Deterministic Weight Modification-based Extreme Learning Machine for Stock Price Prediction

Abstract

Background: The prediction of the stock price is considered to be one of the most fascinating and important research and patent topics in the financial sector. Aims: Making more accurate predictions is a difficult and significant task because the financial industry supports investors and the national economy. Objectives: The DWM is used to adjust the connection weights and biases to enhance prediction precision and convergence rate. DWM was proposed as a method to reduce system error by changing the weights of various levels. The methods for predictable changes in weight were provided together with the computational difficulty. Methods: An extreme learning machine (ELM) is a fast-learning method for training a singlehidden layer neural network (SLFN). However, the model's learning process is ineffective or incomplete due to the randomly chosen weights and biases of the input's hidden layers. Hence, this article presents a deterministic weight modification (DWM) based ELM called DWM-ELM for predicting the stock price. Results: The calculated results showed that DWM-ELM had the best predictive performance, with RMSE (root mean square error) of 0.0096, MAE (mean absolute error) of 0.0563, 0.0428, MAPE (mean absolute percentage error) of 1.7045, and DS (Directional Symmetry) of 89.34. conclusion: The experimental results showed that, in comparison to other well-known prediction algorithms, the suggested DWM+ELM prediction model offers better prediction performance. Conclusion: The experimental results showed that, in comparison to other well-known prediction algorithms, the suggested DWM+ELM prediction model offers better prediction performance.