Title: Stock movement prediction using neuro genetic hybrid approach and impact on growth trend due to COVID-19

Authors: Pradeepta Kumar Sarangi; Kalpna Guleria; Devendra Prasad; Deepak Kumar Verma

Addresses: Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Chhatrapati Shahu Ji Maharaj University, Kalyanpur, Kanpur Uttar Pradesh, India

Abstract: Knowing the future perspective is a matter of great concern for every business organisation. Its importance increases when it comes to the matter of financial data related to the stock market. Researchers apply various methods to predict the stock trend but still there is no method that can guarantee the accurate stock movement whereas a nearly accurate approach could be achieved in case of short-term stock movements. Artificial neural network (ANN) is one of the most popular methods to analyse the stock trend and forecast the future direction of the stock market and the most challenging task in using the neural network is the selection of a proper architecture, input parameters and training of the network. This can be overcome by analysing the relationship between independent and dependent factors and also by implementing hybrid models such as ANN with genetic algorithm (GA) for network training. This work has addressed this issue for short term stock prediction and is divided into two phases. In phase-I, experiments have been done to implement a hybrid ANN-GA model for short term stock prediction and in phase-II, a study has been carried out to analyse the impact of the COVID-19 on the share prices of selective major banks in India.

Keywords: time series analysis; neural network; NN; genetic algorithm; GA; financial forecasting; COVID-19 impact.

DOI: 10.1504/IJNVO.2021.120172

International Journal of Networking and Virtual Organisations, 2021 Vol.25 No.3/4, pp.333 - 352

Received: 25 Jul 2020
Accepted: 20 Jan 2021
Published online: 10 Jan 2022 *

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