Krieger, Kevin and Fodor, Andy and Mauck, Nathan and Stevenson, Greg (2012): Predicting Extreme Returns and Portfolio Management Implications.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Predicting Extreme Returns and Portfolio Management Implications|
|Keywords:||Implied volatility, portfolio management|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions
G - Financial Economics > G0 - General > G00 - General
|Depositing User:||Nathan Mauck|
|Date Deposited:||05 Jul 2012 11:21|
|Last Modified:||12 Sep 2016 17:04|
Ang, A., R.J. Hodrick, Y. Xing, X. Zhang, 2006. The cross-section of volatility and expected returns, Journal of Finance 61, 259-299. Ang, A., R.J. Hodrick, Y. Xing, X. Zhang, 2009. High idiosyncratic volatility and low returns: International and further U.S. evidence, Journal of Financial Economics 91, 1-23. Beneish, M. D., C.M.C. Lee, R.L. Tarpley. 2001. Contextual fundamental analysis in the prediction of extreme returns, Review of Accounting Studies 6, 165-189. Blitz, D., P. Van Vilet, 2007. The volatility effect: Lower risk without lower return, Journal of Portfolio Management 34, 102-113. Brockman, P. H. J. Turtle, 2003. A barrier option framework for corporate security valuation, Journal of Financial Economics 67, 511-529. Campbell, J., S. Grossman, J. Wang, 1993. Trading volume and serial correlation in stock returns, The Quarterly Journal of Economics 108, 905–939. Christensen, B.J., N.R. Prabhala, 1998. The relation between implied and realized volatility, Journal of Financial Economics 50, 125–150. Dichev, I.D. 2002. Is the risk of bankruptcy a systematic risk?, Journal of Finance 53, 1131-1147. Dong, I. Duan, C. M.J. Jang, 2003. Predicting extreme stock performance more accurately. Working Paper, Harvard University. Fama, E. F., French, K. R., 1992. The cross section of expected stock returns, The Journal of Finance 46, 427-466. Fama, E. F., K. R. French, 1993. Common risk factors in the returns on stocks and Bonds, Journal of Financial Economics 25, 23-49. Hodrick, R. J., 1992. Dividend Yields and Expected Stock Returns: Alternative Procedures for Inference and Measurement, Review of Financial Studies 5, 357–386. Jegadeesh, N., S. Titman, 1993. Returns to buying winners and selling losers: implications for stock market efficiency, Journal of Finance 48, 65-91. Lev, B., S. R. Thiagarajan, 1993. Fundamental information analysis, Journal of Accounting Research 31, 190-215. Li, K., 2002. Long memory versus option-implied volatility predictions, Journal of Derivatives 9, 9–25. Miller, E.M., 1977. Risk, uncertainty, and divergence of opinion, The Journal of Finance 32, 1151-1168. Poon, S.H., C. Granger, 2003. Forecasting volatility in financial markets: a review, Journal of Economic Literature 41, 478–539. Reinganum, M. R., 1988. The Anatomy of a Stock Market Winner, Financial Analysts Journal March/April, 1 Sloan, R. G., 1996. Do stock prices fully reflect information in accruals and cash flows about future earnings?, The Accounting Review 71, 289-315.