RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction
Jingyi Gu, Wenlu Du, Guiling Wang
TL;DR
This work tackles stock market prediction under intrinsic randomness by reframing it as interval prediction and proposing RAGIC, a two-phase GAN framework that generates horizon-aware price sequences and builds risk-sensitive intervals. The generator comprises a risk module guided by volatility signals and a temporal module that captures multi-scale history and seasonality, while a GRU-based critic trains via Wasserstein distance alongside an auxiliary supervised loss. The key contributions include introducing a risk attention mechanism, horizon-wise sequence simulation, and an adaptive, volatility-driven interval construction that achieves consistent 95% coverage with narrow widths across multiple indices. The results demonstrate that RAGIC provides informative intervals with competitive point-prediction performance, offering practical value for risk-aware decision-making in finance and potential downstream trading and portfolio applications.
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.