Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms

Wiesinger, Judith; Sornette, Didier; Satinover, Jeffrey

doi:10.1007/s10614-011-9312-9

Cited by 22 publications

(8 citation statements)

References 33 publications

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“…We can specifically highlight the potential of these simulations to forecast real financial time series via reverse-engineering. A promising recent perspective for such use of ABMs has been highlighted in the field of statistics by [92][93][94]: the agent-based model parameters are constrained to be calibrated and fit real financial time series and then allowed to evolve over a given time period as a basic forecast measure on the original time series used for calibration. With this, one could thus say that ABMs are now reaching Friedman's [95] methodological requirement that a theory must be "judged by its predictive power for the class of phenomena which it is intended to explain.…”

Section: Accuracymentioning

confidence: 99%

A Bright Future for Financial Agent-Based Models

et al. 2018

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The history of research in finance and economics has been widely impacted by the field of Agentbased Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the field of ACE has also received critics by a part of the social science community for its lack of empiricism. Yet recent trends have shifted the weights of these general arguments and potentially given ACE a whole new range of realism. At the base of these trends are found two present-day major scientific breakthroughs: the steady shift of psychology towards a hard science due to the advances of neuropsychology, and the progress of artificial intelligence and more specifically machine learning due to increasing computational power and big data. These two have also found common fields of study in the form of computational neuroscience, and human-computer interaction, among others. We outline here the main lines of a computational research study of collective economic behavior via Agent-Based Models (ABM) or Multi-Agent System (MAS), where each agent would be endowed with specific cognitive and behavioral biases known to the field of neuroeconomics, and at the same time autonomously implement rational quantitative financial strategies updated by machine learning. We postulate that such ABMs would offer a whole new range of realism.

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Section: Accuracymentioning

confidence: 99%

A Bright Future for Financial Agent-Based Models

et al. 2018

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“…The task of calibration is to select values of parameters and receive the model's empirical distribution, which is similar to the real-world market distribution in terms of some pre-selected measures Wiesinger et al (2010). This is the method of reverse engineering in the context of financial time-series.…”

Section: Model Calibrationmentioning

confidence: 99%

Financial Econometrics and Empirical Market Microstructure

Bera

Ivliev

Lillo

2015

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“…An individual with a reference point strategy usually makes his buying and selling decisions according to his subjective evaluation of a given stock, which is called the reference point effect in behavioral finance [ 27 – 31 ]. The models employed in exploring the evolutionary dynamics of complex financial systems have so far been limited to the population with a typical kind of investment strategies [ 32 – 38 ]. The coupled effects of different kinds of investment strategies on the evolution of complex behaviors in financial systems, especially their competitive advantage under different market environments, are short of discussion in depth.…”

Section: Introductionmentioning

confidence: 99%

Market impact shapes competitive advantage of investment strategies in financial markets

Liu

2022

PLoS ONE

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The formation of an efficient market depends on the competition between different investment strategies, which accelerates all available information into asset prices. By incorporating market impact and two kinds of investment strategies into an agent-based model, we have investigated the coevolutionary mechanism of different investment strategies and the role of market impact in shaping a competitive advantage in financial markets. The coevolution of history-dependent strategies and reference point strategies depends on the levels of market impact and risk tolerance. For low market impact and low risk tolerance, the majority-win effect makes the trend-following strategies become dominant strategies. For high market impact and low risk tolerance, the minority-win effect makes the trend-rejecting strategies coupled with trend-following strategies become dominant strategies. The coupled effects of price fluctuations and strategy distributions have been investigated in depth. A U-shape distribution of history-dependent strategies is beneficial for a stable price, which is destroyed by the existence of reference point strategies with low risk tolerance. A δ-like distribution of history-dependent strategies leads to a large price fluctuation, which is suppressed by the existence of reference point strategies with high risk tolerance. The strategies that earn more in an inefficient market lose more in an efficient market. Such a result gives us another explanation for the principle of risk-profit equilibrium in financial markets: high return in an inefficient market should be coupled with high risk in an efficient market, low return in an inefficient market should be coupled with low risk in an efficient market.

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Reverse Engineering Financial Markets with Majority and Minority Games Using Genetic Algorithms

Cited by 22 publications

References 33 publications

A Bright Future for Financial Agent-Based Models

A Bright Future for Financial Agent-Based Models

Financial Econometrics and Empirical Market Microstructure

Market impact shapes competitive advantage of investment strategies in financial markets

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