In this study, we apply a rolling window approach to wavelet-filtered (denoised) S&P500 returns (2000–2020) to obtain time varying Hurst exponents. We analyse the dynamics of the Hurst exponents by applying statistical tests (e.g., for stationarity, Gaussianity and self-similarity), a...

Any lead-lag effect in an asset pair implies the future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead-lag indicators to uncover the origin of price discovery...

We identify all return leader-follower pairs among individual stocks using Granger causality regressions. Thus-identified leaders can reliably predict their followers' returns out of sample, and the return predictability works at the level of individual stocks rather than industries. Our results...

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectationformation process in the US stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time as...

We show that news stories contain information about economic linkages between firms and document that information diffuses slowly across linked stocks. Specifically, we identify linked stocks from co-mentions in news stories and find that linked stocks cross-predict one another's returns in the...

We introduce a new approach to predicting market returns using the cross-section of earnings and book values to explain current stock prices and extract aggregate expected returns. The proposed measure is countercyclical; it portends a significant fraction of the time-series variation in stock...

The level of daily stock returns is generally regarded as unpredictable. Instead of the level, we focus on the signs of these returns and generate forecasts using various statistical classification techniques, such as logistic regression, generalized additive models, or neural networks. The...

This paper examines return predictability of the U.S. stock market using portfolios sorted by size, book-to-market ratio, and industry. A novel panel variance ratio test is proposed and employed to evaluate time-varying return predictability from 1964 to 2011. It is found that the stock returns...

We study return predictability of the Dow Jones Industrial Average indices from 1900 to 2009. We find strong evidence that time-varying return predictability is driven by changing market conditions, consistent with the implications of the adaptive markets hypothesis. During market crashes, no...