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.
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