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 was published in Journal of Economic Dynamics and Control, and a copy can be found here.
Despite considerable efforts, the determinants of firm growth and financial market volatility have not been definitively identified, yet sets of stylized facts – most notably power-law distributions – relating to firm size and market returns suggest both evolve as part of a complex system. This scenario implies that a positive feedback loop between firms and investors exists and may be responsible for prejudicing the way management allocates their resources, with firm, and economic, growth adversely affected. Further, there are growing real-world concerns that the management of publicly listed firms is becoming too concerned with the movement of their firm’s share price, which is adversely influencing resource allocation decisions. A related concern is that agents, in general, within financial markets are placing a disproportionate focus on short-term factors. To investigate the ramifications of the proposed feedback loop on firm growth and market volatility, this paper implements a novel agent-based artificial stock market where management can consider the movements of their firm’s endogenously determined share price when allocating resources between sales and margin growth. The results highlight an inferior outcome regarding firm growth, and various other financial metrics, if management is overly concerned with share price movements. The growth of the firms (and market) is also affected by the mixture of the investor classes initiated due to the divergent levels of volatility they create. Additionally, the model presents insights into how and why the extent to which agents consider past outcomes in their decision-making process becomes influential. Notably, the model’s results emulate an extensive set of global micro-level firm data. By providing significant insights on the effects of the stock market on management decision-making and its ramifications for firm growth, this paper provides crucial insights into the mechanisms responsible for inefficient behavior by market participants.
This paper was published in Complexity and a copy can be found here.
The unexplained and inconsistent behavior of financial markets provides the motivation to engage interdisciplinary approaches to understand its intricacies better. A proven approach is to consider investors as heterogeneous interacting agents who form information networks to inform their investment decisions. The rationale is that the topology of these networks has contributed to a better understanding of the erratic behavior of financial markets. Introducing investor heterogeneity also allows researchers to identify the characteristics of higher performing investors and the implications of investors exhibiting short-termism, a feature recognized by some as detrimental to the performance of the economy. To address these topics, an agent-based artificial stock market is implemented, where investors utilize various information sources, including advice from investors in their network, to inform their investment decisions. Over time investors update their trust in their information sources and evolve their network by connecting to outperforming investors – Oracles – and discarding poor advisers, thereby simulating the evolution of an investor network. The model’s most significant finding is uncovering how the market’s behavior is materially affected by the time-horizon of investors, with short-term behavior resulting in greater volatility in the market. Another finding is the reason why short-term investors generally outperform their long-term counterparts, particularly in more volatile environments. By providing significant insights into the formation of an investor network and its ramifications for market volatility and wealth creation (destruction) this paper provides crucial clues regarding the empirical data that needs to be collected, assessed, and tracked to ensure policymakers and investors better understand the dynamics of financial markets.
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.
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.
The growth of sports analytics (SA) has raised numerous research topics across a variety of sports, including basketball. Agent-based modeling (ABM) has great potential to assist and inform SA, but to date it has not been utilized. To support the use of ABM in SA, a model of a basketball game, which considers most fundamentals of play, is presented. Additionally, player behavior is partially predicated on assessing the length of a player’s shooting streak (testing the “hot-hand” effect) and the consideration a team gives to a streak and their franchise player. The model’s output is used to calibrate and validate it against statistics from the National Basketball Association (NBA). Via a set of experiments, the model indicates that an increased belief in the franchise player leads to increased scoring action, but a belief in the hot-hand a minor effect. Thereby, demonstrating the utility of ABM to SA, thus opening a new research field.
A copy of the paper can be found here.
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.