A Historical Data Based Ensemble System for Efficient Stock Price Prediction

Title:A Historical Data Based Ensemble System for Efficient Stock Price Prediction

Volume: 14 Issue: 4

Author(s): Vijay K. Dwivedi*Manoj M. Gore

Affiliation:

• Department of Computer Science and Engineering, MNNIT Allahabad, Prayagraj-211004,India

Keywords: Accurate stock price prediction, efficient stock price prediction, ensemble system, historical data, LASSO based prediction, least absolute shrinkage and selection operator, stock price prediction.

Abstract:

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems.

Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction.

Methods: The proposed system combines various machine learning-based prediction models employing Least Absolute Shrinkage and Selection Operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models.

Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system.

Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

Cite this article as:

Dwivedi K. Vijay *, Gore M. Manoj , A Historical Data Based Ensemble System for Efficient Stock Price Prediction, Recent Advances in Computer Science and Communications 2021; 14 (4) . https://dx.doi.org/10.2174/2213275912666190730161807