often assumed in the literature. In this study it is examined whether the short-term variation in the US size and value premium is predictable. Style-timing strategies are documented based on technical and (macro-) economic predictors using a recently developed artificial intelligence tool called Support Vector Regressions (SVR). SVR are known for their ability to tackle the standard problem of overfitting, especially in multivariate settings. The findings indicate that both premiums are predictable under fair levels of transaction costs and various forecasting horizons.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/09603100500426556
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 16 (2006)
    Issue (Month): 15 ()
    Pages: 1095-1111

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    Handle: RePEc:taf:apfiec:v:16:y:2006:i:15:p:1095-1111

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    1. T. C. Mills & J. V. Jordanov, 2003. "The size effect and the random walk hypothesis: evidence from the London Stock Exchange using Markov Chains," Applied Financial Economics, Taylor & Francis Journals, vol. 13(11), pages 807-815.
    2. André Lucas & Ronald van Dijk & Teun Kloek, 2001. "Stock Selection, Style Rotation, and Risk," Tinbergen Institute Discussion Papers 01-021/2, Tinbergen Institute.
    3. 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.
    4. Pesaran, M Hashem & Timmermann, Allan, 1995. " Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-28, September.
    5. Johnathan Mun & Richard Kish & Geraldo Vasconcellos, 2001. "The contrarian investment strategy: additional evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 11(6), pages 619-640.
    6. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-28.
    7. Said Elfakhani, 2000. "Short positions, size effect, and the liquidity hypothesis: implications for stock performance," Applied Financial Economics, Taylor & Francis Journals, vol. 10(1), pages 105-116.
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