The nature of statistical learning theory
The nature of statistical learning theory
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Modeling chaotic behavior of stock indices using intelligent paradigms
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Boosting the differences: A fast Bayesian classifier neural network
Intelligent Data Analysis
Integrating Ensemble of Intelligent Systems for Modeling Stock Indices
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Weather analysis using ensemble of connectionist learning paradigms
Applied Soft Computing
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Multiobjective evolutionary neural networks for time series forecasting
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Hi-index | 0.00 |
The use of intelligent systems for stock market predictions has been widely established. This paper introduces a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model and a difference boosting neural network. As evident from the empirical results, none of the five considered techniques could find an optimal solution for all the four performance measures. Further the results obtained by these five techniques are combined using an ensemble and two well known Evolutionary Multiobjective Optimization (EMO) algorithms namely Non-dominated Sorting Genetic Algorithm II (NSGA II) and Pareto Archive Evolution Strategy (PAES)algorithms in order to obtain an optimal ensemble combination which could also optimize the four different performance measures (objectives). We considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index as test data. Empirical results reveal that the resulting ensemble obtain the best results.