Hierarchical Multi-Task Learning with Liquidity-Aware Signals for Stock Forecasting

2026-09-21

AI Summary

为解决股票价格预测中的随机性和复杂时间模式问题，提出了一种结合流动性信号的多层次多任务学习框架LiMT，通过市场机制编码器和流动性驱动学习模块来提高预测准确性。

Abstract

Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns. While recent deep-learning models have raised forecasting accuracy by jointly modeling inter-stock and temporal price dynamics, they conflate inter-stock relationships with intra-stock temporal dependencies and focus solely on the univariate objective of price movement. To address these limitations, we propose LiMT, a Hierarchical Multi-Task Learning framework that integrates liquidity-aware signals for stock price forecasting. LiMT employs a Market Regime Encoder (MRE) module that first extracts contemporaneous cross-stock dependencies, then models each stock's temporal dynamics, yielding a unified latent state. Building on this latent state, we introduce a Liquidity-Driven Learning (LDL) module, a mixture-of-experts architecture that features cross-task gating mechanisms to jointly predict price movement, volatility, and trading volume. We further design an Adaptive Portfolio Optimization (APO) mechanism that converts multi-task forecasts into executable portfolio weights under transaction-cost and liquidity constraints. Extensive experiments on the CSI300 and CSI500 benchmarks show that LiMT performs best among strong neural and tree-based baselines across the reported metrics. In realistic CSI300 backtests, APO improves annualized return from 3.99% to 10.01% and Sharpe ratio from 1.22 to 1.86 over equal weighting, showing that the multi-task forecasts translate into deployable portfolio gains.

Problem

• Stock Price Forecasting
• Inter-Stock Relationships
• Temporal Dependencies
• Market Randomness
• Complex Temporal Patterns

Innovation

• Hierarchical Multi-Task Learning
• Liquidity-Aware Signals
• Market Regime Encoder
• Liquidity-Driven Learning
• Adaptive Portfolio Optimization