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

الموضوعات :

Meysam Doaei ¹ , Seyed Ahmad Mirzaei ² , Mohammad Rafigh ³

1 - Department of Management, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran
2 - Faculty of Management and Accounting, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran
3 - Department of Finance, Esfarayen Branch, Islamic Azad University, Esfarayen, Iran

تاريخ الإرسال : 11 الخميس , ذو القعدة, 1441 تاريخ التأكيد : 18 الأحد , جمادى الثانية, 1442 تاريخ الإصدار : 24 الجمعة , صفر, 1443

الکلمات المفتاحية: Neural Networks, Stock Market Forecasting, Metaheuristic Algorithms,

ملخص المقالة :

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 18 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.

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