Abstract
In this research, an agent-based model (ABM) of the stock market is constructed to detect the proportion of different types of traders. We model a simple stock market which has three different types of traders: noise traders, fundamental traders, and technical traders, trading a single asset. Bayesian optimization is used to tune the hyperparameters of the strategies of traders as well as of the stock market. The experimental results on Bayesian calibration with the Kolmogorov–Smirnov (KS) test demonstrated that the proposed separate calibrations reduced simulation error, with plausible estimated parameters. With empirical data of the Dow Jones Industrial Average (DJIA) index, we found that fundamental traders account for 9%–11% of all traders in the stock market. The statistical analysis of simulated data can produce the important stylized facts in real stock markets, such as the leptokurtosis, the heavy tail of the returns, and volatility clustering.
Highlights
The study of a complex system has always been of great interest because of thier rich properties and behavior, for example, the solar system [1] or biological systems [2]
An agent-based model (ABM) in the financial market is a class of quantitative models to simulate the decisions and interactions of different traders in order to understand their behaviors and their impact on the market [11]
We studied the stationarity of the stock returns by using an augmented Dickey–Fuller (ADF) unit root test
Summary
The study of a complex system has always been of great interest because of thier rich properties and behavior, for example, the solar system [1] or biological systems [2]. An agent-based model (ABM) in the financial market is a class of quantitative models to simulate the decisions and interactions of different traders in order to understand their behaviors and their impact on the market [11]. An agent-based model is introduced to highlight the stylized facts of the stock market. The results show that Bayesian optimization can propose an optimal set of parameters and reduce simulation error This provides stylized facts to compare with the real market, providing a bench-marking method for calibrating parameters of agent-based stock market. We found that the fundamental traders accounted for 9%–11% of all traders in the stock market These results are consistent with the real-market research [17].
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