Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data

Title

Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data

Creator

Ranjith J.; Chandar S.K.

Description

Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved.

Source

Fusion: Practice and Applications, Vol-16, No. 2, pp. 178-189.

Date

2024-01-01

Publisher

American Scientific Publishing Group (ASPG)

Subject

Explainable artificial intelligence; Machine learning; Multi-source data; Sentiment analysis; Stock market prediction; Technical indicators

Coverage

Ranjith J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 27700070

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.54216/FPA.160211

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201629045&doi=10.54216%2fFPA.160211&partnerID=40&md5=0c98cd96375a5917d38668bb4cb6923c