Oral Defense of Doctoral Dissertation – Computational Social Science – The Utilization of Computational Social Science for the Benefit of Finance – Matthew Oldham
Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University
Bachelor of Economics with Honours, University of Tasmania, 1995
Master of Arts in Interdisciplinary Studies, George Mason University, 2016
The Utilization of Computational Social Science
for the Benefit of Finance
Wednesday, April 24, 2019, 9:00 a.m.
Exploratory Hall, Room 3301
All are invited to attend.
Robert Axtell, Chair
The ability to identify the mechanisms responsible for the behavioral characteristics of financial markets has remained an elusive pursuit. Further, the precise behavioral characteristics of financial markets remains a point of contention. Some practitioners proclaim that markets are efficient and the return profile of financial assets follow a Gaussian distributed random walk, while others suggest that markets are not efficient, with returns tending to be heavily skewed and markets record instances of extreme outlying events at a rate more than what the efficient school prescribes. A feasible explanation for why financial markets behave as they do is that they are a complex adaptive system (CAS), an approach where investors and firms are considered heterogeneous interacting agents (HIA), which contrasts against the single representative agent approach utilized in the efficient market (neoclassical economic) paradigm.
Firstly, this dissertation provides an overview of the basis of the efficient market framework (EMF) before presenting the need to pursue alternative methods. The principal alternative discussed is the utilization of Computational Social Science (CSS) tools to consider financial markets as a CAS. The primary impetus for the approach is the statistical imprint of a CAS – power-law distributions – are found in asset returns and various other economic variable related to financial markets, including the distributions of shareholders and firm size. Of the various CSS tools, the remainder of the dissertation presents two agent-based models aimed at addressing a variety of research, yet with a common theme of quantifying the effects of agents placing an increased focus on short-term factors, a phenomenon known as “short-termism.”
The first model considers the effects of investors forming an information network with each other in an agent-based artificial stock market. In turn, agents try and improve their investment performance by adjusting their connections; a process that involves cutting ties with those agents who provide poor quality information and connecting to the betterperforming investors. The crucial elements in the model are the timeframe over which the agents consider their performance; the interval between rewiring their connections; and
their tendency to follow the advice of their connections over other information sources. Through varying the effect of these elements meaningful insights into the dynamics driving the behavior of the financial markets, with the presence of even a small proportion of
short-term investors being responsible for a material increase in market volatility. A similar record occurred after reducing the interval between when investors adjust their information network.
An ambition research agenda underlies the implementation of the second model. The foundation for the model stems from the growing concern that the management of publicly listed firms is becoming preoccupied with the share price of their firm, thereby placing an
increased, and non-optimal, focus on their short-term earnings. To address this issue required the expansion of the existing agent-based artificial stock market approach to include many firms who have their earnings endogenously influenced by the market. To achieve the required expansion, the model has firms maintain growth expectations which they adjust after factoring in their most recent performance against those expectations and the movement in their firm’s share price. Firms also must allocate their limited resources between growing sales or margins. In terms of the investors, the model considers various investment styles, with individual styles and combinations responsible for generating greater volatility in the market and more extreme adjustments by management. Before undertaking the extensions, an extensive set of data relating to the size, growth, and performance of globally listed firms was collected and assessed. Consistent with previous research, the distributions, apart from growth, were heavily skewed. The growth distributions were found to be somewhat consistent with Laplace distributions, which is the existing growth distribution benchmark.