Do oil futures prices predict stock returns?

This paper explores stock return predictability by exploiting the cross-section of oil futures prices. Motivated by the principal component analysis, we find the curvature factor of the oil futures curve predicts monthly stock returns: a 1% per month increase in the curvature factor predicts 0.4% per month decrease in stock market index return. This predictive pattern is prevailing in non-oil industry portfolios, but is absent for oil-related portfolios. The in- and out-of-sample predictive power of the curvature factor for non-oil stocks is robust and outperforms many other predictors, including oil spot prices. The predictive power of the curvature factor comes from its ability to forecast supply-side oil shocks, which only affect non-oil stocks and are hedged by oil-related stocks.     

The output gap and stock returns: Do cyclical fluctuations predict portfolio returns?

This study examines whether the output gap leads portfolio stock returns. The paper conducts in-sample and out-of-sample forecasting of US stock portfolios formed on the basis of size and value. First, the paper finds cross-sectional portfolios are predictable in-sample by the output gap. Out-of-sample evidence is weaker but still generally supports the finding that the historical average benchmark can be beaten. Secondly and most importantly, we find mixed evidence that the Fama–French factor mimicking portfolios can be forecasted by the output gap. In particular, there is some out-of-sample predictability of the size effect (SMB) suggesting this lags the output gap. However, the output gap, a key business cycle indicator, cannot predict the value effect (HML) either in-sample or out-of-sample. Our results add to the prior literature which finds that the factor mimicking returns are related contemporaneously (Petkova and Zhang, 2005) or lead (Liew and Vassalou, 2000) economic indicators.     

When does investor sentiment predict stock returns?

We examine the asymmetry in the predictive power of investor sentiment in the cross-section of stock returns across economic expansion and recession states. We test the implication of behavioral theories and evidence that the return predictability of sentiment should be most pronounced in an expansion state when investors' optimism increases. We segregate economic states according to the NBER business cycles and further implement a multivariate Markov-switching model to capture the unobservable dynamics of the changes in the economic regime. The evidence suggests that only in the expansion state does sentiment perform both in-sample and out-of-sample predictive power for the returns of portfolio formed on size, book-to-market equity ratio, dividend yield, earnings-to-price ratio, age, return volatility, asset tangibility, growth opportunities, and 11 widely documented anomalies. In a recession state, however, the predictive power of sentiment is generally insignificant.     

Does long-term disequilibrium in stock price predict future returns?

We propose a trading strategy based on error correction term (ECT), the residuals from the cointegration relation between the levels of security and the market portfolio. We find that buying stocks in the top 10 % ECT and selling stocks in the bottom 10 % ECT generates 1.09 % a month for 6-month holding period over 1965–2005. The monthly return increases to 1.57 % when the above trading strategy is applied to stocks with insignificant cointegration with the market portfolio. This profit is robust to three and four factor models. Moreover, this profit is neither driven by small and illiquid stocks nor is the result of any inherent positive serial correlation.     

Can margin traders predict future stock returns in Japan?

A growing body of literature suggests that investor sentiment affects stock prices both at the firm level and at the market level. This study examines the relationship between investor behavior and stock returns focusing on Japanese margin transactions using weekly data from 1994 to 2003. Margin trading is dominated by individual investors in Japan. In analysis at the firm level, we find a significant cross-sectional relationship between margin buying and stock returns. Both market-level and firm-level analyses show that margin buying traders follow herding behavior. They seem to follow positive feedback trading behavior for small-firm stocks and negative feedback trading behavior for large firm stocks. Our results show that information about margin buying helps predict future stock returns, especially for small-firm stocks at short horizons. The predictive power does not diminish even after controlling for firm size and liquidity.     

When does the dividend–price ratio predict stock returns?

If the dividend–price ratio becomes I(1) while stock returns are I(0), the unbalanced predictive regression makes the predictability test more likely to indicate that the dividend–price ratio has no predictive power. This might explain why the dividend–price ratio evidences strong predictive power during one period, while it exhibits weak or no predictive power at other times. Using international data, this paper demonstrates that the dividend–price ratio generally has predictive power for stock returns when both are I(0). However, this paper also shows that the dividend–price ratio loses its predictive power when it becomes I(1). The results are shown to be robust across countries.     

Do political factors affect stock returns during presidential elections?

