Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data

This paper provides strong evidence of time-varying return predictability of the Dow Jones Industrial Average index from 1900 to 2009. Return predictability is found to be driven by changing market conditions, consistent with the implication of the adaptive markets hypothesis. During market crashes, no statistically significant return predictability is observed, but return predictability is associated with a high degree of uncertainty. In times of economic or political crises, stock returns have been highly predictable with a moderate degree of uncertainty in predictability. We find that return predictability has been smaller during economic bubbles than in normal times. We also find evidence that return predictability is associated with stock market volatility and economic fundamentals.     

Are stock markets really efficient? Evidence of the adaptive market hypothesis

This study examines the adaptive market hypothesis in the S&P500, FTSE100, NIKKEI225 and EURO STOXX 50 by testing for stock return predictability using daily data from January 1990 to May 2014. We apply three bootstrapped versions of the variance ratio test to the raw stock returns and also whiten the returns through an AR-GARCH process to study the nonlinear predictability after accounting for conditional heteroscedasticity through the BDS test. We evaluate the time-varying return predictability by applying these tests to fixed-length moving subsample windows and also examine whether there is a relationship between the level of predictability in stock returns and market conditions. The results show that there are periods of statistically significant return predictability, but also episodes of no statistically significant predictability in stock returns. We also find that certain market conditions are statistically significantly related to predictability in certain markets but each market interacts differently with the different market conditions. Therefore our findings suggest that return predictability in stock markets does vary over time in a manner consistent with the adaptive market hypothesis and that each market adapts differently to certain market conditions. Consequently our findings suggest that investors should view each market independently since different markets experience contrasting levels of predictability, which are related to market conditions.     

International stock return predictability under model uncertainty

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive international dataset, we find that interest-rate related variables are usually among the most prominent predictive variables, whereas valuation ratios perform rather poorly. Yet, predictability of market excess returns weakens substantially, once model uncertainty is accounted for. We document notable differences in the degree of in-sample and out-of-sample predictability across different stock markets. Overall, these findings suggest that return predictability is neither a uniform, nor a universal feature across international capital markets.     

Developed markets’ business cycle dynamics and time-variation in emerging markets’ asset returns

This paper empirically studies the predictability of emerging markets’ stock returns by business cycle variables and the role of developed markets’ business cycle dynamics in this respect. The evidence shows that the link between business cycles and future stock market returns among emerging markets is considerably weaker than among developed markets. By contrast, I find strong evidence of stock return predictability by the respective country’s dividend-price ratio. This latter finding could reflect that variation in dividend-price ratios potentially reflects both the temporary impact of “hot money” inflows on emerging markets’ asset prices and rational expectations of future returns.     

Anomalies across the globe: Once public,no longer existent?

Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 241 cross-sectional anomalies in 39 stock markets. We find, based on more than two million anomaly country-months, that the United States is the only country with a reliable post-publication decline in long-short returns. Collectively, our meta-analysis of return predictors suggests that barriers to arbitrage trading can create segmented markets and that anomalies tend to represent mispricing instead of data mining.     

Information diffusion and the predictability of New Zealand stock market returns

This paper examines the impact of international predictors from liquid markets on the predictability of excess returns in the New Zealand stock market using data from May 1992 to February 2011. We find that US stock market return and VIX contribute significantly to the out‐of‐sample forecasts at short horizons even after controlling for the effect of local predictors, while the contribution by Australian stock market return is not significant. We further demonstrate that the predictability of New Zealand stock market returns using US market predictors could be explained by the information diffusion between these two countries.     

Trading volume and short-horizon contrarian profits: Evidence from the Malaysian market

We provide evidence on short-term predictability of stock returns on the Malaysian stock market. We examine the relation between return predictability and the level of trading activity. This is particularly relevant in emerging stock markets, where thin trading is more pervasive. We find that the returns from a contrarian portfolio strategy are positively related to the level of trading activity in the securities. Specifically, the contrarian profits on actively and frequently traded securities are significantly higher than that generated from the low trading activity securities. We find that the differential behavior of high- and low-volume securities is not subsumed by the size effect, although for the small firms, the volume–predictability relation is most pronounced. We also suggest that the price patterns may be related to the institutional arrangement in the Malaysian stock market.     

