Introducing a Multi-Asset Stock Market to Test the Power of Investor Networks

Oldham, Matthew

doi:10.18564/jasss.3497

Cited by 3 publications

(3 citation statements)

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“…Of most significance is that the investors with the highest connectedness will earn the largest profits from trading more aggressively, and the market's price volatility will be highest in an environment where the investor network exhibits an intermediate level of connectedness and lowest in markets with higher or lower levels of connectedness. The latter suggests that a scale-free topology will exhibit the highest volatility, a theory confirmed in various agent-based models (ABMs) (see [22,27,28]).…”

Section: Introductionmentioning

confidence: 81%

“…Table 3 provides a summary of the baseline settings and parameter changes utilized for the various experiments. The settings were chosen to ensure a level of consistency against Harras & Sornette [37] and Oldham ( [23,27]). These papers reported that as the variable that determines the inclination of the investor to be influenced by their network increases-denoted by c 1 (see (1))-the price series begins to experience increased periods of volatility.…”

Section: Cutting Poor Advisers and Selecting New Advisers (Steps 10a-mentioning

confidence: 99%

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

Oldham

2019

Complexity

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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.

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Section: Introductionmentioning

confidence: 81%

Section: Cutting Poor Advisers and Selecting New Advisers (Steps 10a-mentioning

confidence: 99%

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

Oldham

2019

Complexity

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“…ABM allows testing core sociological theories against simulations 13 with emphasis on heterogeneous, autonomous actors with bounded, spatial information 17 . They provide a framework to understand complex behaviors like those of economic systems 18 , 19 , as well as individual 20 and organizational 21 , 22 decision making processes. These models have been applied for designing distributed systems such as traffic control 23 and energy management 24 .…”

Section: Introductionmentioning

confidence: 99%

Segregation dynamics with reinforcement learning and agent based modeling

Sert

Bar‐Yam

Morales

2020

Sci Rep

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Societies are complex. properties of social systems can be explained by the interplay and weaving of individual actions. Rewards are key to understand people's choices and decisions. For instance, individual preferences of where to live may lead to the emergence of social segregation. In this paper, we combine Reinforcement Learning (RL) with Agent Based Modeling (ABM) in order to address the self-organizing dynamics of social segregation and explore the space of possibilities that emerge from considering different types of rewards. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions inspired on the rules of the Schelling Segregation model and rewards for interactions. Despite the segregation reward, our experiments show that spatial integration can be achieved by establishing interdependencies among agents of different kinds. They also reveal that segregated areas are more probable to host older people than diverse areas, which attract younger ones. Through this work, we show that the combination of RL and ABM can create an artificial environment for policy makers to observe potential and existing behaviors associated to rules of interactions and rewards. The recent availability of large datasets collected from various resources, such as digital transactions, location data and government census, is transforming the ways we study and understand social systems 1. Researchers and policy makers are able to observe and model social interactions and dynamics in great detail, including the structure of friendship networks 2 , the behavior of cities 3 , politically polarized societies 4 , or the spread of information on social media 5. These studies show the behaviors present in the data but do not explore the space of possibilities that human dynamics may evolve to. Robust policies should consider mechanisms to respond to every type of events 6 , including those that are very rare 7. Therefore it is crucial to develop simulation environments such that potentially unobserved social dynamics can be assessed empirically. Agent Based Modeling (ABM) is a generative approach to study natural phenomena based on the interaction of individuals 8 in social, physical and biological systems 9. These models show how different types of individual behavior give rise to emergent macroscopic regularities 10,11 with forecasting capabilities 12. Applications to social systems include the emergence of wealth distributions 13 , new political actors 14 , multipolarity in interstate systems 15 , and cultural differentiation 16 , among other applications 9. ABM allows testing core sociological theories against simulations 13 with emphasis on heterogeneous, autonomous actors with bounded, spatial information 17. They provide a framework to understand complex behaviors like those of economic systems 18,19 , as well as individual 20 and organizational 21,22 decision making processes. These ...

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Quantifying the concerns of Dimon and Buffett with data and computation

Oldham

2020

Journal of Economic Dynamics and Control

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Introducing a Multi-Asset Stock Market to Test the Power of Investor Networks

Cited by 3 publications

References 0 publications

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

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

Segregation dynamics with reinforcement learning and agent based modeling

Quantifying the concerns of Dimon and Buffett with data and computation

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