An Analysis of Extreme Price Shocks and Illiquidity Among Systematic Trend Followers

Lee, Bernard T.; Cheng, Shih-Fen; Koh, Annie

doi:10.2139/ssrn.1546458

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…For example, in [3], the platform allows human traders to participate, and the simulation platform is basically a training ground: exposing human traders to controlled and realistic market scenario so that they can practice their trading skills. A very different application is discussed in [8], where the same platform is used for policy analysis, allowing policy maker to evaluate the effectiveness of different regulation changes under different market composite. In this application, there is no human trader and the market only includes autonomous software agents with different strategies.…”

Section: Discussionmentioning

confidence: 99%

“…For example, instead of having to wait for days or even weeks for marketchanging events, a scenario could be easily designed to contain a year worth of important events within an hour of simulation time. Extreme market conditions that are rare but important could also be easily designed to develop participant's crisis management ability or test emergency market rescue measures [8]. As demonstrated by the case studies presented in [3] and [8], this platform enables us to design highly effective training programs for novice traders and effective simulations for policy analysis.…”

Section: Introductionmentioning

confidence: 99%

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Event Study Approach for Validating Agent-Based Trading Simulations

Cheng

2010

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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Abstract-In this paper, we introduce how one can validate an event-centric trading simulation platform that is built with multi-agent technology. The issue of validation is extremely important for agent-based simulations, but unfortunately, so far there is no one universal method that would work in all domains. The primary contribution of this paper is a novel combination of event-centric simulation design and event study approach for market dynamics generation and validation. In our event-centric design, the simulation is progressed by announcing news events that affect market prices. Upon receiving these events, event-aware software agents would adjust their views on the market and act accordingly. Their actions would be based on their roles and also their private information, and collectively the market dynamics will be shaped. The generated market dynamics can then be validated by a variant of the event study approach. We demonstrate how the methodology works with several numerical experiments and conclude by highlighting the practical significance of such simulation platform.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event Study Approach for Validating Agent-Based Trading Simulations

Cheng

2010

2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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“…Human experiment results have been used to improve or change the system of study or understand the effect of changes in the system circumstances based on the human behavior results. [46][47][48][49] Also, ABM has been used to support human subject experiment results or can be used as the system of study when using human subject experiment is not appropriate. 48,[50][51][52] When discussing ABM, we would like to highlight another agent-based oriented model called multi-agent system (MAS) that is sometimes confused with ABM.…”

Section: Abmmentioning

confidence: 99%

Computerized agents versus human agents in finding core coalition in glove games

2022

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One of the challenges for agent-based modeling is being able to incorporate human behavior. Human behavior is a multifaceted phenomenon, with strategic coalition formation being one form. A hybrid agent-based modeling approach, called ABMSCORE, has been derived to emulate strategic group formation. In this paper, we describe a simulation experiment to compare the ABMSCORE with actual human behavior. The comparison criterion is the respective rates of finding an ideal coalition. In our experimental design, we go to great lengths to ensure the similarity of the scenarios in the two trial types: trials with computerized agents only and trials involving human participants when one of the computerized agents is replaced by an actual human. We did this to limit the number of possible extraneous variables introduced into the experimental system. The scenario considered is the glove game, a standard cooperative game that has been previously used in human experiments. Our results indicate that the ABMSCORE model produces similar rates of finding the ideal coalition as the human players; however, there are some limitations. This research provides evidence for using the ABMSCORE modeling approach to model human strategic coalition formation in agent-based models.

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“…Within agent-based modeling, human subject experimentation has been used to understand human behavior in multiple contexts like emergency evacuation [31], resource allocation system [32], commodity market [33], knowledge management [34], etc. In most cases, researchers aimed to study two phenomena: (1) the effect of human behaviors for development or change in the system of study and (2) the effect of the system of study circumstances on human behavior.…”

Section: Agent-based Modelingmentioning

confidence: 99%

Interactive Agent-Based Simulation for Experimentation: A Case Study with Cooperative Game Theory

Collins

Etemadidavan

2021

Modelling

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Incorporating human behavior is a current challenge for agent-based modeling and simulation (ABMS). Human behavior includes many different aspects depending on the scenario considered. The scenario context of this paper is strategic coalition formation, which is traditionally modeled using cooperative game theory, but we use ABMS instead; as such, it needs to be validated. One approach to validation is to compare the recorded behavior of humans to what was observed in our simulation. We suggest that using an interactive simulation is a good approach to collecting the necessary human behavior data because the humans would be playing in precisely the same context as the computerized agents. However, such a validation approach may be suspectable to extraneous effects. In this paper, we conducted a correlation research experiment that included an investigation into whether game theory experience, an extraneous variable, affects human behavior in our interactive simulation; our results indicate that it did not make a significant difference. However, in only 42 percent of the trials did the human participants’ behavior result in an outcome predicted by the underlying theory used in our model, i.e., cooperative game theory. This paper also provides a detailed case study for creating an interactive simulation for experimentation.

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An Analysis of Extreme Price Shocks and Illiquidity Among Systematic Trend Followers

Cited by 10 publications

References 6 publications

Event Study Approach for Validating Agent-Based Trading Simulations

Event Study Approach for Validating Agent-Based Trading Simulations

Computerized agents versus human agents in finding core coalition in glove games

Interactive Agent-Based Simulation for Experimentation: A Case Study with Cooperative Game Theory

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