Improving Technical Analysis Predictions: An Application of Genetic Programming
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A Comparison of Adaptive and Static Agents in Equity Market Trading
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Dynamic Asset Allocation for Stock Trading Optimized by Evolutionary Computation
IEICE - Transactions on Information and Systems
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows
Computational Optimization and Applications
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Conventional approach in evolutionary technical trading strategies adopted the raw excess returns as the sole performance measure, without considering the associated risk involved. However, every individual has a different degree of risk averseness and thus different preferences between risk and returns. Acknowledging that these two factors are inherently conflicting in nature, this paper considers the multi-objective evolutionary optimization of technical trading strategies, which involves the development of trading rules that are able to yield high returns at minimal risk. Popular technical indicators used commonly in real-world practices are used as the building blocks for the strategies, which allow the examination of their trading characteristics and behaviors on the multi-objective evolutionary platform. While the evolved Pareto front accurately depicts the inherent tradeoff between risk and returns, the experimental results suggest that the positive correlation between the returns from the training data and test data, which is generally assumed in the single-objective approach of this optimization problem, does not necessarily hold in all cases.