Stock return predictability and determinants of predictability and profits

Abstract

We examine stock return predictability for India and find strong evidence of sectoral return predictability over market return predictability. We show that mean-variance investors make statistically significant and economically meaningful profits by tracking financial ratios. For the first time in this literature, we examine the determinants of time-varying predictability and mean-variance profits. We show that both expected and unexpected shocks emanating from most financial ratios explain sectoral return predictability and profits. These are fresh contributions to the understanding of asset pricing.

Introduction

Stock return predictability has been one of the most researched topics in empirical asset pricing. There is voluminous literature on the use of financial ratios as predictors of stock returns (see, inter alia, Fama and French, 1988, Lamont, 1998, Welch and Goyal, 2008, Rapach et al., 2010, Gupta et al., 2014). The empirical findings on predictability have not met with any consensus, thereby triggering a methodological response. Studies began by addressing fundamental econometric issues which were prevalent in the earlier literature. These issues mainly relate to the predictor variable, that is, whether or not the predictor variable is persistent and endogenous (see, inter alia, Campbell and Yogo, 2006, Lanne, 2002, Lewellen, 2004; and Stambaugh, 1999) and whether the predictive regression model is heteroskedastic (see, Westerlund and Narayan, 2012, Westerlund and Narayan, 2015).

In this paper we contribute to the stock return predictability literature by investigating whether financial ratios predict sectoral stock returns on the Indian stock exchange. Our empirical investigation is based on four specific approaches. First, we use a time-series predictive regression model proposed by Westerlund and Narayan, 2012, Westerlund and Narayan, 2015 to examine the null hypothesis of no predictability based on a generalised least squares estimator (GLS). The main advantage of this test is that it accounts for all three salient features of the data and model, namely, predictor persistency and endogeneity, and model heteroskedasticity. Second, we extend the GLS-based predictive regression model to a time-varying model thereby extracting and observing predictability (or lack of it) over time. Third, using the time-series predictive regression estimates we treat them as a dependent variable and regress them on expected and unexpected financial ratio shocks. Our goal here is to examine what determines predictability over time. Fourth, we expand on the economic significance aspect of our paper by estimating, using forecasted returns, profits for a mean-variance investor who is faced with a mean-variance utility function. This analysis results in a time-series of profits per sector. We then examine the determinants of this sectoral profitability by regressing profits on expected and unexpected financial ratio shocks. To the best of our knowledge, ours is the first paper to undertake this type of analysis.

These approaches allow us to conclude with the following key findings. First, while evidence of market return predictability is weak, sectoral return predictability is strong. Second, dividend–payout ratio and dividend yield turn out to be the most popular predictors, predicting returns for all the 12 sectors, while earnings–price ratio turns out to be the second most popular predictor—it predicts returns for five sectors. The book-to-market ratio, by comparison, appears to be the least popular predictor, predicting returns for only two sectors. Third, the predictability of sectoral stock returns is supported by evidence that all financial ratio-based forecasting models offer investors statistically significant profits. However, profits vary by sector and some of the sectoral profits are in excess of the market profits. Fourth, we find that while expected financial ratio risks explain predictability and profitability in almost all sectors, unexpected financial ratio risks only explain predictability and profitability in some of the 12 sectors. From this, we conclude that one source of sectoral heterogeneity with respect to predictability and profitability is the unexpected financial ratio risk.

Our paper connects with and contributes to multiple strands of the literature. First, our study relates to the relatively small group of studies that examines stock return predictability for developing countries (see, Dicle et al., 2010, Harvey, 1995, Hjalmarsson, 2010, Gupta and Modise, 2012, Narayan and Bannigidadmath, 2015, Narayan et al., 2015b, Westerlund et al., 2015). The differences between the present study and that of Narayan and Bannigidadmath (2015) are multiple. First, we study time-varying predictability. Hence, with our model and results we have a dynamic predictive regression model while Narayan and Bannigidadmath (2015) have a static model. In other words, from our study one can observe predictability over time, allowing one to infer phases over which predictability exists and vice versa. By comparison, from Narayan and Bannigidadmath (2015) study one only learns whether predictability exists or not on average. The second main difference is that Narayan and Bannigidadmath (2015) do not explain the determinants of predictability. We propose time-series models of the determinants of predictability. We further extend the analysis to study also the determinants of mean-variance investor profits. We are able to propose a time-series predictability and profitability determinants model because we use daily data which gives us sufficient sample sizes to conduct empirical tests.

We believe that a daily data model is a better predictor of returns than a monthly data model for two reasons. First, recent studies question hypotheses test based on the use of a single data frequency; see, for instance, Narayan and Sharma (2015a) and Narayan et al. (2015a). From this literature it is clear that hypotheses test can be data frequency dependent. Hence, the use of at least the commonly used data frequencies should be considered in order to ascertain the robustness of the outcomes regarding a particular hypothesis test. Narayan and Bannigidadmath (2015) study is based on monthly data only. Therefore, the question that arises, motivated by the data-frequency debate alluded to earlier, is whether their results on predictability will hold when subjected to a daily data set, which contains richer information than monthly data.

