Public news arrival and cross-asset correlation breakdown

This study models and tests empirically the role of public news arrivals in the quote matching across single-stock futures and underlying stock markets—a trading strategy often adopted by algorithmic traders. Our model suggests that quote return correlation across these two markets breaks down when the news uncertainty is sufficiently large and futures market makers switch from automating the quote matching process to manually analyze, monitor, and update quotes. We show empirically that the breakdown is more prominent for large stocks, and this effect of firm size falls during periods of high-market volatility. Our empirical results are robust to the effect of distraction due to extraneous news events.