A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices

Authors:
Ajit Kumar Rout;Birendra Biswal;Pradipta Kishore Dash
Affiliations:
G.M.R. Institute of Technology, Rajam, Andhra Pradesh, India;G.M.R. Institute of Technology, Rajam, Andhra Pradesh, India;Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, India
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems
Year:
2014

Citing 10
Cited 0

Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index

Computers and Operations Research - Special issue: Emerging economics
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Development and performance evaluation of FLANN based model for forecasting of stock markets

Expert Systems with Applications: An International Journal
Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

Expert Systems with Applications: An International Journal
Nonlinear system control using adaptive neural fuzzy networks based on a modified differential evolution

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange

Expert Systems with Applications: An International Journal
Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

Knowledge-Based Systems
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On self-adaptive features in real-parameter evolutionary algorithms

IEEE Transactions on Evolutionary Computation
Functional-link net computing: theory, system architecture, and functionalities

Computer

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a computationally efficient functional link artificial neural network CEFLANN based adaptive model for financial time series prediction of leading Indian stock market indices. Financial time-series data are usually non-stationary and volatile in nature. The proposed adaptive CEFLANN based model employs the least mean square LMS algorithm with a new cost function to train the weights of the networks. The mean absolute percentage error MAPE with respect to actual stock prices is selected as the performance index to estimate the quality of prediction. The CEFLANN model inputs are chosen from the past stock prices of different market sectors along with technical indicators to determine best stock trend prediction one day ahead in time. Further to improve the performance of the CEFLANN model, weights are optimized using an adaptive differential evolution DE algorithm and its overall prediction performance is compared with the improved LMS algorithm showing the effectiveness of the DE in producing more accurate forecast. We have selected different combinations of important technical indicators to have a strong control on changes in stock indices.