Global financial markets are highly integrated. Even the hint of a recession in the United States reverberates in European and Asian markets. More recently, the sub-prime crisis started in the United States but propagated globally.  Markets around the world also react to positive information. Actions by U.S. regulators to ensure the stability of the U.S. financial system resulted in recovers in financial markets around the world. The ability to identify events that will affect markets globally obviously could provide investors with a tool for identifying opportunities for high returns.

In this article, we report on a study of extreme return cluster. By an extreme return cluster, we mean a set of extreme returns, positive or negative, that is driven by the same information. For example, the very low returns observe around the world after the failure of Bear Stern in September 2008 is an extreme return cluster. The beginning of an extreme return cluster is marked by investors’ reaction to new information and end is determined by this information being fully incorporated into prices.

            Consider the sub-prime credit crisis that began around January 2007. We are continuing to experience the impact of related information till the time this article is being written. This type of information is quite distinct and independent from information generated immediately after the 9/11 terrorist attack. These periods characterize informationally independent return clusters

Our goal is to come up with a method that allows us to determine the beginning and ends of extreme return clusters by identifying information underlying the clusters.

A detailed discussion of our results is beyond the scope of this article, but a copy of our research paper is available by emailing to sankaran@nmsu.edu. Here we only provide a summary. Our research provides a novel way of analyzing information that affects the domestic indices such as DJIA, S&P 500 and NASDAQ by characterizing clusters of positive extreme returns.  We employ a return strategy to predict lead/lag at the starting of a cluster and a volatility strategy for the ending.  It is the occurrence of an extreme return that marks the index as being in-cluster, thus enabling the investor to predict the first extreme return of another index using the asynchronous in-cluster status.  Additionally, if an investor observes that an index is the first to leave a cluster, he or she can use this as a signal to predict a drop in volatility in another index.  We show that the detection methodology described in this research would provide investors with ample opportunity to predict cross-index movement in returns and/or volatility and proactively engage in returns and volatility strategies. Additionally, investors can take positions in index options to take advantage of cross index return and/or volatility movements.

 Our results indicate that very similar information drives DJIA and S&P, but only a subset of this information seems to affect the NASDAQ index. We find that these patterns translate into the extent of overlap in days between common clusters across indices and also in the patterns of lead/lag while entering or leaving a cluster.  Our analysis shows that if an investor observes that NASDAQ is in cluster, he or she can expect the DJIA to enter cluster in about 12 days.  In the case of a cluster ending, we find there is roughly 12 to 14 days lag from the time DJIA and/or S&P 500 and NASDAQ following suit.