发表机构
Scuola Normale Superiore（比萨高等师范学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用深度神经网络和Shapley可解释性方法，从价格-交易数据中提取非线性依赖结构，并提出一个简约的SHAP启发参数模型，在保持可解释性的同时达到与神经网络相当的预测性能。
AI 中文摘要
理解价格与交易的联合动态是市场微观结构的核心，其中收益率和订单流通过非线性和状态依赖机制相互作用。线性模型具有可解释性，但可能遗漏这些效应，而深度神经网络以透明度为代价提高了预测性能。我们将神经网络用作结构发现的工具，而不仅仅是预测工具。一个深度前馈网络在高频收益率和大、小价差股票的有符号成交量上训练，并与线性VAR基准进行比较。神经网络提高了预测性能，尤其是对收益率，揭示了超越线性设定的非线性依赖关系。利用基于Shapley的可解释性，我们表明主要贡献集中在最近的滞后项上。模型隐含的响应与从数据重建的条件平均值一致。然而，与经验平均值不同，神经网络分解将单个回归变量的贡献从总体依赖中分离出来。滞后有符号成交量产生保号和饱和效应，这与非线性价格冲击和订单流持续性一致。滞后收益率作为状态变量：当之前的交易未推动价格时，模型预测沿过去订单流方向延续，而非零收益率则产生衰减或反转。基于这些发现，我们引入了一个简约的受SHAP启发的非线性参数模型。它再现了主要的收益率-成交量依赖关系，优于线性VAR基准，并实现了与神经网络相当的性能。多滞后扩展捕获了残余的长记忆效应，同时保持了可解释性。总体而言，可解释性提供了一条从黑箱预测到具有经济意义的参数化价格和交易动态模型的路径。
英文摘要
Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear and state-dependent mechanisms. Linear models are interpretable but may miss these effects, while deep neural networks improve forecasting at the cost of transparency. We use neural networks as tools for structural discovery rather than only for prediction. A deep feed-forward network is trained on high-frequency returns and signed volumes for large- and small-tick stocks and compared with a linear VAR benchmark. The neural network improves predictive performance, especially for returns, revealing nonlinear dependencies beyond the linear specification. Using Shapley-based explainability, we show that the dominant contributions are concentrated at the most recent lags. Model-implied responses are consistent with conditional averages reconstructed from the data. Unlike empirical averages, however, the neural-network decomposition isolates individual regressor contributions to the aggregate dependence. Lagged signed volume generates sign-preserving and saturating effects, consistent with nonlinear price impact and order-flow persistence. Lagged returns act as state variables: when the previous trade does not move the price, the model predicts continuation in the direction of past order flow, whereas non-zero returns generate attenuation or reversal. Building on these findings, we introduce a parsimonious SHAP-inspired nonlinear parametric model. It reproduces the main return-volume dependencies, outperforms the linear VAR benchmark, and achieves performance comparable to the neural network. A multi-lag extension captures residual longer-memory effects while preserving interpretability. Overall, explainability offers a route from black-box prediction to economically meaningful parametric models of price and trade dynamics.
