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.

翻译：受众多不可预测因素影响，股票市场本质上具有随机性，因此预测市场结果的尝试成效有限。现有预测方法多聚焦于单点预测，缺乏有效决策所需的深度，且往往忽视市场风险。为弥合这一差距，我们提出一种新颖模型RAGIC，通过引入序列生成技术进行股票区间预测，更有效地量化不确定性。该方法利用生成对抗网络（GAN）生成蕴含金融市场固有随机性的未来价格序列。RAGIC的生成器包含风险模块（捕捉知情投资者的风险感知）与时序模块（考虑历史价格趋势与季节性因素）。这种多维度生成器通过统计推断，融合跨时间视角的洞察，构建风险敏感的预测区间。区间宽度经过精心调整以反映市场波动性。重要的是，本方法仅依赖公开数据，且计算开销极低。在全球知名宽基指数上的评估表明，RAGIC在准确性与信息量之间实现了均衡表现，在保持95%稳定覆盖率的同时维持较窄的区间宽度。这一令人振奋的结果表明，我们的方法在纳入关键风险考量的同时，有效应对了股票市场预测的挑战。