Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of...

It is commonly accepted that information is helpful if it can be exploited to improve a decision making process. In economics, decisions are often based on forecasts of up- or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework...

We propose a new measure of the expected variance risk premium that is based on a forecast of the conditional variance from a GARCH-MIDAS model. We find that the new measure has strong predictive ability for future U.S. aggregate stock market returns and rationalize this result by showing that...

This paper aims at decomposing the forecast error variance of excess returns in five major European stock markets into the variance of news about future excess returns, dividends and real interest rates. Special emphasis is given on the issue of stationarity and structural breaks in the...

This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape (CARS) model with beta density to predict the direction of stock returns. The CARS model is continuously valued, which makes it different from binary classification...

This article examines an equity pairs trading strategy using daily, weekly and monthly European share price data over the period 1998–2007. The authors shows that when stocks are matched into pairs with minimum distance between normalised historical prices, a simple trading rule based on...