Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

Authors:
Ritanjali Majhi;G. Panda;Babita Majhi;G. Sahoo
Affiliations:
School of Management, National Institute of Technology, Warangal, India;Dept. of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Orissa 769008, India;Dept. of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Orissa 769008, India;Dept. of Computer Science and Engineering, BIT, Mesra, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 7
Cited 10

Practical neural network recipes in C++

Practical neural network recipes in C++
Data mining: concepts and techniques

Data mining: concepts and techniques
Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting

Neurocomputing
Bacteria Foraging Based Independent Component Analysis

ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Transmission loss reduction based on FACTS and bacteria foraging algorithm

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation

IEEE Transactions on Evolutionary Computation
Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks

IEEE Transactions on Neural Networks

A Multi-strategy Differential Evolution Algorithm for Financial Prediction with Single Multiplicative Neuron

ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Bacterial foraging optimization and tabu search: performance issues and cooperative algorithms

ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
New inspirations in swarm intelligence: a survey

International Journal of Bio-Inspired Computation
A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization

Mathematics and Computers in Simulation
Parametric modelling approach using bacterial foraging algorithms for modelling of flexible manipulator systems

Engineering Applications of Artificial Intelligence
Vehicle routing problem with time windows based on adaptive bacterial foraging optimization

ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
FAST: Fundamental Analysis Support for Financial Statements. Using semantics for trading recommendations

Information Systems Frontiers
An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting

International Journal of Applied Evolutionary Computation
Stock indices prediction using radial basis function neural network

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices

International Journal of Knowledge-based and Intelligent Engineering Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.