Assessment of Hybrid Artificial Neural Networks and Metaheuristics for Stock Market Forecasting

Year 2018, Volume: 27 Issue: 1, 63 - 78, 15.04.2018

Ayşe Tuğba Dosdoğru Aslı Boru , Mustafa Göçken , Mehmet Özçalıcı , Tolunay Göçken

Abstract

Even though a number of stock market
forecasting studies are done related with hybrid Artificial Neural Network
(ANN) models, no standard procedures are available in the literature for each
stock. This causes a growing interest in using metaheuristic for the designing
of appropriate ANN architecture. Therefore, this study used ten different
metaheuristics including Ant Lion Optimization (ALO), Bird Swarm Optimization
(BSA), Differential Evolution (DE), Grey Wolf Optimization (GWO), Moth-Flame
Optimization (MFO), Multi-verse Optimizer (MVO), Particle Swarm Optimization
(PSO), Simulated Annealing (SA), Weighted Superposition Attraction (WSA), and
Firefly Algorithm (FFLY) to improve the performance of the ANN models. Proposed
hybrid ANN models lead to significant opportunities to forecast stock market
more effectively. Based upon results of performance measures, we also expect
hybrid ANN models provide a remarkable solution for other forecasting problems.

Keywords

Stock Market Forecasting, Metaheuristics, Artificial Neural Network

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