  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.
