Title: Feature selection for stock price prediction: a critical review

Authors: Binita Kumari; Srikanta Patnaik; Tripti Swarnkar

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India ' Department of Computer Application, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India

Abstract: Stock price prediction has drawn huge attention due to its impact on economic stability. Accurate stock price prediction is highly essential to reduce the risk associated with it so as to decide good investment strategies. There are various factors influencing the prediction of stock indices namely gross margin, exchange rate, inflation rate, relative index and so on. Feature selection plays a vital role in effective and accurate prediction of stock indices. This paper aims to provide a clear review of widely used features affecting the stock price fluctuations, feature selection techniques and prediction models from the recent literature. The study also highlights the future directions in this domain focusing the enhancement of the prediction performance.

Keywords: features; feature selection; stock price prediction.

DOI: 10.1504/IJIE.2023.127236

International Journal of Intelligent Enterprise, 2023 Vol.10 No.1, pp.48 - 72

Accepted: 17 Feb 2020
Published online: 30 Nov 2022 *