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.

我们提出了一种新的模型 RAGIC，通过引入序列生成进行股票间隔预测以更有效地量化不确定性，从而弥补了现有单点预测方法的不足。RAGIC 的生成器包括风险模块和时间模块，通过统计推断生成风险敏感的区间，以反映市场波动性，同时考虑到历史价格趋势和季节性，实现了平衡的性能和准确性，并成功解决了股票市场预测中的挑战。

RAGIC：用于股票区间构建的风险感知生成对抗模型

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

We study the impacts of business cycles on machine learning (ML) predictions.
Using the S&P 500 index, we find that ML models perform worse during most
recessions, and the inclusion of recession history or the risk-free rate does
not necessarily improve their performance. Investigating recessions where
models perform well, we find that they exhibit lower market volatility than
other recessions. This implies that the improved performance is not due to the
merit of ML methods but rather factors such as effective monetary policies that
stabilized the market. We recommend that ML practitioners evaluate their models
during both recessions and expansions.

研究商业周期对机器学习预测的影响，发现大部分衰退期间机器学习模型表现较差，建议机器学习从业者在衰退期和扩张期中评估其模型。