Word Vector Models Approach to Text Regression of Financial Risk Prediction

Symmetry

Da-Bai Shen¹  Yu-Ching Yeh²  Hsiang-Yuan Yeh³ 

[1] Department of Accounting, Soochow University, Taipei 11102, Taiwan;Department of Mathematics, Soochow University, Taipei 11102, Taiwan;School of Big Data Management, Soochow University, Taipei 11102, Taiwan;

关键词: word vector model;    text regression;    financial risk prediction;    stock return volatility;   

DOI  :  10.3390/sym12010089

来源: DOAJ

【 摘 要 】

Linking textual information in finance reports to the stock return volatility provides a perspective on exploring useful insights for risk management. We introduce different kinds of word vector representations in the modeling of textual information: bag-of-words, pre-trained word embeddings, and domain-specific word embeddings. We apply linear and non-linear methods to establish a text regression model for volatility prediction. A large number of collected annually-published financial reports in the period from 1996 to 2013 is used in the experiments. We demonstrate that the domain-specific word vector learned from data not only captures lexical semantics, but also has better performance than the pre-trained word embeddings and traditional bag-of-words model. Our approach significantly outperforms with smaller prediction error in the regression task and obtains a 4%−10% improvement in the ranking task compared to state-of-the-art methods. These improvements suggest that the textual information may provide measurable effects on long-term volatility forecasting. In addition, we also find that the variations and regulatory changes in reports make older reports less relevant for volatility prediction. Our approach opens a new method of research into information economics and can be applied to a wide range of financial-related applications.

【 授权许可】

Unknown