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Combination Return Forecasts and Portfolio Allocation with the Cross-Section of Book-to-Market Ratios
[Illiquidity and stock returns: cross-section and time-series effects]

Author

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  • Andrew Detzel
  • Jack Strauss

Abstract

In this paper, we forecast industry returns out-of-sample using the cross-section of book-to-market (BM) ratios and investigate whether investors can exploit this predictability in portfolio allocation. Cash-flow and return forecasting regressions show that cross-industry BM ratios contain significant predictive information beyond aggregate and industry-specific BM ratios. Forecast combination methods based on industry BM ratios generate significant out-of-sample predictability for many industries. Real-time portfolio-rotation strategies that buy industries with high predicted returns and short industries with low predicted returns based on combination forecasts earn significant alpha with respect to standard asset pricing models net of transaction costs.

Suggested Citation

  • Andrew Detzel & Jack Strauss, 2018. "Combination Return Forecasts and Portfolio Allocation with the Cross-Section of Book-to-Market Ratios [Illiquidity and stock returns: cross-section and time-series effects]," Review of Finance, European Finance Association, vol. 22(5), pages 1949-1973.
    • Handle: RePEc:oup:revfin:v:22:y:2018:i:5:p:1949-1973.
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    as
    1. Tuomo Vuolteenaho, 2002. "What Drives Firm‐Level Stock Returns?," Journal of Finance, American Finance Association, vol. 57(1), pages 233-264, February.
    2. Lewellen, Jonathan, 2015. "The Cross-section of Expected Stock Returns," Critical Finance Review, now publishers, vol. 4(1), pages 1-44, June.
    3. Kothari, S. P. & Shanken, Jay, 1997. "Book-to-market, dividend yield, and expected market returns: A time-series analysis," Journal of Financial Economics, Elsevier, vol. 44(2), pages 169-203, May.
    4. Chakravarty, Sugato & Panchapagesan, Venkatesh & Wood, Robert A., 2005. "Did decimalization hurt institutional investors?," Journal of Financial Markets, Elsevier, vol. 8(4), pages 400-420, November.
    5. Lewellen, Jonathan & Nagel, Stefan & Shanken, Jay, 2010. "A skeptical appraisal of asset pricing tests," Journal of Financial Economics, Elsevier, vol. 96(2), pages 175-194, May.
    6. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," The Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    7. Benjamin Golez, 2014. "Expected Returns and Dividend Growth Rates Implied by Derivative Markets," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 790-822.
    8. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    9. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    10. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    11. Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
    12. Kewei Hou & Chen Xue & Lu Zhang, 2015. "Editor's Choice Digesting Anomalies: An Investment Approach," The Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 650-705.
    13. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    14. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    15. Garratt A. & Lee K. & Pesaran M.H. & Shin Y., 2003. "Forecast Uncertainties in Macroeconomic Modeling: An Application to the U.K. Economy," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 829-838, January.
    16. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    17. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    18. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    19. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    20. Stoll, Hans R. & Whaley, Robert E., 1983. "Transaction costs and the small firm effect," Journal of Financial Economics, Elsevier, vol. 12(1), pages 57-79, June.
    21. Wayne E. Ferson & Sergei Sarkissian & Timothy T. Simin, 2003. "Spurious Regressions in Financial Economics?," Journal of Finance, American Finance Association, vol. 58(4), pages 1393-1413, August.
    22. Timmermann, Allan, 2008. "Reply to the discussion of Elusive Return Predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 29-30.
    23. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    24. Hirshleifer, David & Hou, Kewei & Teoh, Siew Hong, 2009. "Accruals, cash flows, and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 91(3), pages 389-406, March.
    25. JULES H. Van BINSBERGEN & RALPH S. J. KOIJEN, 2010. "Predictive Regressions: A Present‐Value Approach," Journal of Finance, American Finance Association, vol. 65(4), pages 1439-1471, August.
    26. repec:bla:jfinan:v:58:y:2003:i:4:p:1393-1414 is not listed on IDEAS
    27. Bhushan, Ravi, 1989. "Firm characteristics and analyst following," Journal of Accounting and Economics, Elsevier, vol. 11(2-3), pages 255-274, July.
    28. Chordia, Tarun & Subrahmanyam, Avanidhar & Tong, Qing, 2014. "Have capital market anomalies attenuated in the recent era of high liquidity and trading activity?," Journal of Accounting and Economics, Elsevier, vol. 58(1), pages 41-58.
    29. Robert Novy-Marx & Mihail Velikov, 2016. "A Taxonomy of Anomalies and Their Trading Costs," The Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 104-147.
    30. 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.
    31. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    32. Joel Hasbrouck, 2009. "Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data," Journal of Finance, American Finance Association, vol. 64(3), pages 1445-1477, June.
    33. Eugene F. Fama & Kenneth R. French, 2016. "Dissecting Anomalies with a Five-Factor Model," The Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 69-103.
    34. Pontiff, Jeffrey & Schall, Lawrence D., 1998. "Book-to-market ratios as predictors of market returns," Journal of Financial Economics, Elsevier, vol. 49(2), pages 141-160, August.
    35. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    36. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
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