Integration of genetic algorithm with artificial neural network for stock market forecasting

Dinesh K. Sharma (), H. S. Hota (), Kate Brown () and Richa Handa ()
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Dinesh K. Sharma: University of Maryand Eastern Shore
H. S. Hota: Atal Bihari Vajpayee University
Kate Brown: University of Maryand Eastern Shore
Richa Handa: D.P. Vipra College

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 2, No 6, 828-841

Abstract: Abstract Traditional statistical as well as artificial intelligence techniques are widely used for stock market forecasting. Due to the nonlinearity in stock data, a model developed using the traditional or a single intelligent technique may not accurately forecast results. Therefore, there is a need to develop a hybridization of intelligent techniques for an effective predictive model. In this study, we propose an intelligent forecasting method based on a hybrid of an Artificial Neural Network (ANN) and a Genetic Algorithm (GA) and uses two US stock market indices, DOW30 and NASDAQ100, for forecasting. The data were partitioned into training, testing, and validation datasets. The model validation was done on the stock data of the COVID-19 period. The experimental findings obtained using the DOW30 and NASDAQ100 reveal that the accuracy of the GA and ANN hybrid model for the DOW30 and NASDAQ100 is greater than that of the single ANN (BPANN) technique, both in the short and long term.

Keywords: Artificial neural networks (ANN); Genetic algorithms (GA); Back propagation neural network (BPANN); Stock market forecasting (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01209-5

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