通过历史情绪周期匹配，提升股市趋势预测准确率。
TopicProphet: Prophesies on Temporal Topic Trends and Stocks
- 基于话题建模与时间断点检测，寻找相似社会情绪的历史时期
- 在真实数据上实现比现有方法更高的金融涨跌幅预测精度
- 适合关注金融时序预测与主题分析交叉研究的读者
股票无法被预测。尽管长期抱有希望，但这一前提因量化股票数据缺乏因果逻辑，以及市场快速变化导致训练数据难以积累而持续成立。为应对该问题，我们提出一种新框架 TopicProphet，通过分析与当前公众情绪趋势和历史背景相似的历史时期，来优化训练数据选择。不同于以往仅关注关键词与情绪影响的研究，本研究引入一系列话题建模、时间序列分析、断点检测与区间优化技术，识别出最适合训练的时间段。该方法使模型能捕捉到特定时代下的经济社会与政治背景带来的细微模式，同时缓解了相关股票数据不足的问题。大量实验证明，TopicProphet在捕捉最优训练数据以预测金融百分比变化方面，优于当前最先进方法。
原文摘要 · Abstract (English)
Stocks can't be predicted. Despite many hopes, this premise held itself true for many years due to the nature of quantitative stock data lacking causal logic along with rapid market changes hindering accumulation of significant data for training models. To undertake this matter, we propose a novel framework, TopicProphet, to analyze historical eras that share similar public sentiment trends and historical background. Our research deviates from previous studies that identified impacts of keywords and sentiments - we expand on that method by a sequence of topic modeling, temporal analysis, breakpoint detection and segment optimization to detect the optimal time period for training. This results in improving predictions by providing the model with nuanced patterns that occur from that era's socioeconomic and political status while also resolving the shortage of pertinent stock data to train on. Through extensive analysis, we conclude that TopicProphet produces improved outcomes compared to the state-of-the-art methods in capturing the optimal training data for forecasting financial percentage changes.
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