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

TL;DR

RAGIC introduces a risk-aware generative adversarial model for stock interval prediction, effectively capturing market uncertainty and risk perception to produce accurate, informative intervals with high coverage and low computational cost.

Contribution

The paper presents a novel GAN-based model that generates risk-sensitive stock price intervals, integrating risk perception and historical trends for improved market prediction.

Findings

Achieves 95% coverage with narrow intervals

Balances accuracy and informativeness effectively

Operates with low computational overhead

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.