In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...

The availability of many variables with predictive power makes their selection in a regression context difficult. This study considers robust and understandable low-dimensional estimators as building blocks to improve overall predictive power by optimally combining these building blocks. Our new...

This paper uses transaction data to estimate how single stock circuit breakers on the London Stock Exchange affect other stocks that remain in continuous trading. This ‘spillover' effect is estimated by calculating the effect of a trading halt on the market quality of stocks that remain in...

We investigate the effects of fragmentation in equity trading on the quality of the trading outcomes, specifically volatility, liquidity and volume. We use panel regression methods on a weekly dataset following the FTSE350 stocks over the period 2008-2011, which provides a lot of cross-sectional...

This paper uses proprietary data to evaluate the efficacy of single-stock circuit breakers on the London Stock Exchange during July and August 2011. We exploit exogenous variation in the length of the uncrossing periods that follow a trading suspension to estimate the effect of auction length on...