Stock market prediction employing ensemble methods: the Nifty50 index

Title

Stock market prediction employing ensemble methods: the Nifty50 index

Creator

Manjunath C.; Marimuthu B.; Ghosh B.

Description

Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. All rights reserved.

Source

IAES International Journal of Artificial Intelligence, Vol-13, No. 2, pp. 2047-2057.

Date

2024-01-01

Publisher

Institute of Advanced Engineering and Science

Subject

Bagging; Boosting; Fundamental analysis; Machine learning; National stock exchange fifty; Stacking; Stock market forecasting

Coverage

Manjunath C., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Bengaluru, India; Ghosh B., Symbiosis Institute of Business Management, Symbiosis International (Deemed University), Bengaluru, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 20894872

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.11591/ijai.v13.i2.pp2049-2059

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