Paper: arXiv 2609.25617

Authors: Hengyi Yang, Sida Lin, Yiyan Qi, Yankai Chen, Haohan Zhang, Xianhua Peng, Jian Guo

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a novel hierarchical multi-task learning framework with a strong emphasis on integrating liquidity-aware signals, which is a significant contribution. The empirical rigor is high, with extensive experiments and realistic backtests on established benchmarks. The methodology is clearly articulated, and the provision of code enhances reproducibility.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve Stock Price Forecasting] --> B{Problem: Conflated Inter/Intra-stock dynamics, Univariate objective};
    B --> C[Methodology: Hierarchical Multi-Task Learning (LiMT) with Liquidity-Aware Signals];
    C --> D{LiMT Components:};
    D --> D1[Market Regime Encoder (MRE): Cross-stock dependencies & Temporal dynamics];
    D --> D2[Liquidity-Driven Learning (LDL): Mixture-of-experts for Price, Volatility, Volume];
    D1 & D2 --> E[Adaptive Portfolio Optimization (APO): Converts forecasts to portfolio weights];
    E --> F[Outcomes: Improved Return & Sharpe Ratio in Backtests];
    F --> G[Key Findings: LiMT outperforms baselines; APO generates deployable gains];

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