Abstract
In this paper, we examine the directional predictability of US excess stock market returns by lagged excess returns from industry portfolios and a number of other commonly used variables by means of dynamic probit models. We focus on the directional component of the market returns because, for investment purposes, forecasting the direction of return correctly is presumably more relevant than the accuracy of point forecasts. Our findings suggest that only a small number of industries have predictive power for market returns, meaning that we find little evidence of stock markets reacting with a delay to information contained in industry returns. We also find that the binary response models outperform conventional predictive regressions in forecasting the direction of the market return. Finally, we test trading strategies and find that some of the industry portfolios do contain information that can be used to improve investment returns.
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Notes
In Sect. 7 we also study the robustness of our findings using daily frequency data. This data is also obtained from the Kenneth French’s data library.
All the other results are available upon request.
Hong et al. (2007) have revised their results to cover the period 1946–2013 in a recent note (2014), available on Rossen Valkanov’s website: http://rady.ucsd.edu/docs/faculty/valkanov/Note_10282014?pdf=Note_10282014. They also report that with a longer sample, fewer industries seem to lead the stock market.
These findings are available upon request.
Findings available by request.
The adjusted pseudo-\(R^2\) receives values that are lower than those of the unadjusted measure, since it includes a term that penalizes for each extra predictor included.
Daily data for the industry REIT were not available, which reduced the number of industries to 33. The nine industries with statistically significant coefficients included MINES, OIL, FOOD, APPRL, PTRLM, METAL, ELCTR, INSTR, and RTAIL. Findings for dynamic probit models (7)–(9) using daily data were similar to the ones obtained using the static probit models, as illustrated in Table 9 for the METAL portfolio.
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Acknowledgments
The author would like to thank Heikki Kauppi, Helinä Laakkonen, Markku Lanne, Matthijs Lof, Henri Nyberg, participants of FDPE Econometrics Workshops I&II 2013 (Helsinki, May and Dec 2013), the GSF and FDPE Winter Research Workshop in Finance (Turku, Nov 2013), and FindEcon’2014 (Lodz, May 2014), the editors, and two anonymous referees for useful comments on the paper. Financial support from the Yrjö Jahnsson Foundation, the Foundation for the Advancement of Finnish Security Markets, and the Research Funds of the University of Helsinki is also gratefully acknowledged.
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Pönkä, H. Predicting the direction of US stock markets using industry returns. Empir Econ 52, 1451–1480 (2017). https://doi.org/10.1007/s00181-016-1098-0
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DOI: https://doi.org/10.1007/s00181-016-1098-0