Improved optimization model for forecasting stock directions (FSD)

Abstract

The study of stock market price predictions is very important. The Recurrent Neural Network (RNN) has shown excellent results with this issue. There are two significant problems with using this strategy. One is that it constantly struggles with extensive neural network construction efforts and hyper-parameter adjustments. Two, it often fails to come up with a superior answer. The suggested model is proposed to optimize the network topology and hyper-parameters of the RNN model. RNN is utilized for effective forecasting of stock directions in this research. Additionally, the Improved Differential Evolution (IDE) method is used to tune the RNN's hyperparameters to their best potential. Utilizing the IDE method helps in achieving the best stock direction prediction results possible. The direction of Stock Prediction (SP) changes has been accurately predicted by the proposed model that is being presented. A series of tests on two popular benchmark datasets (AAPL and FB) revealed the superiority of the proposed model over the other strategies with accuracy of 99.02% and the loss close of 0.1% for training and testing.

Keywords:

• Stock prices
• prediction model
• direction forecasting
• deep learning
• hyperparameter optimizers
• differential evolution
• RNN

JEL CODES:

• G1; M; O

Disclosure statement

No potential conflict of interest was reported by the authors.