基于词嵌入的财务信息披露情绪IR模型的波动预测
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
摘要：
波动性预测-金融市场中的一个基本概念-最近已使用情感分析方法加以处理。我们研究了股票市场中公司年度披露的情绪，以预测波动性。我们专门探讨使用最近的信息检索（IR）的长期加权模型，有效地扩展了相关的条款，使用词嵌入。与文本信息平行，事实市场数据已被广泛用作预测市场风险的主流方法。因此，我们研究了不同的融合方法来结合文本和市场数据资源。我们基于词嵌入的方法显著优于最先进的方法。此外，我们还研究了不同金融行业公司的报告特征。
英文摘要：
Volatility prediction--an essential concept in financial markets--has recently been addressed using sentiment analysis methods. We investigate the sentiment of annual disclosures of companies in stock markets to forecast volatility. We specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings. In parallel to textual information, factual market data have been widely used as the mainstream approach to forecast market risk. We therefore study different fusion methods to combine text and market data resources. Our word embedding-based approach significantly outperforms state-of-the-art methods. In addition, we investigate the characteristics of the reports of the companies in different financial sectors.
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