Predicting the direction of US stock markets using industry returns

Pönkä, Harri (2014): Predicting the direction of US stock markets using industry returns.

Abstract

In this paper, we examine the directional predictability of 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. 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 a number of industry portfolios contain information that can be used to improve investment returns.

• Item Type: MPRA Paper
• Language: English
• Keywords: industry excess return, sign prediction, probit model, forecasting
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

References

[1] Anatolyev, S., Gospodinov, N., 2010. Modeling financial return dynamics via decomposition. J. Bus. Econ. Stat. 28, 232-245.

[2] Cenesizoglu, T., Timmermann, A., 2012. Do return prediction models add economic value. J. Bank. Financ. 36, 2974-2987.

[3] Christoffesen, P.F., Diebold F.X., 2006. Financial asset returns, direction-of-change forecasting, and volatility dynamics. Manag. Sci. 52, 1273-1287.

[4] Cole, R., Moshirian, F., Wu Q., 2008. Bank stock returns and economic growth. J. Bank. Financ. 32, 995-1007.

[5] Estrella, A., 1998. A new measure of fit for equations with dichotomous dependent variables. J. Bus. Econ. Stat. 16, 198-205.

[6] Fama, E., French K., 1989. Business Conditions and expected returns on stocks and bonds. J. Financ. Econ. 25, 23-49.

[7] French, K., Schwert, G.W., Stambaugh R., 1987. Expected stock returns and volatility. J. Financ. Econ. 19, 3-29.

[8] Goyal, A., Welch I., 2008. A comprehensive look at the empirical performance of equity premium prediction Rev. Financ. Stud. 21, 1455-1508.

[9] Granger, C.W.J., Pesaran M.H., 2000. Economic and statistical measures of forecast accuracy. J. Forec. 19, 537-600.

[10] Hansen, P.R., Timmermann A., 2012. Choice of sample split in out-of-sample forecast evaluation. European University Institute Working Paper ECO 2010/10.

[11] Hong, H., Stein J., 1999. A unified theory of underreaction, momentum trading, and overreaction in asset markets. The J. Financ. 54, 2143-2184.

[12] Hong, H., Torous, W., Valkanov, R., 2007. Do industries lead stock markets? J. Financ. Econ. 83, 367-396.

[13] Kauppi, H., Saikkonen P., 2008. Predicting U.S. recessions with dynamic binary response models. Rev. Econ. Stat. 90, 777-791.

[14] Kong, A., Rapach, D., Strauss, J., Zhou, G., 2011. Predicting market components out of sample: asset allocation implications. J. Portf. Manag. 37:4, 29-41.

[15] Lamont, O., 2001. Economic tracking portfolios. J. Econom. 105, 161-184.

[16] Leitch, G., Tanner E., 1991. Economic forecast evaluation: Profits versus conventional error measures. Am. Econ. Rev. 81, 580-590.

[17] Leung M.T., Daouk, H., Chen, A-S., 2000. Forecasting stock indices: a comparison of classification and level estimation models. Int. J. Forec. 16, 173-190.

[18] Menzly, L., Ozbas O., 2010. Market segmentation and cross-predictability of returns. J. Financ. 65, 1555-1580.

[19] Moskowitz, T., Grinblatt M., 1999. Do industries explain momentum? J. Financ. 54, 1249-1290.

[20] Ng, E.C.Y., 2012. Forecasting US recessions with various risk factors and dynamic probit models. J. Macroecon. 34, 112-125.

[21] Nyberg, H., 2010. Dynamic probit models and financial variables in recession forecasting. J. Forec. 29, 215-230.

[22] Nyberg, H., 2011. Forecasting the direction of the US stock market with dynamic binary probit models. Int. J. Forec. 27, 561-578.

[23] Nyberg, H., 2013. Predicting bear and bull stock markets with dynamic binary time series models. J. Bank. Financ. 37, 3351-3363.

[24] Pesaran, M.H., Timmermann A., 1992. A simple nonparametric test of predictive performance. J. Bus. Econ. Stat. 10, 461-465.

[25] Pesaran, M.H., Timmermann A., 2009. Testing dependence among serially correlated multi-category variables. J. Am. Stat. Assoc. 485, 325-337.

[26] Rapach, D., Zhou G., 2013. Forecasting stock returns., in: Elliott, G., Timmermann A. (Eds.), Handbook of Economic Forecasting, Volume 2A. Elsevier, Amsterdam, pp.328-383.

[27] Rydberg, T., Shephard, N., 2003. Dynamics of trade-by-trade price movements: decomposition and models. J. Financ. Econom. 1, 2-25.

[28] Sharpe, W.F., 1966. Mutual fund performance. J. Bus. 39, 119-138.

[29] Sharpe, W.F., 1994. The Sharpe ratio. J. Portf. Manag. 21, 49-58.

[30] Timmermann, A., 2008. Elusive return predictability. Int. J. Forec. 24, 1-18.