用状态机建模市场周期，提升资产收益预测准确性
Modeling Market States with Clustering and State Machines
- 基于动量与风险特征聚类识别市场状态
- 状态转移矩阵生成混合高斯收益分布，捕捉偏度与峰度
- 适用于量化交易与风险管理，结果稳定可靠
本文提出一种可解释的概率状态机框架，通过在多时间尺度上基于动量和风险特征对历史收益进行聚类，识别出扩张、收缩、危机、复苏等市场状态。基于状态间的转移矩阵构建概率状态机，动态模拟市场演化过程，并生成基于状态频率加权的混合高斯收益分布。实验表明，该方法在捕捉资产收益的偏度与峰度等关键统计特性方面显著优于传统方法，且在随机资产与时间段上均表现稳健。
原文摘要 · Abstract (English)
This work introduces a new framework for modeling financial markets through an interpretable probabilistic state machine. By clustering historical returns based on momentum and risk features across multiple time horizons, we identify distinct market states that capture underlying regimes, such as expansion phase, contraction, crisis, or recovery. From a transition matrix representing the dynamics between these states, we construct a probabilistic state machine that models the temporal evolution of the market. This state machine enables the generation of a custom distribution of returns based on a mixture of Gaussian components weighted by state frequencies. We show that the proposed benchmark significantly outperforms the traditional approach in capturing key statistical properties of asset returns, including skewness and kurtosis, and our experiments across random assets and time periods confirm its robustness.
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