Hybrid Multilayer Perceptron Neural Network with Grey Wolf Optimization for Predicting Stock Market Index

  • سال انتشار: 1400
  • محل انتشار: فصلنامه پیشرفتهایی در ریاضیات مالی و کاربردها، دوره: 6، شماره: 4
  • کد COI اختصاصی: JR_AMFA-6-4_014
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 254
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نویسندگان

Meysam Doaei

Department of Management, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran

Seyed Ahmad Mirzaei

Faculty of Management and Accounting, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran

Mohammad Rafigh

Department of Finance, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran

چکیده

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. Artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted ۱۸ technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.

کلیدواژه ها

Neural Networks, Metaheuristic Algorithms, Stock Market Forecasting

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