In:
Symmetry, MDPI AG, Vol. 12, No. 1 ( 2020-01-02), p. 89-
Kurzfassung:
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.
Materialart:
Online-Ressource
ISSN:
2073-8994
Sprache:
Englisch
Verlag:
MDPI AG
Publikationsdatum:
2020
ZDB Id:
2518382-5
