COMPUTATIONAL SOCIAL SCIENCE – The Quest for Living Beta: Investigating the Implication of Shareholder Networks – Oldham

When:
September 15, 2017 @ 3:00 pm – 4:30 pm
2017-09-15T15:00:00-04:00
2017-09-15T16:30:00-04:00
Where:
Center for Social Complexity Suite 3rd Floor, Research Hall, Fairfax Campus
Cost:
Free
Contact:
Karen Underwood
7039939298

COMPUTATIONAL SOCIAL SCIENCE FRIDAY SEMINAR

Matthew Oldham, PhD Student
Computational Social Science Program
Department of Computational and Data Sciences
George Mason University

The Quest for Living Beta: Investigating the Implication of Shareholder Networks

Friday, September 15,  3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall

The behavior of financial markets has, and continues, to frustrate investors and academics. With the advent of new approaches, including complex systems and network analysis, the search for an explanation as to how and why markets behavior as they do has greatly expanded, and moved away from the tradition neoclassical approaches that have been beholden to the Efficient Market Hypothesis.

The complex system approach utilizes a number of a concepts in an attempt to understand stock market returns including; imitation, herding, self-organized co-operativity, and positive feedbacks, with many of these features captured by network analysis. In addition, with the meteoric rises of network science has come the realization that the behavior of a system can vary greatly depending on the network structure (the topology) of a system, thus providing further impetus for the use of network analysis in terms of financial markets.

My presentation will detail my recent research of the US Institutional shareholder networks over the period of 2007-10, a period which includes the beginning of the Global Financial Crisis. The research utilized an extensive dataset provided from the Thomson Reuters 13f database, to undertake a temporal analysis of the networks formed between US institutional investors and the stocks in the S&P 500. The analysis makes use of both projected and bipartite networks and uncovers numerous insights regarding relationships between the market in general, stocks and their shareholders. In addition, I will illustrate how the findings can be used in conjunction with an agent-based model to uncover the workings of the stock market.