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人工股票市场中的策略演化与复杂性

文章 arXiv papers · 作者: Chun-Xia Yang et al.
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总结

本研究描述了一个多主体人工股票市场，其中主体根据有限的过往价格记忆选择交易。主体的交易策略通过自我学习过程而变化，使模型能够考察随着主体数量增加，市场行为如何演变。

与正态过程相比，模拟产生了更频繁的大幅价格波动。收益率的中心部分呈 Lévy 形态，尾部则近似按指数形式截断。作者还定义了系统复杂度指标，并报告称，随着主体数量增加，系统会从较简单阶段转向较复杂阶段。这些是模拟结果；摘录没有提供对真实市场的校准或模型参数详情，因此其经验适用范围尚不明确。

核心观点

  • 主体根据有限的股票价格历史和不断演变的策略采取行动。
  • 自我学习机制使主体的策略随时间变化。
  • 模拟市场中大幅价格波动事件比正态过程更频繁。
  • 收益率呈现类似 Lévy 的中心分布，尾部近似按指数形式截断。
  • 随着主体数量增长，测得的系统复杂度从较简单阶段转向较复杂阶段。

标签

股票机器学习统计学市场微观结构
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# Study on Evolvement Complexity in an Artificial Stock Market


# Study on Evolvement Complexity in an Artificial Stock Market









An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is Lévy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。