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

Jingyi Gu, Wenlu Du, Guiling Wang
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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.
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RAGIC:股票区间构建的风险意识生成对抗模型
由于市场本身具有随机性,受到众多不可预测因素的影响,预测股票市场结果的努力成果有限。许多现有的预测方法侧重于单点预测,缺乏有效决策所需的深度,而且往往忽略了市场风险。为了弥补这一缺陷,我们提出了一个新颖的模型 RAGIC,它为股票区间预测引入了序列生成,从而更有效地量化不确定性。我们的方法利用生成对抗网络(GAN)生成未来价格序列,并在其中注入金融市场固有的随机性。RAGIC 的生成器包括一个风险模块(捕捉知情投资者的风险意识)和一个时间模块(考虑历史价格趋势和季节性)。这个多方面的生成器通过统计推断,结合远景洞察力,为创建风险敏感区间提供信息。区间的宽度经过仔细调整,以反映市场波动性。重要的是,我们的方法完全依赖于公开数据,计算开销很低。RAGIC 在全球公认的宽基指数中的评估结果表明,它在准确性和信息量方面表现均衡。RAGIC 的覆盖率始终保持在 95%,并且保持了较窄的区间宽度。这一令人鼓舞的结果表明,我们的方法有效地解决了股票市场预测的难题,同时纳入了重要的风险考虑因素。
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