by Matthew Oldham
This page provides examples of my work that have been published or that I have presented at a conference. I also provide a quick summary of the rationale for the paper and the key results, just in case you do not wish to read the whole thing.
I have also provided the DOI for the publications so you track down the official copy and cite it properly if you use it.
This paper can be found here: Advances in Complex Systems (World Scientific). The DOI for the paper is 10.1142/S0219525917500072
The inability of investors and academics to consistently predict, and understand the be- havior of financial markets has forced the search for alternative analytical frameworks. Analyzing financial markets as complex systems is a framework that has demonstrated great promises, with the use of agent-based models (ABMs) and the inclusion of network science playing an important role in increasing the relevance of the framework. Using an artificial stock market created via an agent-based model (ABM), this paper provides significant insight into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. The paper demonstrates that the network topology that investors form and the dividend policy of firms significantly affect the behavior of the market. However, if investors have a bias to following their neighbors then the topology becomes redundant. By successfully addressing these issues this paper helps refine and shape a variety of additional research tasks for the use of ABMs in uncovering the dynamics of financial markets.
This paper can be found here: Journal of Artificial Societies and Social Simulation. The DOI for the paper is 10.18564/jasss.3497
The behavior of financial markets has frustrated, and continues to frustrate, investors and academics. By utilizing a complex systems framework, researchers have discovered new fields of investigations that have provided meaningful insight into the behavior of financial markets. The use of agent-based models (ABMs) and the inclusion of network science have played an important role in increasing the relevance of the complex systems to financial markets. The challenge of how best to combine these new techniques to produce meaningful results that can be accepted by the broader community remains an issue. By implementing an artificial stock market that utilizes an Ising model based agent-based model (ABM), this paper provides insights into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. A key finding is that the network topology investors form significantly affects the behavior of the market, with the exception being if investors have a bias to following their neighbors, at which point the topology becomes redundant. The model also investigates the impact of introducing multiple risky assets, something that has been absent in previous attempts. By successfully addressing these issues this paper helps to refine and shape a variety of further research tasks for the use of ABMs in uncovering the dynamics of financial markets.
This paper can be found here and the DOI is 10.1002/isaf.1419. It was published in the Intelligent Systems in Accounting, Finance and Management journal.
Innovation within the economy has been a major driver of increasing incomes, but a complete explanation of what drives the level innovation and the probability of success has remained a challenging topic. One feasible explanation has come from the adaption of the Red Queen hypothesis (Van Valen, 1973) from the field of biology. The Red Queen effect forces firms into an arms race, as a single change by a firm tips the balance of power in the system, with the consequence being firms require continuous development to maintain their relative positions. In such a system, there is no best state nor stable equilibrium, and this renders traditional closed form economic models of little value. A solution is to utilize an agent-based model (ABM) to simulate the environment. This paper details and reports on the findings of an ABM designed to investigate the factors that determine the competitiveness of the Red Queen race and who is likely to win. Key factors that were uncovered include: the influence of market concentration; the ratio of innovators and imitators within the population; the rate of creative destruction: and the margin that firms generate. While some of the outcomes could have been anticipated, it is the model’s ability to quantify the relationships across a broad spectrum that demonstrates the validity of the approach. The findings, in turn, can be used to inform policy makers in terms of competition policy, with the key ramification being that if a market is concentrated, the intentions of the firms within the market need to be understood.
This paper was published in the Journal of Network Theory in Finance
Network science is being increasingly utilized to assist in the search for causes of irregular behavior in financial markets. The search gained greater impetus after traditional finance theories were unable to predict the extent of the most recent global financial crisis. The increased abilities of researchers to access and manipulate data has also opened new avenues of investigation, including the discovery of key networks and the agents that interact within them. In this paper, an analysis of the temporal net- works formed between US institutional investors and Standard & Poor’s 500 stocks between 2007 and 2010 is presented, with the results identifying key relationships between the density of these networks and the movement of the market. The analysis also identified the changing behavior of investors, as their risk aversion varied ahead of the market’s price movements. To a lesser degree, relationships between the return of individual stocks and their investor networks are reported.
The Churchillian quote “Never,in the field of humanconflict, was so much owed by so many to so few”, encapsulates perfectly the heroics of Royal Air Force (RAF) Fighter Command (FC) during the Battle of Britain. Despite the undoubted heroics, questions remain about how FC employed the ‘so few’. In particular, the question as to whether FC should have employed the ‘Big Wing’ tactics, as per 12 Group, or implement the smaller wings as per 11 Group, remains a source of much debate. In this paper, I create an agent-based model (ABM) simulation of the Battle of Britain, which provides valuable insight into the key components that influenced the loss rates of both sides. It provides mixed support for the tactics employed by 11 Group, as the model identified numerous variables that impacted the success or otherwise of the British.
A copy of the paper can be found here. The DOI for the paper is 10.1007/978-3-319-46882-2_5.
I presented my Battle of Britain model at the 17th International conference on multi-agent based simualtions. The presentation can be found here.
My contribution to the 12th Artificial Economics Conference, was what utilimately became my Market Fluctuations Explained by Dividends and Investor Networks paper. The presentation can be downloaded from here.
My contribution to the 2nd Workshop on Statistical Physics of Financial and Economic Networks satellie at NetSci2017 conference can be downloaded here.