Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

A Novel Artificial Intelligence-Based Model for Stock Price Prediction

Document Type : Original Article

Authors
1 M.Sc. Student in Financial Management, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran
2 Assistant Professor, Department of Financial Management, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran
Abstract
Predicting stock price movements is a crucial, intriguing, and highly challenging task for researchers, traders, and market analysts. Daily stock price prediction is particularly difficult due to the nonlinear and chaotic nature of stock price fluctuations. Artificial intelligence (AI) techniques have been widely applied to predict data with nonlinear and chaotic structures. Most previous studies have utilized single artificial neural networks (ANNs) along with a limited number of technical indicators for stock price and index prediction. However, relying on a single ANN with fixed input variables often leads to increased prediction errors. Therefore, a more robust model is required to achieve higher prediction accuracy. In this study, a two-stage hybrid framework is proposed. In the first stage (feature selection), the genetic algorithm (GA) and multilayer perceptron (MLP) neural network are employed to identify the most suitable technical indicators. In the second stage (ensemble learning), three predictors namely, the multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and radial basis function (RBF) neural network are combined to forecast stock prices. The final output is obtained by averaging the results of these predictors. Experimental results demonstrate that the proposed model significantly reduces prediction errors compared to individual methods.
Keywords

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Volume 2, Issue 4
Autumn 2020
Pages 225-237

  • Receive Date 04 August 2020
  • Revise Date 12 October 2020
  • Accept Date 23 December 2020