Option trading after the opening bell and intraday stock return predictability

Prior literature finds that information is reflected in option markets before stock markets, but no study has explored whether option volume soon after market open has predictive power for intraday stock returns. Using novel intraday signed option-to-stock volume data, we find that a composite option trading score (OTS) in the first 30 min of market open predicts stock returns during the rest of the trading day. Such return predictability is greater for smaller stocks, stocks with higher idiosyncratic volatility, and stocks with higher bid–ask spreads relative to their options’ bid–ask spreads. Moreover, OTS is a significantly stronger predictor of intraday stock returns after overnight earnings announcements. The evidence suggests that option trading in the 30 min after the opening bell has predictive power for intraday stock returns.     

Market Segmentation and Cross‐predictability of Returns

We present evidence supporting the hypothesis that due to investor specialization and market segmentation, value‐relevant information diffuses gradually in financial markets. Using the stock market as our setting, we find that (i) stocks that are in economically related supplier and customer industries cross‐predict each other's returns, (ii) the magnitude of return cross‐predictability declines with the number of informed investors in the market as proxied by the level of analyst coverage and institutional ownership, and (iii) changes in the stock holdings of institutional investors mirror the model trading behavior of informed investors.     

Return and Volatility Spillovers and Cojump Behavior Between the U.S. and Korean Stock Markets

ABSTRACT

In this study, we examine return spillover, volatility transmission, and cojump behavior between the U.S. and Korean stock markets. In particular, we focus on cojump behavior between the two markets in order to explain the transmission of unexpected shocks. We find that the U.S. stock market causes return spillover effects in the Korean stock market, and there is significant volatility transmission between the two markets. Importantly, we find a stronger association in size, as compared with intensity, of cojumps between the U.S. and Korean stock markets, particularly during the recent financial crisis.     

The dividend–price ratio does predict dividend growth: International evidence

Unpredictable dividend growth by the dividend–price ratio is considered a ‘stylized fact’ in post war US data. Using long-term annual data from the US and three European countries, we revisit this stylized fact, and we also report results on return predictability. We make two main contributions. First, we document that for the US, results for long-horizon predictability are crucially dependent on whether returns and dividend growth are measured in nominal or real terms, and this difference is due to long-term inflation being strongly negatively predictable by the dividend–price ratio. The impact of inflation is to reinforce real return predictability and to reduce – or change direction of – real dividend growth predictability. This provides an explanation for the strong predictability of long-horizon real returns in the ‘right’ direction, and the strong predictability of long-horizon real dividend growth in the ‘wrong’ direction, that we see in US post war data. Second, we find that predictability patterns in three European stock markets are in many ways different from what characterize the US stock market. In particular, in Sweden and Denmark dividend growth is strongly predictable by the dividend–price ratio in the ‘right’ direction while returns are not predictable. The results for the UK are mixed. Our results are robust to a number of changes in the modeling framework. We discuss the results for dividend growth predictability in terms of the ‘dividend smoothing hypothesis’.     

A study of cross-industry return predictability in the Chinese stock market

We investigate cross-industry return predictability for the Shanghai and Shenzhen stock exchanges, by constructing 6- and 26- industry portfolios. The dominance of retail investors in these markets, in conjunction with the gradual diffusion of information hypothesis provide the theoretical background that allows us to employ machine learning methods to test for cross-industry predictability. We find that Oil, Telecommunications and Finance industry portfolio returns are significant predictors of other industries. Our out-of-sample forecasting exercise shows that the OLS post-LASSO estimation outperforms a variety of benchmarks and a long–short trading strategy generates an average annual excess return of 13%.     

Out-of-Sample Predictability in International Equity Markets: A Model Selection Approach

For 13 major international stock markets, there is much evidence of out-of-sample predictability for growth stocks especially when evaluated with economic criteria, and to a noticeably lesser extent for general stock markets and value stocks. Our results shed light on the recent debate about stock return predictability, using different assets (growth-style indexes), forecasting variables (past returns), forecasting models (nonlinear models), and alternative forecasting evaluation criteria (economic criteria). Our analysis suggests that (growth) stock returns might be predictable.     

