Modeling and analysis of an agent-based model for Chinese stock market

2013 ◽  
Vol 377 (34-36) ◽  
pp. 2041-2046 ◽  
Author(s):  
Chun-Xia Yang ◽  
Rui Wang ◽  
Sen Hu
Keyword(s):  
Stock Market ◽  
Chinese Stock Market ◽  
Agent Based Model ◽  
Modeling And Analysis ◽  
Agent Based

scholarly journals Anchoring Heuristics, Investor Sentiment and Stylized Facts in the Stock Market: An Agent Based Model

2020 ◽  
Vol 10 (01) ◽  
pp. 198-217
Author(s):  
Hermes Yukio Higachi ◽  
Ana Cristina Cruz de Faria ◽  
Adriana Sbicca ◽  
Jefferson Kato
Keyword(s):  
Stock Market ◽  
Investor Sentiment ◽  
Agent Based Model ◽  
Stylized Facts ◽  
Agent Based

CROSS-MARKET FINANCIAL RISK ANALYSIS: AN AGENT-BASED COMPUTATIONAL FINANCE

2011 ◽  
Vol 10 (03) ◽  
pp. 563-584 ◽  
Author(s):  
XIONG XIONG ◽  
MEI WEN ◽  
WEI ZHANG ◽  
YONG JIE ZHANG
Keyword(s):  
Stock Market ◽  
Futures Market ◽  
Market Volatility ◽  
Computational Finance ◽  
Future Market ◽  
Investment Strategies ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Agent Based ◽  
Index Futures

Using the method of agent-based computational finance, this paper designs ten experiments to examine the impacts of the index futures market, typical investment strategies, and different trading mechanisms on the volatility of the Chinese stock market, taking into account the behavior of investors. We have the following results. First, the volatility of the stock market decreases with the index future market and cross-market arbitrageurs. Second, different investment strategies have different effects on stock market volatility. In many cases, both market-imitating and stop-loss strategies can increase stock market volatility. Third, the mechanism of price limits for the index futures market can help to stabilize the fluctuation of the stock market.

scholarly journals Validation and Calibration of an Agent-Based Model: A Surrogate Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Zhang ◽  
Zhe Li ◽  
Yongchao Zhang
Keyword(s):  
Surrogate Model ◽  
Original System ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Chinese Stock Market ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Asset Pricing Model ◽  
Remarkable Reduction

Agent-based modelling has been proved to be extremely useful for learning about real world societies through the analysis of simulations. Recent agent-based models usually contain a large number of parameters that capture the interactions among microheterogeneous subjects and the multistructure of the complex system. However, this can result in the “curse of dimensionality” phenomenon and decrease the robustness of the model’s output. Hence, it is still a great challenge to efficiently calibrate agent-based models to actual data. In this paper, we present a surrogate analysis method for calibration by combining supervised machine-learning and intelligent iterative sampling. Without any prior assumptions regarding the distribution of the parameter space, the proposed method can learn a surrogate model as the approximation of the original system with a relatively small number of training points, which will serve the needs of further sensitivity analysis and parameter calibration research. We take the heterogeneous asset pricing model as an example to evaluate the model’s performance using actual Chinese stock market data. The results demonstrate the good capabilities of the surrogate model at modelling the observed reality, as well as the remarkable reduction of the computational time for validating the agent-based model.

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