•  
  •  
 

Abstract

Stock market prediction is an interesting financial topic that has attracted the attention of researchers for the last years. This paper aims at improving the prediction of the Iraq-Stock-Exchange (ISX) using a developed method of feedforward Neural-Networks based on the Quasi-Newton optimization approach. The proposed method reduces the error factor depending on the Jacobian vector and Lagrange multiplier. This improvement has led to accelerating convergence during the learning process. A sample of companies listed on ISX was selected. This includes twenty-six banks for the years from 2010 to 2020. To evaluate the proposed model, the research findings are compared with other standard prediction techniques. It was found that the developed research model outperformed other prediction techniques according to the accuracy and root-mean-secured-error measures.

Q3.jpg (102 kB)
Fig. 1

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS