The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
A Web-Based Information System for Stock Selection and Evaluation
WECWIS '99 Proceedings of the International Workshop on Advance Issues of E-Commerce and Web-Based Information Systems
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Expert Systems with Applications: An International Journal
Soft computing techniques applied to finance
Applied Intelligence
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
A double-stage genetic optimization algorithm for portfolio selection
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Cost functions and model combination for VaR-based asset allocation using neural networks
IEEE Transactions on Neural Networks
Stock index tracking by Pareto efficient genetic algorithm
Applied Soft Computing
A hybrid OC-GA approach for fast and global truss optimization with frequency constraints
Applied Soft Computing
Implementing support vector regression with differential evolution to forecast motherboard shipments
Expert Systems with Applications: An International Journal
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In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA-SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.