Coherence-based multivariate analysis of high frequency stock market values

TL;DR

This paper introduces a coherence-based metric and averaging technique to analyze high frequency stock data, revealing linear relations among stocks without long-term detrending, applied to NYSE data from March 2008.

Contribution

It proposes a novel coherence-based metric combined with averaging for multivariate analysis of high frequency stock data, improving dependency detection over traditional correlation methods.

Findings

Effective detection of linear relations at different times

Application to NYSE high volume stocks in March 2008

Enhanced topological structure analysis of stock prices

Abstract

The paper tackles the problem of deriving a topological structure among stock prices from high frequency historical values. Similar studies using low frequency data have already provided valuable insights. However, in those cases data need to be collected for a longer period and then they have to be detrended. An effective technique based on averaging a metric function on short subperiods of the observation horizon is suggested. Since a standard correlation-based metric is not capable of catching dependencies at different time instants, it is not expected to perform the best when dealing with high frequency data. Hence, the choice of a more suitable metric is discussed. In particular, a coherence-based metric is proposed, for it is able to detect any possible linear relation between two times series, even at different time instants. The averaging technique is employed to analyze a set of 100 high volume stocks of the New York Stock Exchange, observed during March 2008.