Joint Latent Topic Discovery and Expectation Modeling for Financial Markets

ArXiv ID: 2307.08649 “View on arXiv”

Authors: Unknown

Abstract

In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks. However, current relational stock methods are limited by their reliance on predefined stock relationships and the exclusive consideration of immediate effects. To address these limitations, we present a groundbreaking framework for financial market analysis. This approach, to our knowledge, is the first to jointly model investor expectations and automatically mine latent stock relationships. Comprehensive experiments conducted on China’s CSI 300, one of the world’s largest markets, demonstrate that our model consistently achieves an annual return exceeding 10%. This performance surpasses existing benchmarks, setting a new state-of-the-art standard in stock return prediction and multiyear trading simulations (i.e., backtesting).

Keywords: Stock Return Prediction, Relational Models, Deep Learning, Backtesting, Latent Relationships

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including LSTMs, attention mechanisms, and Tanimoto coefficients for latent topic discovery and expectation modeling, indicating high mathematical density. It demonstrates strong empirical rigor by testing on the large CSI 300 dataset, reporting specific metrics like >10% annual return, and comparing against 16 established baselines on the Qlib platform.

  flowchart TD
    A["Research Goal: Improve Stock Return Prediction<br>by modeling latent relationships & expectations"] --> B["Data Input: CSI 300 Market Data<br>(Stock Prices, Volumes, Financial Reports)"]
    B --> C["Methodology: Joint Latent Topic Discovery<br>& Expectation Modeling Framework"]
    C --> D["Computational Process: Deep Learning<br>Model Training & Optimization"]
    D --> E{"Backtesting & Evaluation"}
    E -->|Performance Metrics| F["Key Findings & Outcomes"]
    F --> G["Annual Return > 10%"]
    F --> H["New State-of-the-Art Standard<br>Surpasses Benchmarks"]