Understanding How Short‐Termism and a Dynamic Investor Network Affects Investor Returns: An Agent‐Based Perspective

Abstract

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.

Modelling the Diffusion of Investment Decisions on Modular Social Networks

“…In future research, threshold behaviors of the structured investor social network could be considered [20]. In addition, multiple types of investment communities [26], overlapping community structures, or investor social networks with dynamic topology [27] could be studied.…”

Dynamics of Social Influence and Knowledge in Networks: Sociophysics Models and Applications in Social Trading, Behavioral Finance and Business

“…Personal bias also seems to have a rather strong impact on individual behavior. Thus, it seems that financial agents do not always follow a rationalist modus operandi; behavioral short-termism is common [65]. However, despite their bounded rationality [66], agent behavior can still be mathematically modeled and simulated via the appropriate application of ABM principles.…”

Collusion or monitoring? Connected institutional investors and stock price crash risk in China

“…More recent work by Bajo et al (2020) claims that an institutional investor may have several connections with other institutional investors merely because it invests in large companies, for example, those belonging to the S&P500 index. There is increasing evidence to suggest that information diffusion among the investor population influences trading behaviour and firm performance in the financial market (Oldham, 2019; Romano, 2021; Rossi et al, 2018; Wen et al, 2021). However, studies on the impact of investor network embeddedness on stock price crash risk are lacking in the main literature.…”