Second, our goal in this paper is to propose a time-varying predictive regression model. Given that time-series data for India is available only from 1990, a time-varying predictive regression model based on monthly data will not be parsimonious, neither from a statistical point of view nor from an economic significance point of view. Since the theme of the paper revolves around a new statistical approach (time-varying predictive regression model) and economic implications of such time-varying predictability (time-varying profits and investor utility), we need a sample size that is not only rich (like daily data are) but one which gives us a sufficient number of observations (as daily data do) to conduct the statistical hypothesis test that we propose. Using daily data offer us a solution without costs.

Our choice of India is motivated by the fact that it is an emerging market which has been profitable for investors over the last decade and has, therefore, performed impressively (see, Narayan et al., 2014a, Narayan et al., 2014b), yet, what determines time-series predictability and profits is unknown. Our study, by taking a rigorous investigation on the role of financial ratios in predicting returns, adds not only to an understanding of the asset pricing behaviour on the Indian stock exchange but also to the broader role and importance of financial ratios in an emerging market, particularly with respect to determinants of predictability and profits over time.

Our study also connects to the literature that shows that hypotheses tests are sector-specific. In terms of evidence on sectoral return predictability, Westerlund and Narayan (2014) examine sectoral return predictability using NYSE data. It is important to entertain sectoral return predictability because there are several hypotheses that point to the fact that investors in a market have different speeds of reaction to news. Some investors over-react to news while others under-react to news. The over-reaction to news is explained by the positive-feedback-trader model of DeLong et al. (1990) and the overconfidence model of Daniel et al. (1998). DeLong et al. (1990), in particular, argue that the prices initially over-react to news about fundamentals, and then continue to over-react further for a period of time due to the prevalence of positive feedback from investors, who buy stocks when prices rise and sell when prices decline. Investors' under-reaction to news occurs through different channels, as described in the limited information hypothesis (see, Merton, 1987), the conservatism hypothesis (Barberis et al., 1998), and the gradual information diffusion hypothesis (Hong and Stein, 1999). A detailed discussion on these hypotheses is undertaken in the next section. The main message emerging from these theories is that investors are likely to be heterogeneous because: (a) some are more conservative than others; (b) some have more information than others; and (c) some receive information faster than others. In other words, how much investors are affected by information and, as a result, are different from each other, depends on “who they are”. It is the “who they are” aspect that we take particular interest in. We define “who they are” by “investors in different sectors”. Closely related to this idea is the work of Hong et al. (2007b), who contend that investors specialize in particular market segments and consider segmentation across US industries in forecasting stock returns. In this regard, our idea of considering sectoral stocks on the National Stock Exchange (India) seems reasonable and consistent with work done on the US market by Westerlund and Narayan (2014).

Using a time-series model, we confirm that predictability is sector-specific and that there are some financial ratios which are relatively more popular predictors of returns than others. By showing evidence of sector-specific predictability we contribute to a related strand of the literature that shows that hypotheses tests are sector-dependent. More specifically, this heterogeneity is captured when testing the predictability of macroeconomic variables (Hong et al., 2007b), testing the effects of oil price shocks (see, Narayan and Sharma, 2011), examining the performance of mutual funds (see, Busse and Tong, 2012), testing cross-predictability of returns (Menzly and Ozbas, 2010), testing turn-of-the-month effects (Sharma and Narayan, 2014), and testing price discovery in stocks and CDS markets (see, Narayan et al., 2014c), among others. In light of this relatively new body of evidence on sectoral heterogeneity, our study attempts to add to our understanding of sectoral heterogeneity beyond the US market. We contribute by showing that not only return predictability and mean-variance investor profits are sector-specific, but also the determinants of predictability and profits are different for different sectors.

Our third contribution relates directly to the stock return predictability literature. The focus of this literature has been on developing new approaches to testing for stock return predictability. For a review of this literature, see Westerlund and Narayan (2015). Therefore, it is easy to appreciate that this literature has not considered predictability in a time-varying manner. For this reason, no attempt has been made to test for the determinants of predictability. While we understand that stock returns are predictable, we have limited understanding of what the determinants of predictability are. By using a recursive window approach in testing for stock return predictability, we extract evidence of time-varying predictability, allowing us to test its determinants. We show that at the market level and for most sectors, both expected and unexpected financial ratio shocks determine predictability.

Our final contribution relates to the economic significance of stock return predictability. The bulk of the studies on stock return predictability tests the economic significance of predictability using a mean-variance investor utility function (see, inter alia, Rapach et al., 2010). The main finding from these studies is that by tracking financial ratios and using them to forecast returns, investors are able to make statistically significant profits. However, what actually determines these profits is unknown and has not been examined. We show that expected financial ratio shocks determine profitability in most of the sectors.

The balance of the paper proceeds as follows. In Section 2, we discuss our motivation for sectoral analysis of stock return predictability. The data set and estimation approach is discussed in Section 3. The preliminary statistical behaviour of data and the empirical findings are presented in Section 4. The penultimate section is devoted to understanding the determinants of sectoral predictability. The final section provides concluding remarks.