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Predicting standardized absolute returns using rolling-sample textual modelling
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 12, p e0260132 (2021), PLoS ONE, Vol 16, Iss 12 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science, 2021.
-
Abstract
- Understanding how textual information impacts financial market volatility has been one of the growing topics in financial econometric research. In this paper, we aim to examine the relationship between the volatility measure that is extracted from GARCH modelling and textual news information both publicly available and from subscription, and the performances of the two datasets are compared. We utilize a latent Dirichlet allocation method to capture the dynamic features of the textual data overtime by summarizing their statistical outputs, such as topic distributions in documents and word distributions in topics. In addition, we transform various measures representing the popularity and diversity of topics to form predictors for a rolling regression model to assess the usefulness of textual information. The proposed method captures the statistical properties of textual information over different time periods and its performance is evaluated in an out-of-sample analysis. Our results show that the topic measures are more useful for predicting our volatility proxy, the unexplained variance from the GARCH model than the simple moving average. The finding indicates that our method is helpful in extracting significant textual information to improve the prediction of stock market volatility.
- Subjects :
- Computer and Information Sciences
Asia
Stock Markets
Economics
Science
Social Sciences
Research and Analysis Methods
Geographical locations
Machine Learning
Mathematical and Statistical Techniques
Artificial Intelligence
Statistical Methods
Capital Markets
Investments
Financial Markets
Multidisciplinary
Statistics
Linguistics
Reference Standards
Probability Theory
Probability Distribution
Semantics
Physical Sciences
Medicine
Hong Kong
People and places
Finance
Mathematics
Models, Econometric
Research Article
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 12
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....1c5a25901292156b33d49de8a4482608