This study investigates whether or not political factors such as government policy and political connections affected stock returns during the 2008 Taiwanese presidential election. We find that firms that benefitted from (were threatened by) the proposed Three-Links policy of the winning party experienced positive (negative) stock returns during the election. We use the sensitivities of firms’ returns to bilateral trade flows between Taiwan and China to measure the government-policy effect. Our results show that the effects of political connections weakly exist, but they become more significant when the support ratio of the winning party increased in polling data. We also find that only the government-policy effect holds for different crash-risk and corporate-governance levels. Finally, investment strategies based on both political factors can generate positive abnormal returns with respect to the Fama-French Three-factor Model.     

Do industries matter in explaining stock returns and asset-pricing anomalies?

Industry returns cannot be explained fully by well-known asset pricing models. This study reveals that common factors extracted from industry returns carry significant risk premiums that go beyond the explanatory power of size, book-to-market (BM) ratios, and momentum. In particular, this study shows that (1) the small-firm effect is significant only for firms whose market capitalization is below their industry average; (2) the BM effect is an intra-industry phenomenon; (3) a one-year momentum effect is significant only for firms whose BM ratio is smaller than the industry average and limited to non-January months; and (4) there is seasonality in all effects that cannot be explained by risk-based asset-pricing models. Neither rational nor behavioral theories alone can explain industry returns, and it is perhaps too hasty to attribute asset pricing anomalies to a single driving force.     

Wage gap and stock returns: Do investors dislike pay inequality?

Recent research shows that a high wage-gap between managers and workers identifies better-performing firms, but the stock market does not seem to price this information. In this paper, we show that not all investors neglect pay inequality. Using a unique data set on German firms’ employee compensation, we find that the mispricing of the wage gap is driven by limits to arbitrage. Specifically, some investors seem to bid up low-wage-gap stocks for non-monetary reasons, thus exhibiting a preference for low pay-inequality. The results suggest that firms with equitable pay schemes are rewarded with a lower cost of capital.     

Does volume help in predicting stock returns? An analysis of the Australian market

This paper presents an analysis of the relationship between trading volume and stock returns in the Australian market. We test this hypothesis by using data from a sample of firms listed on the Australian stock market for a period of 5 years from January 2001 to December 2005. We explore this relationship by focusing on the level of trading volume and thin trading in the market. Our results suggest that trading volume does seem to have some predictive power for high volume firms and in certain industries of the Australian market. However, for smaller firms, trading volume does not seem to have the same predictive power to explain stock returns in Australia.     

Does cash flow predict returns?

In this paper, we propose the hypothesis that cash flow and cash flow volatility predict returns. We categorize firms listed on the New York Stock Exchange into sectors, and apply tests for both in-sample and out-of-sample predictability. While we find strong evidence that cash flow volatility predicts returns for all sectors, the evidence obtained when using cash flow as a predictor is relatively weak. Estimated profits and utility gains also suggest that it is cash flow volatility that is more relevant as a source of information than cash flow.     

Do hedge funds' exposures to risk factors predict their future returns?

This paper investigates hedge funds' exposures to various financial and macroeconomic risk factors through alternative measures of factor betas and examines their performance in predicting the cross-sectional variation in hedge fund returns. Both parametric and non-parametric tests indicate a significantly positive (negative) link between default premium beta (inflation beta) and future hedge fund returns. The results are robust across different subsample periods and states of the economy, and after controlling for market, size, book-to-market, and momentum factors as well as the trend-following factors in stocks, short-term interest rates, currencies, bonds, and commodities. The paper also provides macro-level and micro-level explanations of our findings.     

What drives stock returns in Japan?

Financial Markets and Portfolio Management - We investigate systematic factors driving stock returns and stock return predictability in Japan. We find that dividend yield, cash-flow yield, and...     

Does country risks predict stock returns and volatility? Evidence from a nonparametric approach

We use the k-th order nonparametric causality test at monthly frequency over the period of 1984:1–2015:12 to analyze whether aggregate country risk, and its components (economic, financial and political) can predict movements in stock returns and volatility of eighty-three developed and developing economies. The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the weak evidence of causality obtained under the standard Granger tests cannot be relied upon. When we apply the nonparametric test, we find that, while there is no evidence of predictability of squared stock returns barring one case, at times, there are nearly 50 percent of the countries where the aggregate risks and its components tend to predict stock returns and realized volatility.     

