DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets

TL;DR

DeltaLag introduces a novel deep learning approach that dynamically learns lead-lag relationships in financial markets, improving prediction accuracy and trading performance over traditional and static methods.

Contribution

It is the first end-to-end deep learning model to discover and exploit dynamic, pair-specific lead-lag patterns for portfolio construction in finance.

Findings

Outperforms fixed-lag and self-lead-lag baselines.

Surpasses precomputed statistical lead-lag graphs.

Achieves better trading performance and interpretability.

Abstract

The lead-lag effect, where the price movement of one asset systematically precedes that of another, has been widely observed in financial markets and conveys valuable predictive signals for trading. However, traditional lead-lag detection methods are limited by their reliance on statistical analysis methods and by the assumption of persistent lead-lag patterns, which are often invalid in dynamic market conditions. In this paper, we propose \textbf{DeltaLag}, the first end-to-end deep learning method that discovers and exploits dynamic lead-lag structures with pair-specific lag values in financial markets for portfolio construction. Specifically, DeltaLag employs a sparsified cross-attention mechanism to identify relevant lead-lag pairs. These lead-lag signals are then leveraged to extract lag-aligned raw features from the leading stocks for predicting the lagger stock's future return. Empirical evaluations show that DeltaLag substantially outperforms both fixed-lag and self-lead-lag baselines. In addition, its adaptive mechanism for identifying lead-lag relationships consistently surpasses precomputed lead-lag graphs based on statistical methods. Furthermore, DeltaLag outperforms a wide range of temporal and spatio-temporal deep learning models designed for stock prediction or time series forecasting, offering both better trading performance and enhanced interpretability.