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

被引：25

作者：

Sharma, Dinesh K. ^{[1
]}

Hota, H. S. ^{[2
]}

Brown, Kate ^{[1
]}

Handa, Richa ^{[3
]}

机构：

[1] Univ Maryand Eastern Shore, Princess Anne, MD 21853 USA

[2] Atal Bihari Vajpayee Univ, Bilaspur, India

[3] DP Vipra Coll, Bilaspur, India

来源：

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT | 2022年 / 13卷 / SUPPL 2期

关键词：

Artificial neural networks (ANN); Genetic algorithms (GA); Back propagation neural network (BPANN); Stock market forecasting; SUPPORT VECTOR REGRESSION; TIME-SERIES; PREDICTION; SYSTEM; PRICES; MODEL;

D O I：

10.1007/s13198-021-01209-5

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：828 / 841

页数：14

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