Investor sentiment indices based on k-step PLS algorithm: A group of powerful predictors of stock market returns

We construct a group of new investor sentiment indices by applying a new dimension reduction technique called k-step algorithm which adopts partial least squares method recursively. With the purpose of forecasting the aggregate stock market return, the new group of investor sentiment indices performs a greater ability in predicting the market return than existing investor sentiment indices in and out of sample by adequately using the information in residuals and eliminating a common noise component in sentiment proxies. This group of new investor sentiment indices beats five widely used economic variables and still has a strong return predictability after controlling these variables. Moreover, they could also predict cross-sectional stock returns sorted by industry, size, value, and momentum and generate considerable economic value for a mean-variance investor. We find the predictability of this group of investor sentiment indices comes from its forecasting power for discount rates and market illiquidity.     

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.     

Asset pricing in China's domestic stock markets: Is there a logic?

China's stock markets have grown rapidly since their inception and have become an increasingly important emerging market for international investors. However, there are few systematic studies on how asset prices are formed in Chinese domestic equity markets; popular financial media even depict the market as irrational. In this paper, we study the asset pricing mechanism in the nascent Chinese stock markets, with the objective of identifying variables that capture the cross-sectional variation in average stock returns. We focus on the effects of various market imperfections in China. We find that while the market risk (beta) is not priced, there is a significantly negative relationship between firm-specific risk and expected returns. Chinese investors are willing to pay a significant premium for more liquid stocks or for dividend-paying stocks. Furthermore, investors value local A-shares more if there are offshore counterparts (e.g., B- and H-shares) for foreigners, implying that a Chinese firm with a foreign shareholder base has a lower cost of capital, ceteris paribus. Lastly, as with U.S. and other mature markets, firm size and the book-to-market ratio are systematically related to stock returns. Given market imperfections, stocks are priced rather rationally in China, despite the widespread perception to the contrary.     

More Than Words: Quantifying Language to Measure Firms' Fundamentals

We examine whether a simple quantitative measure of language can be used to predict individual firms' accounting earnings and stock returns. Our three main findings are: (1) the fraction of negative words in firm-specific news stories forecasts low firm earnings; (2) firms' stock prices briefly underreact to the information embedded in negative words; and (3) the earnings and return predictability from negative words is largest for the stories that focus on fundamentals. Together these findings suggest that linguistic media content captures otherwise hard-to-quantify aspects of firms' fundamentals, which investors quickly incorporate into stock prices.     

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.     

Stock Return Predictability and The Role of Monetary Policy

This article examines whether shifts in the stance of monetary policy can account for the observed predictability in excess stock returns. Using long-horizon regressions and short-horizon vector autoregressions, the article concludes that monetary policy variables are significant predictors of future returns, although they cannot fully account for observed stock return predictability. I undertake variance decompositions to investigate how monetary policy affects the individual components of excess returns (risk-free discount rates, risk premia, or cash flows).     

The Variation of Economic Risk Premiums in Real Estate Returns

We examine the predictable components of returns on stocks, bonds, and real estate investment trusts (REITs). We employ a multiple-beta asset pricing model and find that there are varying degrees of predictability among stocks, bonds, and REITs. Furthermore, we find that most of the predictability of returns is associated with the economic variables employed in the asset pricing model. The stock market risk premium is highly important in capturing the predictable variation in stock portfolios, and the bond market risk premiums (term and risk structure of interest rates) are important in capturing the predictable variation in bond portfolios. For REITs, however, both the stock and bond market risk premiums capture the predictable variation in returns. REITs have comparable return predictability to stock portfolios. We conclude that there is an important economic risk premium for REITs that are not captured by traditional multiple-beta asset pricing models.     

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.     

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.     

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.     

Do risk exposures explain accounting anomalies? A new testing method

I quantitatively evaluate how much of the cross-sectional return predictive ability of a range of accounting anomalies can be attributed to firm-specific stock return covariances with market risk factors. Using a novel two-step regression-based testing method, I find robust evidence that the risk factor exposures do not explain a meaningful fraction of the time-series variations in the return predictive coefficients of the anomaly variables. Out-of-sample tests further confirm that it is the mispricing component of the accounting anomalies that is mainly responsible for the return predictability.     

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.     

Share issuance and cross-sectional returns: International evidence

Share issuance predicts cross-sectional returns in a non-U.S. sample of stocks from 41 different countries. Issuance predictability has greater statistical significance than either size or momentum, and is similar to book-to-market. As in the U.S., the international issuance effect is robust across both small and large firms. Unlike the U.S., the effect is driven more by low returns after share creation rather than positive returns following share repurchases. Issuance return predictability is stronger in countries with greater issuance activity, greater stock market development, and stronger investor protection. The results suggest that the share issuance effect is related to the ease with which firms can issue and repurchase their shares.     

Twitter investor sentiment and corporate earnings announcements

We examine the impact of firm-specific investor sentiment (FSIS) on stock returns for negative and positive earnings surprises. Using a measure constructed from firm-specific tweets, we find that FSIS has a greater impact on stock returns for negative relative to positive earnings surprises. We further show that the impact of FSIS is greater for firms whose valuation is uncertain and difficult to arbitrage. Moreover, we provide evidence of return reversals over post-announcement periods. Our results highlight the importance of FSIS around earnings announcements.     

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.     

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.     

Does Academic Research Destroy Stock Return Predictability?

We study the out‐of‐sample and post‐publication return predictability of 97 variables shown to predict cross‐sectional stock returns. Portfolio returns are 26% lower out‐of‐sample and 58% lower post‐publication. The out‐of‐sample decline is an upper bound estimate of data mining effects. We estimate a 32% (58%–26%) lower return from publication‐informed trading. Post‐publication declines are greater for predictors with higher in‐sample returns, and returns are higher for portfolios concentrated in stocks with high idiosyncratic risk and low liquidity. Predictor portfolios exhibit post‐publication increases in correlations with other published‐predictor portfolios. Our findings suggest that investors learn about mispricing from academic publications.     

Sticky prices or economically-linked economies: The case of forecasting the Chinese stock market

We explore whether economic links via trade affect aggregate Chinese stock market returns. We find that market return indices from countries that China net imports from can forecast the Chinese aggregate market return at the weekly time horizon. The stock returns of countries that China net exports to have no consistently significant OOS predictability.The economic intuition for our results follows from the fact that China has positioned itself as a low-cost provider competing on price. As a low-cost provider China has a more difficult time passing cost increases through to export customers because of sticky prices. However, import costs, e.g., raw materials, are subject to both consumption and speculative demand and thus vary. We can conclude that costs will drive short term economic gains for the overall Chinese economy. One interpretation of our results is that supply shocks are absorbed within 2 weeks.     

Will Order Imbalances Predict Stock Returns in Extreme Market Situations? Evidence from China

This article examines the relation between order imbalances and stock returns in China during the extreme market situations in 2007 and 2008. We find that order imbalances are positively and significantly related to contemporaneous stock returns but have limited predictability for subsequent returns in extreme market situations. Moreover, order imbalances significantly predict returns in normal market environment, especially for small stocks. This may be attributed to the investor structure in the Chinese market. A trading strategy utilizing the relation between order imbalances and stock returns generates positive returns. Overall, the information contents of order imbalances vary with the market environment.