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Predicting Extreme Returns and Portfolio Management Implications

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  • Krieger, Kevin
  • Fodor, Andy
  • Mauck, Nathan
  • Stevenson, Greg

Abstract

We consider which readily observable characteristics of individual stocks (e.g., option implied volatility, accounting data, analyst data) may be used to forecast subsequent extreme price movements. We are the first to explicitly consider the predictive influence of option implied volatility in such a framework, which we unsurprisingly find to be an important indicator of future extreme price movements. However, after controlling for implied volatility levels, other factors, particularly firm age and size, still have additional predictive power of extreme future returns. Furthermore, excluding predicted extreme return stocks leads to a portfolio that has lower risk (standard deviation of returns) without sacrificing performance.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 39845.

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Date of creation: 14 May 2012
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Handle: RePEc:pra:mprapa:39845

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Keywords: Implied volatility; portfolio management;

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  1. Miller, Edward M, 1977. "Risk, Uncertainty, and Divergence of Opinion," Journal of Finance, American Finance Association, American Finance Association, vol. 32(4), pages 1151-68, September.
  2. John Y. Campbell & Sanford J. Grossman & Jiang Wang, 1992. "Trading Volume and Serial Correlation in Stock Returns," NBER Working Papers 4193, National Bureau of Economic Research, Inc.
  3. Fama, Eugene F & French, Kenneth R, 1992. " The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, American Finance Association, vol. 47(2), pages 427-65, June.
  4. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  5. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, American Finance Association, vol. 48(1), pages 65-91, March.
  6. Blitz, D.C. & van Vliet, P., 2007. "The Volatility Effect: Lower Risk without Lower Return," ERIM Report Series Research in Management, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasm ERS-2007-044-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  7. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross-Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, American Finance Association, vol. 61(1), pages 259-299, 02.
  8. Hodrick, Robert J, 1992. "Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 5(3), pages 357-86.
  9. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 50(2), pages 125-150, November.
  10. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, Elsevier, vol. 33(1), pages 3-56, February.
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