Stock price prediction based on chaotic hybrid particle swarm optimization-RBF neural network

2017 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Sainan Wang ◽  
Luda Wang ◽  
Shouping Gao ◽  
Zhi Bai
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Stock Price ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Hybrid Particle Swarm Optimization

A Neuro-Fuzzy Expert System Trained by Particle Swarm Optimization for Stock Price Prediction

2012 ◽  
pp. 633-650
Author(s):  
Mohammad Hossein Fazel Zarandi ◽  
Milad Avazbeigi ◽  
Meysam Alizadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Stock Market ◽  
Soft Computing ◽  
Stock Price ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Neuro Fuzzy ◽  
Input Variables

In today’s competitive markets, prediction of financial variables has become a critical issue. Especially in stock market analysis where a wrong prediction may result in a big loss in terms of time and money, having a robust prediction is a crucial issue. To model the chaotic, noisy, and evolving behavior of stock market data, new powerful methods should be developed. Soft Computing methods have shown a great confidence in such environments where there are many uncertain factors. Also it has been observed through many experiments that the hybridization of different soft computing techniques such as fuzzy logic, neural networks, and meta-heuristics usually results in better results than simply using one method. This chapter presents an adaptive neuro-fuzzy inference system (ANFIS), trained by the particle swarm optimization (PSO) algorithm for stock price prediction. Instead of previous works that have emphasized on gradient base or least square (LS) methods for training the neural network, four different strategies of PSO are implemented: gbest, lbest-a, lbest-b, and Euclidean. In the proposed fuzzy rule based system some technical and fundamental indexes are applied as input variables. In order to generate membership functions (MFs), a robust noise rejection clustering algorithm is developed. The proposed neuro-fuzzy model is applied for an automotive part-making manufactory in an Asia stock market. The results show the superiority of the proposed model in comparison with the available models in terms of error minimization, robustness, and flexibility.

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