RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction

ArXiv ID: 2402.10760 “View on arXiv”

Authors: Unknown

Abstract

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC’s generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval’s width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC’s evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.

Keywords: Generative Adversarial Networks, Interval Prediction, Risk Quantification, Stock Prediction, Uncertainty Estimation, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including GANs, statistical inference for interval construction, and attention mechanisms, while also providing concrete backtesting results with specific metrics (95% coverage) on multiple global indices using publicly available data.

  flowchart TD
    A["Research Goal<br>Quantify stock market uncertainty<br>and risk for interval prediction"] --> B["Data Input<br>Publicly available<br>stock indices data"]
    B --> C["RAGIC Model<br>Generative Adversarial Network"]
    C --> D["Generator Components<br>Temporal Module +<br>Risk Module"]
    D --> E["Computational Process<br>Sequence Generation &<br>Statistical Inference"]
    E --> F["Output<br>Risk-sensitive Stock Intervals<br>(95% Coverage, Narrow Width)"]
    F --> G["Key Findings<br>Balanced Accuracy & Informativeness<br>Low Computational Overhead"]