On the predictability of Chinese stock returns

We examine stock return predictability in China. We take 18 firm-specific variables that have been documented to predict cross-sectional stock returns in the U.S. and examine their relation with stock returns in China for the sample period from 1995 to 2007. We find relatively weak predictability for Chinese stocks. Only five firm-specific variables predict returns in the Chinese market. Tests on U.S. stock returns find that more predictors can explain cross-sectional stock return variation. We test two explanations for the cause of weak returns predictability in China. First, perhaps return predictors in China are less heterogeneously distributed than they are in the U.S. Second, stock prices are less informative in China than they are in the U.S. We find support for both explanations.     

Pockets of Predictability

For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (“pockets”) are interspersed with long periods with no return predictability. We document this result empirically using a flexible time-varying parameter model that estimates predictive coefficients as a nonparametric function of time and explore possible explanations of this finding, including time-varying risk premia for which we find limited support. Conversely, pockets of return predictability are consistent with a sticky expectations model in which investors slowly update their beliefs about a persistent component in the cash flow process.     

Correlation and return dispersion dynamics in Chinese markets

This paper studies the relation between firm-level return dispersions and correlations among Chinese stocks during periods of unusually large upward and downward swings. We analyze individual stock returns across 18 sectors and test if return dispersions and stock correlations show asymmetric patterns for extreme up and down markets. Evidence from studies on U.S. stocks suggests that equity return correlations tend to be much greater on the downside than on the upside and that the degree of comovement gets even stronger during extreme market states. However, in the case of Chinese stock market, we find that higher downside correlations apply to only stocks within the Financial sector. With the exception of Financial stocks, we find that stock correlations are significantly higher during up markets, rather than down markets. Regarding firm-level return dispersions, our findings are consistent with rational asset pricing model predictions. We find that equity return dispersions are significantly higher during periods of large price changes.     

The changing and relative efficiency of European emerging stock markets

The martingale hypothesis is tested for 15 European emerging stock markets located in Croatia, the Czech Republic, Estonia, Hungary, Iceland, Latvia, Lithuania, Malta, Poland, Romania, Russia, the Slovak Republic, Slovenia, Turkey and the Ukraine. For comparative purposes, the developed stock markets in Greece, Portugal and the UK are also included. Rolling window variance ratio tests based on returns and signs and with wild bootstrapped p-values are used with daily data over the period beginning in February 2000 and ending in December 2009. The fixed-length rolling sub-period window captures changes in efficiency and is used to identify events which coincide with departures from weak-form efficiency and to rank markets by relative efficiency. Overall, return predictability varies widely. The most efficient are the Turkish, UK, Hungarian and Polish markets; the least efficient are the Ukrainian, Maltese and Estonian stock markets. The global financial market crisis of 2007–2008 coincides with return predictability in the Croatian, Hungarian, Polish, Portuguese, Slovakian and UK stock markets. However, not all markets were affected: the crisis had little effect on weak-form efficiency in stock markets located in Greece, Latvia, Romania, Russia and Turkey.     

Shareholder Coordination,Information Diffusion and Stock Returns

We show that the quality of information‐sharing networks linking firms’ institutional investors has stock return predictability implications. We find that firms with high shareholder coordination experience less local comovement and less post‐earnings announcement drift, consistent with the notion that information‐sharing networks facilitate information diffusion and improve stock price efficiency. In support of the view that coordination acts as an information diffusion channel, we document that the stock return performance of firms with high shareholder coordination leads that of firms with low shareholder coordination.     

In-sample vs. out-of-sample tests of stock return predictability in the context of data mining

We undertake an extensive analysis of in-sample and out-of-sample tests of stock return predictability in an effort to better understand the nature of the empirical evidence on return predictability. We find that a number of financial variables appearing in the literature display both in-sample and out-of-sample predictive ability with respect to stock returns in annual data covering most of the twentieth century. In contrast to the extant literature, we demonstrate that there is little discrepancy between in-sample and out-of-sample test results once we employ out-of-sample tests with good power. While conventional wisdom holds that out-of-sample tests help guard against data mining, Inoue and Kilian [Inoue, A., Kilian, L., 2004. In-sample or out-of-sample tests of predictability: which one should we use? Econometric Reviews 23, 371–402.] recently argue that in-sample and out-of-sample tests are equally susceptible to data mining biases. Using a bootstrap procedure that explicitly accounts for data mining, we still find that certain financial variables display significant in-sample and out-of-sample predictive ability with respect to stock returns.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.