Evolutionary Multiobjective Optimization Approach for Evolving Assemble of Intelligent Paradigms for Stock Market Modeling

Abstract

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.