Summary:
The question of why different assets deliver different returns is a fundamental problem in finance. In this regard, the literature has mainly focused on the relationship between the profitability and subsequent stock returns of firms. Profitability is also an important factor in the newly proposed asset pricing models. Furthermore, the empirical research on asset pricing has shown that a large number of firm characteristics can be used to forecast a cross-section of stock returns. However, because some of these factors have lost their predictability after being identified in academic papers or learned by the market, the question of how to extract the commonalty of the predictors and aggregate the effective information has become a key issue in empirical finance. Different from the literature, which explores the predictability of individual profitability-related proxies, in this paper, we aggregate a composite profitability measure of Chinese firms from a set of individual profitability-related indicators. We then investigate the relation between a firm's composite profitability and stock returns in the Chinese stock market. Specifically, we use the partial least squares (PLS) and forecast combination (FC) methods to aggregate a composite profitability measure from 12 individual profitability related proxies. Composite profitability provides a comprehensive measure of a firm's profitability, and may provide the basis for a new asset pricing model. We obtain data from the China Stock Market and Accounting Research (CSMAR) database from 2000 to December 2017, including accounting data, monthly stock returns, Fama-French (1993, 2015) common factors, and the Chinese risk-free rate. We find that firms with high composite profitability always have high future stock returns. Using the single factor PLS method and taking the 12-month average slopes is the most efficient way to aggregate the composite profitability. The long-short portfolio generates 15% average annualized returns, with a Sharpe ratio of 0.75. In comparison, using the FC method to calculate the composite profitability generates lower subsequent stock returns. The main objective of PLS is to extract a common factor from a set of predictors that has the highest covariance with the predicted variable, which is a “disciplined” dimension reduction technique. The FC approach averages the univariate predictive regression values of firms' profitability equally, but it ignores the multivariate information structure and interaction between firms' profitability. Hence, the PLS approach is more effective in aggregating information for cross-sectional analyses, and makes more accurate future return predictions. We use different asset pricing models to calculate the abnormal returns generated by the composite profitability, including the Fama-French five-factor model. The results show that when using the PLS single factor model, the abnormal returns of the monthly long-short portfolios are 1.27% (t=3.07), 1.50% (t=3.16), and 1.22% (t=2.94) based on the capital asset pricing model, and the Fama-French three-factor and five-factor models. After controlling for other firm characteristics and risks, such as firm size, book-to-market ratio, and reversal, the positive relation between composite profitability and stock returns is still significant and robust. We then investigate why firms with a high composite profitability have higher future stock returns. The results indicate that the composite profitability premium is stronger among firms with low investment friction, which is consistent with the implications of the investment-based q-theory asset pricing models. However, the premium is not stronger among firms with high mispricing, which contradicts the behavioral mispricing explanations. Our results differ from the findings on the U.S. market, which suggests that investors in the Chinese market also have to focus on the rational expectation-based model. Our findings also indicate that the research on the international markets cannot adequately explain what happens in the Chinese market. Furthermore, reducing the investment friction helps the market to value its composite profitability more precisely. Future studies should focus on other aggregated information on firm characteristics, such as the investment and trading frictions. Moreover, the economic links and information structure of these factors should also be explored to understand the uniqueness of the Chinese stock market.
