用几何深度学习捕捉金融市场的分阶段演化特征
Representation Learning for Regime detection in Block Hierarchical Financial Markets
- 基于块层级协方差矩阵的黎曼流形构建模型
- 三类模型在三种数据配置下均出现过拟合现象
- 强调单一指标评估金融模型不可靠性
本文从深层表征学习角度出发，利用块层级协方差结构刻画交易资产系统的因果信息几何，以实现金融市场状态识别。评估了三种尊重输入块层级对称正定（SPD）协方差矩阵黎曼流形结构的模型：SPDNet、SPD-NetBN 和 U-SPDNet。通过三种数据配置进行市场相位检测：随机化南非股市前60大公司数据、合成生成的块层级SPD矩阵，以及保持时间顺序的块重采样南非股市前60大公司数据。结果表明，在金融投资场景中，仅依赖单一性能指标会误导判断，因深度学习模型在学习时空相关性动态时易发生过拟合。
原文摘要 · Abstract (English)
We consider financial market regime detection from the perspective of deep representation learning of the causal information geometry underpinning traded asset systems using a hierarchical correlation structure to characterise market evolution. We assess the robustness of three toy models: SPDNet, SPD-NetBN and U-SPDNet whose architectures respect the underlying Riemannian manifold of input block hierarchical SPD correlation matrices. Market phase detection for each model is carried out using three data configurations: randomised JSE Top 60 data, synthetically-generated block hierarchical SPD matrices and block-resampled chronology-preserving JSE Top 60 data. We show that using a singular performance metric is misleading in our financial market investment use cases where deep learning models overfit in learning spatio-temporal correlation dynamics.
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