Comparison of the Efficiency of Statistical Learning Algorithms and Artificial Neural Networks to Predict Stock Prices

International Journal of Mathematical Modelling & Computations

مقاله 5، دوره 12، 4 (Fall) - شماره پیاپی 48، اسفند 2022، صفحه 275-297   

نوع مقاله: Full Length Article

شناسه دیجیتال (DOI): 10.30495/ijm2c.2022.1948716.1241

نویسندگان

Alireza Sadat Najafi¹؛ Soheila Sardar^{* 2}

¹Department of Information Technology Management, Faculty of Management and Social Sciences, Islamic Azad University Tehran North Branch, Tehran, Iran

²Department of Industrial Management, Faculty of management and Social Sciences, Islamic Azad University Tehran North Branch, Tehran, Iran

چکیده

The importance of the capital market in economic development is undeniable through the effective management of capital and the optimal allocation of resources. In this study, according to capital market behaviors and research, Statistical Learning (SL) algorithms compared to Artificial Neural Networks (ANN) to analyze time-series data and predict stock prices have been investigated. In studies to compare methods or provide hybrid models, most statistical learning algorithms are limited and examined without the comparison of other algorithms. In this study, to eliminate this shortcoming by implementing and comparing statistical learning algorithms in the two categories of Regression Learner and Classification Learner, the most efficient algorithm has been identified based on the selected shares and based on the presented parameters. The first category (Regression Learner) includes Linear Regression, Interaction Linear Regression, Robust Linear Regression, Stepwise Linear Regression, Fine Tree, Medium Tree, Coarse Tree, Linear Support Vector Machine (SVM), Quadratic SVM, Cubic SVM, Fine Gaussian SVM, Medium Gaussian SVM, Coarse Gaussian SVM, Ensemble Boosted Trees, Ensemble Bagged Trees, Squared Exponential Gaussian Process Regression, Matern 5/2 Gaussian Process Regression, Exponential Gaussian Process Regression, Rational Quadratic Gaussian Process Regression. The second category (Classification Learner) includes Gaussian, Naive Bayes, K-nearest neighbors. The results show that Regression Learner methods are more effective in predicting the price of selected stocks.

کلیدواژه‌ها

Statistical learning algorithm؛ Artificial neural network؛ Data analysis؛ Capital market؛ Prediction