This study models the role of public news arrivals on asset correlation in a trading environment populated by computerized algorithms. The model is empirically tested with the individual stock futures and its underlying spot markets, which are characterized by the mechanical cost-of-carry relation that is typically exploited by algorithmic trading. Under normal circumstances, the return correlation between the stock futures and spot quotes is expected to be nearly perfect, since futures market makers routinely peg their quotes to those of the underlying by using computerized algorithms. Our model predicts that this near-perfect correlation can occasionally break down with two conditions: one, there are liquidity differences between the futures and spot markets; and two, there are suffciently large uncertainties surrounding the impact of public news on the underlying stocks. This breakdown occurs because the futures market makers switch from automating the quotematching process to manually monitor and update their quotes. By employing the comprehensive RavenPack database with firm-level news releases, we test and con rm our model predictions. The correlation breakdown is more prominent with increased uncertainty associated with the firm-level news release, higher trading activity of the underlying stock, and smaller firm size. Our results remain robust even after using various measures of news uncertainty and controlling for extreme stock market turbulence and short-selling activity. We discuss the implications of our results for
the limits of algorithmic trading.
Keywords: Correlation Breakdown, Algorithmic Quote Matching, Single-Stock Futures, Public News Arrival
JEL Classification Number: G10, G11, G12, G14
학회소식
[2012년 제 4차] Public News Arrival and Cross-Asset Correlation Bre
작성자 : 관리자
조회수 : 1013
게시일 :
2012-12-12
