The nature of statistical learning theory
The nature of statistical learning theory
Proceedings of the Second International Workshop on Multiple Classifier Systems
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Portfolio selection with fuzzy MCDM using genetic algorithm: application of financial engineering
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
Genetic programming for the prediction of insolvency in non-life insurance companies
Computers and Operations Research
Diverse committees vote for dependable profits
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving robust GP solutions for hedge fund stock selection in emerging markets
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Comparison Study of Credit Scoring Models
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 01
A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Using genetic algorithm to support portfolio optimization for index fund management
Expert Systems with Applications: An International Journal
Selecting valuable stock using genetic algorithm
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
On the robustness of population-based versus point-basedoptimization in the presence of noise
IEEE Transactions on Evolutionary Computation
GP age-layer and crossover effects in bid-offer spread prediction
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Soft memory for stock market analysis using linear and developmental genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using GAs to balance technical indicators on stock picking for financial portfolio composition
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Meta optimization and its application to portfolio selection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Money in trees: How memes, trees, and isolation can optimize financial portfolios
Information Sciences: an International Journal
A simple adaptive algorithm for numerical optimization
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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Stock selection for hedge fund portfolios is a challenging problem that has previously been tackled by many machine-learning, genetic and evolutionary systems, including both Genetic Programming (GP) and Support Vector Machines (SVM). But which is the better? We provide a head-to-head evaluation of GP and SVM applied to this real-world problem, including both a standard comparison of returns on investment and a comparison of both techniques when extended with a "voting" mechanism designed to improve both returns and robustness to volatile markets. Robustness is an important additional dimension to this comparison, since the markets (the environment in which the GP or SVM solution must survive) are dynamic and unpredictable. Our investigation highlights a key difference in the two techniques, showing the superiority of the GP approach for this problem.