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.     

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.     

International Evidence on the Predictability of Stock Returns

This article examines the predictability of stock returns using international stock market data from eighteen countries. The study finds that the ability of dividend yields to predict stock returns increases as the return horizon lengthens from one month to forty-eight months. These results add to earlier ones, based on U.S. data only, showing that predictability grows with the return horizon. The study also explores why the observed pattern of predictability arises and provides evidence supporting the reasons suggested by Fama and French.     

Cross‐firm return predictability and accounting quality

We test the hypothesis that if poor accounting quality (AQ) is associated with poor investor understanding of firms’ revenue and cost structures, then poor AQ stocks likely respond more slowly than good AQ stocks to new non‐idiosyncratic information that affects both sets of firms. Consistent with this, results indicate that stock returns of good AQ firms significantly positively predict one‐month‐ahead stock returns to industry‐ and size‐matched poor AQ firms. In testing a delayed‐information‐processing mechanism behind the cross‐firm return predictability, we find that: (i) analyst earnings forecast revisions (FR) mimic the return patterns, as FR of good AQ firms significantly positively predict one‐month‐ahead FR of matched poor AQ firms; (ii) cross‐firm return predictability is concentrated in months with substantial news arrival, including months with Federal Open Market Committee (FOMC) rate announcements, but not in no‐news months; (iii) cross‐firm return predictability is stronger when the good AQ predictor firms have a richer information environment than poor AQ firms as proxied by analyst following, institutional ownership, and the presence of a Big 4 auditor. Collectively, the results uncover a new relation between accounting quality and stock return dynamics.     

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.     

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.     

Valuation Ratios and Stock Return Predictability in South Africa: Is It There?

Using monthly South African data for January 1990 through October 2009, this paper, to the best of our knowledge, is the first to examine the predictability of real stock return based on valuation ratios, namely, price-dividend and price-earnings ratios. We cannot detect either short-horizon or long-horizon predictability; that is, the hypothesis that the current value of a valuation ratio is uncorrelated with future stock price changes cannot be rejected at both short and long horizons based on bootstrapped critical values constructed from linear representations of the data. We find, via Monte Carlo simulations, that the power to detect predictability in finite samples tends to decrease at long horizons in a linear framework. Although Monte Carlo simulations applied to exponential smooth-transition autoregressive models of the price-dividend and price-earnings ratios show increased power, the ability of the nonlinear framework in explaining the pattern of stock return predictability in the data does not show any promise at either short or long horizons, just as in the linear predictive regressions.     

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.     

The information content of implied skewness and kurtosis changes prior to earnings announcements for stock and option returns

We use option prices to examine whether changes in stock return skewness and kurtosis preceding earnings announcements provide information about subsequent stock and option returns. We demonstrate that changes in jump risk premiums can lead to changes in implied skewness and kurtosis and are also associated with the mean and variability of the stock price response to the earnings announcement. We find that changes in both moments have strong predictive power for future stock returns, even after controlling for implied volatility. Additionally, changes in both moments predict call returns, while put return predictability is primarily linked to changes in skewness.     

As told by the supplier: Trade credit and the cross section of stock returns

With superior information about their customers’ prospects, suppliers extend trade credit to capture future profitable business. We show that this information advantage generates significant return predictability. After controlling for major firm characteristics, firms that rely more on trade credit relative to debt financing have higher subsequent stock returns. The return predictability by trade credit is stronger among firms with lower borrowing capacity or profitability, and is more significant for firms with a higher degree of information asymmetry. Our findings suggest that trade credit extension reveals suppliers’ information that diffuses gradually across the investing public.     

Out‐of‐sample stock return predictability in emerging markets

This article builds on the widely debated issue of stock return predictability by applying a broad range of predictor variables and comprehensively considering the in‐sample and out‐of‐sample stock return predictability of ten advanced emerging markets. It compares forecasts from models with a single predictor variable, multiple predictor variables and a combination forecast approach. The results confirm the findings of Welch and Goyal (2008) for US data that only a limited number of individual predictor variables are able to deliver significant out‐of‐sample forecasts. However, a combination forecast approach provides statistically and economically significant out‐of‐sample forecast results.     

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%.     

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’.     

Economic Links and Predictable Returns

This paper finds evidence of return predictability across economically linked firms. We test the hypothesis that in the presence of investors subject to attention constraints, stock prices do not promptly incorporate news about economically related firms, generating return predictability across assets. Using a data set of firms' principal customers to identify a set of economically related firms, we show that stock prices do not incorporate news involving related firms, generating predictable subsequent price moves. A long–short equity strategy based on this effect yields monthly alphas of over 150 basis points.     

The information content of option ratios

A broad stream of research shows that information flows into underlying stock prices through the options market. For instance, prior research shows that both the Put–Call Ratio (P/C) and the Option-to-Stock Volume Ratio (O/S) predict negative future stock returns. In this paper, we compare the level of information contained in these two commonly used option volume ratios. First, we find that P/C ratios contain more predictability about future stock returns at the daily level than O/S ratios. Second, in contrast to our first set of results, O/S ratios contain more predictability about future returns at the weekly and monthly levels than P/C ratios. In fact, our tests show that while P/C ratios contain predictability about future daily returns and, to some extent, future weekly returns, the return predictability in P/C ratios is fleeting. O/S ratios, on the other hand, significantly predict negative returns at all levels: daily, weekly, and monthly. While Pan and Poteshman (2006) show that signed P/C ratios, which require proprietary data, have predictive power, we find that unsigned P/C ratios, which do not require proprietary data, also have predictive power.     

Importance of skewness in decision making: Evidence from the Indian stock exchange

In this paper our goal is to examine the importance of skewness in decision making, in particular on investor utility. We use time-series daily data on sectoral stock returns on the Indian stock exchange. We test for sectoral stock return predictability using commonly used financial ratios, namely, the price-to-book, dividend yield and price-earnings. We find strong evidence of predictability. Using this evidence of predictability, we forecast sectoral stock returns for each of the sectors in our sample, allowing us to devise trading strategies that account for skewness of returns. We discover evidence that accounting for skewness leads not only to higher utility compared to a model that ignores skewness, but utility is sector-dependent.     

Return Behavior in Emerging Stock Markets

This article investigates the behavior of stock returns in thetwenty stock markets represented in the International FinanceCorporation's Emerging Markets Data Base. The aim is to testfor return anomalies and predictability. Using statistical methodologiesthat have identified seasonal and size-based return differences,as well as general return predictability in industrial markets,we find that these emerging markets display few of the sameanomalies. In particular, we find limited evidence of turn-of-the-tax-yeareffects and small-firm effects. We do find, however, evidenceof return predictability.     

Investor attention,aggregate limit-hits,and stock returns

We develop a simple measure of investor attention by aggregating the number of days that a stock hits the upper or lower limit on a monthly basis. This attention proxy describes investor trading behavior and contains information of future stock returns. Using data from the Chinese equity market from 2002 to 2017, we provide extensive evidence that the investor attention captured by our measure negatively predicts cross-sectional stock returns, and the long–short trading strategy based on this attention measure produces significant economic value. We argue that the attention-motivated trading is the main cause behind the return predictability of aggregate limit-hits.