[1]高春亭和周孝华,2016,《公司盈利、投资与资产定价:基于中国股市的实证》,《管理工程学报》第4期,第25~33页。 [2]高秋明、胡聪慧和燕翔,2014,《中国A股市场动量效应的特征和形成机理研究》,《财经研究》第2期,第97~107页。 [3]姜富伟、涂俊、Rapach D.、Strauss J.和周国富,2011,《中国股票市场可预测性的实证研究》,《金融研究》第9期,第107~121页。 [4]李志冰、杨光艺、冯永昌和景亮,2017,《Fama-French五因子模型在中国股票市场的实证检验》,《金融研究》第6期,第191~206页。 [5]张信东和李建莹,2018,《盈利因子与投资因子具有定价能力吗？——来自中国股市的实证》,《金融与经济》第2期,第10~18页。 [6]赵胜民、闫红蕾和张凯,2016,《Fama-French五因子模型比三因子模型更胜一筹吗——来自中国A股市场的经验数据》,《南开经济研究》第2期,第41~59页。 [7]Allen, F., Qian, J., Shan, C., and Zhu, J. 2018. “Dissecting the Long-term Performance of the Chinese Stock Market,” SSRN working paper. [8]Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross-section and Time-series Effects,” Journal of Financial Markets, 5(1): 31~56. [9]Asness, C. S., Frazzini, A., and Pedersen, L. H. 2017. “Quality Minus Junk,” SSRN working paper. [10]Balakrishnan, K., Bartov, E., and Faurel, L. 2010. “Post loss/profit Announcement Drift,” Journal of Accounting and Economics, 50(1): 20~41. [11]Banz, R. W. 1981. “The Relationship between Return and Market Value of Common Stocks,” Journal of Financial Economics, 9(1): 3~18. [12]Carpenter, J. N., Lu, F., and Whitelaw, R. F. 2015. “The Real Value of China's Stock Market,” National Bureau of Economic Research working paper. [13]Chandrashekar, S., and Rao, R. K. 2009. “The Productivity of Corporate Cash Holdings and the Cross-section of Expected Stock Returns,” McCombs Research Paper Series, No. FIN-03~09. [14]Fama, E. F., and French, K. R. 1993. “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics, 33(1): 3~56. [15]Fama, E. F., and French, K. R. 2015. “A Five-factor Asset Pricing Model,” Journal of Financial Economics, 116(1): 1~22. [16]Fama, E. F., and MacBeth, J. D. 1973. “Risk, Return, and Equilibrium: Empirical Tests,” Journal of Political Economy, 81(3): 607~636. [17]Green, J., Hand, J. R., and Zhang, X. F. 2013. “The Supra-view of Return Predictive Signals,” Review of Accounting Studies, 18(3): 692~730. [18]Green, J., Hand, J. R., and Zhang, X. F. 2014. “The Remarkable Multidimensionality in the Cross-section of Expected US Stock Returns,” SSRN working paper. [19]Greenblatt, J. 2006. “The Little Book that Beats the Market,” John Wiley & Sons. [20]Hadlock, C. J., and Pierce, J. R. 2010. “New Evidence on Measuring Financial Constraints: Moving beyond the KZ index,” The Review of Financial Studies, 23(5): 1909~1940. [21]Harvey, C. R., Liu, Y., and Zhu, H. 2016. “… and the Cross-section of Expected Returns,” The Review of Financial Studies, 29(1): 5~68. [22]Haugen, R. A., and Baker, N. L. 1996. “Commonality in the Determinants of Expected Stock Returns,” Journal of Financial Economics, 41(3): 401~439. [23]Hou, K., Xue, C., and Zhang, L. 2015. “Digesting Anomalies: An Investment Approach,” The Review of Financial Studies, 28(3): 650~705. [24]Hou, K., Xue, C., and Zhang, L. 2017. “A Comparison of New Factor Models,” SSRN working paper. [25]Huang, D., Jiang, F., Tu, J., and Zhou, G. 2015. “Investor Sentiment Aligned: A Powerful Predictor of Stock Returns,” The Review of Financial Studies, 28(3): 791~837. [26]Jiang, F., Qi, X., and Tang, G. 2018. “Q-theory, Mispricing, and Profitability Premium: Evidence from China,” Journal of Banking & Finance, 87: 135~149. [27]Jiang, F., Tang, G., and Zhou, G. 2019. “Firm Characteristics and Chinese Stocks,” SSRN working paper. [28]Kelly, B., and Pruitt, S. 2013. “Market Expectations in the Cross-section of Present Values,” The Journal of Finance, 68(5): 1721~1756. [29]Kelly, B., and Pruitt, S. 2015. “The Three-pass Regression Filter: A New Approach to Forecasting using many Predictors,” Journal of Econometrics, 186(2): 294~316. [30]Lam, F. Y., Wang, S., and Wei, K. C. 2015. “The Profitability Premium: Macroeconomic Risks or Expectation errors?” SSRN working paper. [31]Li, D., and Zhang, L. 2010. “Does q-theory with Investment Frictions Explain Anomalies in the Cross Section of Returns?” Journal of Financia`l Economics, 98(2): 297~314. [32]Light, N., Maslov, D., and Rytchkov, O. 2017. “Aggregation of Information about the Cross Section of Stock Returns: A Latent Variable Approach,” The Review of Financial Studies, 30(4): 1339~1381. [33]McLean, R. D., and Pontiff, J. 2016. “Does Academic Research Destroy Stock Return Predictability?” The Journal of Finance, 71(1): 5~32. [34]Neely, C. J., Rapach, D. E., Tu, J., and Zhou, G. 2014. “Forecasting the Equity Risk Premium: the Role of Technical Indicators,” Management Science, 60(7): 1772~1791. [35]Novy-Marx, R. 2013. “The other Side of Value: The gross Profitability Premium,” Journal of Financial Economics, 108(1): 1-28. [36]Palazzo, B. 2012. “Cash Holdings, Risk, and Expected Returns,” Journal of Financial Economics, 104(1): 162~185. [37]Rapach, D. E., Strauss, J. K., and Zhou, G. 2010. “Out-of-sample Equity Premium Prediction: Combination forecasts and links to the real economy,” The Review of Financial Studies, 23(2): 821~862. [38]Soliman, M. T. 2008. “The use of DuPont Analysis by Market Participants,” The Accounting Review, 83(3): 823~853. [39]Timmermann, A. 2006. “Forecast Combinations,” Handbook of Economic Forecasting, 1: 135~196. [40]Wang, H., and Yu, J. (2013). “Dissecting the Profitability Premium,” SSRN working paper.
