We compare the performance of time-series (TS) and cross-sectional (CS) strategies based on past returns. While CS strategies are zero-net investment long/short strategies, TS strategies take on a time-varying net-long investment in risky assets. For individual stocks, the difference between the...

We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

A deep learning method is applied to predict stock portfolio allocation in the Chinese stock market. We use 6 original price and volume series as benchmark model settings and further explore the model's predictive performance with social media sentiment. Our results show that our model can...

We elaborate economic explanations for the time-varying risk of month, quarter and year base load electricity forward contracts traded on the Nord Pool Energy Exchange from January 2006 to March 2010. Daily risk quantities are generated by decomposing realized volatility in its continuous and...

Accurately forecasting volatility is key in many financial applications. In this study, I suggest that individuals gather information online before implementing their trading decisions. In periods of higher investor concern, online information seeking intensifies. By analysing Google search data...

This paper replicates and extends the Amihud (2002) study that links liquidity to asset pricing. Using the current version of the CRSP dataset, we obtain essentially the same results that Amihud presents. The same methods applied to more recent data show a much weaker relation between liquidity...