用于金融技术分析的时间序列推理
Reasoning on Time-Series for Financial Technical Analysis
AI总结：
VTA通过结合语言和潜在推理，生成准确且可解释的股票时间序列预测，展示了在金融技术分析中的优越性能。
摘要：
尽管大型语言模型已被用于生成可解释的股票预测，但它们主要集中在分析文本报告，而非历史价格数据，也称为技术分析。这项任务具有挑战性，因为它涉及两个领域：股票价格输入和输出位于时间序列领域，而推理步骤应位于自然语言领域。在本工作中，我们引入了Verbal Technical Analysis（VTA），一种新颖的框架，结合了语言和潜在推理，以生成既准确又可解释的股票时间序列预测。为了在时间序列上进行推理，我们将股票价格数据转换为文本注释，并通过逆均方误差（MSE）奖励目标优化推理轨迹。为了从文本推理生成时间序列输出，我们使时间序列主干模型的输出依赖于基于推理的属性。在跨美国、中国和欧洲市场的股票数据集上的实验表明，VTA实现了最先进的预测准确性，而推理轨迹在行业专家的评估中也表现良好。
英文摘要：
While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the time-series domain, while the reasoning step should be in natural language. In this work, we introduce Verbal Technical Analysis (VTA), a novel framework that combine verbal and latent reasoning to produce stock time-series forecasts that are both accurate and interpretable. To reason over time-series, we convert stock price data into textual annotations and optimize the reasoning trace using an inverse Mean Squared Error (MSE) reward objective. To produce time-series outputs from textual reasoning, we condition the outputs of a time-series backbone model on the reasoning-based attributes. Experiments on stock datasets across U.S., Chinese, and European markets show that VTA achieves state-of-the-art forecasting accuracy, while the reasoning traces also perform well on evaluation by industry experts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
