E-resources
Peer reviewed
-
Gao, Ruize; Cui, Shaoze; Xiao, Hongshan; Fan, Weiguo; Zhang, Hongwu; Wang, Yu
Information sciences, November 2022, 2022-11-00, Volume: 615Journal Article
In this study, we investigate the predictive capabilities of different news providers based on sentiment analysis, and propose a framework that endows different weights to different news providers for improving the prediction performance. In sentiment analysis, the prevalent Loughran-McDonald sentiment dictionary is utilized to calculate the sentiment scores of news articles, and the sentiment index of each news provider is obtained by integrating these sentiment scores. Based on the market data and sentiment indices of multiple news providers, we employ the recurrent neural network to build a number of base classifiers, and adopt the evidential reasoning rule to combine these base classifiers for predicting the stock market index movement. Additionally, the genetic algorithm is used to optimize the weights of base classifiers and important hyper-parameters of the recurrent neural network. In the experimental study, we apply the proposed approach to the daily movement prediction of the S&P 500 index, Dow Jones Industrial Average index and NASDAQ 100 index, and compare it with some state-of-the-art methods. The results show that our approach is effective for improving the prediction performance. Besides, the designed trading strategy based on the results of the proposed model achieves higher return rates than other trading strategies.
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.