Identifying Actionable Serial Correlations in Financial Markets

Abstract

Financial markets are complex systems where information processing occurs at multiple levels. One signature of this information processing is the existence of recurrent sequences. In this paper, we developed a procedure for finding these sequences and a process of statistical significance testing to identify the most meaningful ones. To do so, we downloaded daily closing prices of the Dow Jones Industrial Average component stocks, as well as various assets like stock market indices, United States government bonds, precious metals, commodities, oil and gas, and foreign exchange. We mapped each financial instrument to a letter and their upward movements to words, before testing the frequencies of these words against a null model obtained by reshuffling the empirical time series. We then identify market leaders and followers from the statistically significant words in different cross sections of financial instruments, and interpret actionable trends that can be traded upon.