An interesting new research paper published in The New Journal of Physics presents a new network model which helps identify clusters of investors and could lead to the identification of trading patterns and strategies that collectively determine stock price.

Via PhysOrg:

The stock price of a company continuously changes, going up or down depending on the collective activity of a large number of investors. Although this process seems fairly straightforward, no one fully understands how this collective trading activity finds the "correct" price of a stock. Some theoretical models have been proposed to describe how different investment strategies affect price dynamics, but challenges such as investor confidentiality and complicated data mining make it difficult to gather empirical support for these models.

Now in a new study, with access to a database of thousands of investors’ trading activity of Finnish stocks, researchers have developed a network that allows them to identify investor clustering, or groups of investors that trade in a similar way. Clustering, which can also be thought of as herding, may eventually lead to the identification of trading patterns and strategies that collectively determine stock price.

The team of researchers, Michele Tumminello, Fabrizio Lillo, Jyrki Piilo, and Rosario N. Mantegna, working at Palermo University in Palermo, Italy; Carnegie Mellon in Pittsburgh, Pennsylvania; Scuola Normale Superiore di Pisa in Pisa, Italy; the Santa Fe Institute in Santa Fe, New Mexico; and the University of Turku in Turun yliopisto, Finland, have published their study on identifying investor clustering in a recent issue of the New Journal of Physics.

Abstract

We use statistically validated networks, a recently introduced method of validating links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing us to track the trading activity of individual investors of Nokia stock. We find that many statistically detected clusters of investors show a very high degree of synchronization in time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and find that several of them show an over-expression of specific categories of investors.