Integration of genetic algorithm with artificial neural network for stock market forecasting
International Journal of System Assurance Engineering and Management
; 13:828-841, 2022.
Article
in English
| ProQuest Central | ID: covidwho-2048611
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.
Computers--Computer Engineering; Artificial neural networks (ANN); Genetic algorithms (GA); Back propagation neural network (BPANN); Stock market forecasting; Mathematical models; Genetic algorithms; Economic forecasting; Artificial intelligence; Prediction models; Artificial neural networks; Securities markets; Neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
International Journal of System Assurance Engineering and Management
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS