Book Chapter10.1007/11579427_68
Evolutionary multiobjective optimization approach for evolving ensemble of intelligent paradigms for stock market modeling
Ajith Abraham,Crina Grosan,Sang Yong Han,Alexander Gelbukh +3 more
- 14 Nov 2005
- pp 673-681
TL;DR: A genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices is introduced and empirical results reveal that the resulting ensemble obtain the best results.
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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.
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Citations
A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications
TL;DR: A survey on the state-of-the-art of research, reported in the specialized literature to date, related to this framework, makes a distinction between the (widely covered) portfolio optimization problem and the other applications in the field.
345
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
TL;DR: This paper presents a methodological framework for conducting a comprehensive literature review on the Multiobjective Evolutionary Algorithms (MOEAs) for the Portfolio Management and identifies areas of concern.
195
Stock Market Forecasting Using LASSO Linear Regression Model
Sanjiban Sekhar Roy,Dishant Mittal,Avik Basu,Ajith Abraham +3 more
- 01 Jan 2015
TL;DR: A Least Absolute Shrinkage and Selection Operator (LASSO) method based on alinear regression model is proposed as a novel method to predict financial market behavior and results indicate that the proposed model outperforms the ridge linear regression model.
108
Research and development of neural network ensembles: a survey
TL;DR: Different approaches on the development and the latest studies on NNE are summarized, followed by detailed descriptions of individual neural network generation method, conclusion generation method and fusion based on granular computing and NNE.
93
Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting
TL;DR: A novel methodology for very-short-term load forecasting is introduced in this paper, and its performance is tested on a set of historical, demand-side, 5-min data.
87
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A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
Kalyanmoy Deb,Samir Agrawal,Amrit Pratap,T. Meyarivan +3 more
- 18 Sep 2000
TL;DR: Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front.