Do economic statistics contain information to predict stock indexes futures prices and returns? Evidence from Asian equity futures markets

This research examines the impact of local and international market factors on the pricing of stock indexes futures in East Asian countries. The purpose of this paper is to present a study of the significant factors that determine the major stock indexes futures’ prices of Hong Kong, Malaysia, Singapore, South Korea and Taiwan. This study first investigates the relationships between Hang Seng Index Futures, KLCI Futures, SiMSCI Futures, KOSPI Futures, Taiwan Exchange Index Futures and local interest rates, dividend yields, local exchange rates, overnight S&P500 index and a newly constructed index, Asian Tigers Malaysia Index (ATMI). 11 years historical data of stock indexes futures and the economic statistics are studied; 10 years in-sample data are used for testing and developing the pricing models, and 1 year out-of-sample data is used for the purpose of verifying the predicted values of the stock indexes futures. Using simple linear regressions, local interest rates, dividend yields, exchange rates, overnight S&P500 and ATMI are found to have significant impact on these futures contracts. In this research, the next period close is predicted using simple linear regression and non-linear artificial neural network (ANN). An examination of the prediction results using nonlinear autoregressive ANN with exogenous inputs (NARX) shows significant abnormal returns above the passive threshold buy and hold market returns and also above the profits of simple linear regression (SLR). The empirical evidence of this research suggests that economic statistics contain information which can be extracted using a hybrid SLR and NARX trading model to predict futures prices with some degree of confidence for a year forward. This justifies further research and development of pricing models using fundamentally significant economic determinants to predict futures prices.

     

Does investor recognition predict returns?

Merton [1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483–510] shows that stocks about which not all investors are informed should yield a return premium. This premium depends on the shadow cost of incomplete information which in turn depends on the shareholder base, relative market size, and idiosyncratic risk. Utilizing a comprehensive database of Swedish shareholdings, we demonstrate that stock returns are positively related to the shadow cost. We also find that the shareholder base is negatively related to returns when controlling for size and idiosyncratic risk. Zero-cost portfolios based on the shadow cost/shareholder base yield substantial trading profits that are never positively correlated with the market and are only modestly explained by the four-factor model.     

Can (unusual) weather conditions in New York predict South African stock returns?

This paper investigates the explanatory power of certain weather variables, measured as deviations from their monthly averages, in a leading international financial trading centre, i.e., New York, for South African stock returns, over the daily period January 2nd, 1973 to December, 31, 2015. The empirical results highlight that these unusual deviations of weather variables have a statistically significant negative effect on the stock returns in South Africa, indicating that unusual weather conditions in New York can be used to predict South African stock returns, which otherwise seems to be highly unpredictable. In fact, a forecasting exercise recommends that a trading rule that considers those weather variables through a GARCH modelling approach seems to outperform the random walk model and thus beat the market.     

An encyclopedia for stock markets? Wikipedia searches and stock returns

We present empirical evidence that collective investor behavior can be inferred from large-scale Wikipedia search data for individual-level stocks. Drawing upon Shannon transfer entropy, a model-free measure that considers any kind of statistical dependence between two time series, we quantify the statistical information flow between daily company-specific Wikipedia searches and stock returns for a sample of 447 stocks from 2008 to 2017. The resulting stock-wise measures on information transmission are then used as a signal within a hypothetical trading strategy. The results evidence the predictive power of Wikipedia searches and are in line with the previously documented notion of buying pressure revealed by online investor attention and the trading patterns of retail investors.     

Do extreme returns matter in emerging markets? Evidence from the Chinese stock market

Recent evidence in the U.S. and Europe indicates that stocks with high maximum daily returns in the previous month, perform poorly in the current month. We investigate the presence of a similar effect in the emerging Chinese stock markets with portfolio-level analysis and firm-level Fama–MacBeth cross-sectional regressions. We find evidence of a MAX effect similar to the U.S. and European markets. However, contrary to U.S. and European evidence, the MAX effect in China does not weaken much less reverse the anomalous idiosyncratic volatility (IV) effect. Both the MAX and IV effects appear to independently coexist in the Chinese stock markets. Interpreted together with the strong evidence of risk-seeking behaviour among Chinese investors, our results partially support the suggestion that the negative MAX effect is driven by investor preference for stocks with lottery-like features.     

When do high stock returns trigger equity issues?

One of the most prominent stylized facts in corporate finance is that equity issues tend to follow periods of high stock returns. We document that firms exhibit such timing behavior only in response to high returns that coincide with strong institutional investor demand. When not accompanied by institutional purchases, stock price increases have little impact on the likelihood of equity issuance. The results highlight the importance of market reception for the timing of equity issues.