用新闻影响时长预训练模型，提升股市预测准确率
Pre-Finetuning with Impact Duration Awareness for Stock Movement Prediction
- 构建新闻影响时长数据集IDED，用于预训练语言模型
- 基于IDED预训练使股市走势预测性能显著提升
- 适合关注金融文本分析与时序预测的研究者
理解新闻事件对股市影响的持续时间对于有效的时间序列预测至关重要，但当前研究大多忽视了这一方面。本文提出一个新的数据集——影响时长估计数据集（Impact Duration Estimation Dataset, IDED），专门用于基于投资者观点估计影响持续时间。研究表明，使用IDED对语言模型进行预微调，可显著提升基于文本的股市走势预测性能。此外，将该预微调任务与情感分析预微调对比，进一步验证了学习影响时长的重要性。研究揭示了这一新方向在股市预测中的潜力，并公开提供IDED数据集及预微调语言模型，采用CC BY-NC-SA 4.0许可，供学术研究使用，推动该领域深入探索。
原文摘要 · Abstract (English)
Understanding the duration of news events' impact on the stock market is crucial for effective time-series forecasting, yet this facet is largely overlooked in current research. This paper addresses this research gap by introducing a novel dataset, the Impact Duration Estimation Dataset (IDED), specifically designed to estimate impact duration based on investor opinions. Our research establishes that pre-finetuning language models with IDED can enhance performance in text-based stock movement predictions. In addition, we juxtapose our proposed pre-finetuning task with sentiment analysis pre-finetuning, further affirming the significance of learning impact duration. Our findings highlight the promise of this novel research direction in stock movement prediction, offering a new avenue for financial forecasting. We also provide the IDED and pre-finetuned language models under the CC BY-NC-SA 4.0 license for academic use, fostering further exploration in this field.
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