News Arrival and Cross-Asset Correlation Breakdown

Australian National University | March 15, 2012

Implications for Algorithmic Trading

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.

In normal circumstances, the return correlation between the stock futures and spot quotes is nearly perfect, because futures market makers peg their quotes to those of the underlying by using computerized algorithms. The author simple model predicts that this near-perfect correlation can occasionally break down with two conditions:

  • the futures market is less liquid than the corresponding spot market
  • the uncertainty surrounding the impact of the news on the underlying stocks is sufficiently large.

This breakdown occurs because the futures market makers switch from automating the quote-matching process to manually monitor and update their quotes.

By employing the comprehensive RavenPack database with firm-level news releases, the authors test and confirm their model predictions.

In particular, the spot-futures return correlation falls as the news uncertainty rises, and this correlation breakdown is more prominent for small-cap stocks.

Furthermore, for actively traded stocks, the impact of the news on the breakdown is more intense. If the overall stock market experiences extreme turbulence, however, this impact is weaker.

Authors discuss the implications for the limits of algorithmic trading.


In the recent decade, one of the most signicant market structure developments worldwide is algorithmic trading, which utilizes computer-based algorithms to implement trading strategies without human intervention. The rapid rise of automated trading scheme is persuasive in a variety of securities markets. By 2009, algorithmic trading has accounted for more than 70% of equity trades in the U.S. (Hendershott, Jones and Menkveld (2011)).

For the rst quarter of 2010, at least one third of the order book executions on the London Stock Exchange (LSE) are resulted from algorithmic trading. Similar fast growing pattern of algorithmic trading is also seen in foreign exchange and nancial derivatives markets (Chaboud, Chiquoine, Hjalmarsson, and Vega (2009), Gomber, Arndt, Lutat, and Uhle (2011)). Alongside this dramatic trading phenomenon, a critical question that naturally arises is: Can algorithmic trading conducted by machines react appropriately at the arrivals of complex public news which require advanced analytical interpretation?

Their study attempts to tackle this question by modeling and testing empirically the role of public news arrivals in a trading environment populated by algorithmic trading. In particular, they establish their theoretical model in the context of a popular algorithmic trading strategy - computerbased arbitrage strategy.

This trading strategy explores the price inefficiencies among related securities or securities markets. In a scenario where algorithmic traders seek for arbitrage or hedging opportunities and set quotes in both single-stock futures and underlying stock markets, their model examines how the release of public news affects the trade-offs faced by these traders and derives factors that might alter the relationship between news arrivals and the return correlations between single-stock futures and underlying stocks.

In normal circumstances, the return correlation between single-stock futures and their underlying stocks should be close to perfect. By the cost-of-carry relation, futures market makers peg the quotes to the quotes of the underlying automatically using computerized algorithms.

However, futures market makers may shift from quote pegging to manual monitoring the news feed upon the arrival of public news if the news content is vague about the asset's fundamental value. Monitoring the news feed and analyzing its impact on prices can be a costly exercise because the correct interpretation of an announcement requires human attention and processing (see Foucault, Roell, and Sandäs (2003) and Liu (2009) for theoretical evidence; see Chakrabarty and Moulton (2012) for empirical support).

In view of the costly news monitoring, their model suggests that if the futures market is less liquid than the corresponding spot market, futures market makers will widen the spread to compensate for the cost of monitoring, and ultimately cause a momentary breakdown in spot-futures return correlation.

Their theoretical model yields two empirical implications. First, if the futures market is less liquid than the corresponding spot market, the strong contemporaneous return correlation between spot and futures declines as public news arrives and the dispersion of agreements on public news heightens. Second, the return correlation breakdown is expected to be more pronounced for stocks characterized by higher monitoring costs or greater opportunity costs of not monitoring.

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