Genetic Algorithm Optimization of an Artificial Neural Network for Financial Applications
Model discovery and performance surface optimization with genetic algorithm demonstrate profitability improvement with an inconclusive effect on statistical criteria. The examination of relationships between statistics used for economic forecasts evaluation and profitability of investment decisions reveals that only the ‘degree of improvement over efficient prediction’ shows robust links with profitability. If profits are not observable, this measure is proposed as an evaluation criterion for an economic prediction. Also combined with directional accuracy, it could be used in an estimation technique for economic behavior, as an alternative to conventional least squares.
KeywordsGenetic Algorithm Transaction Cost Trading Strategy Performance Surface Sharpe Ratio
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- G. Leitch and E. Tanner, "Economic Forecast Evaluation: Profits Versus the Conventional Error Measures," American Economic Review, vol. 81, pp. 580–590, 1991.Google Scholar
- S.-H. Chen and Y.-C. Huang, "Simulating the Evolution of Portfolio Behavior in a Multiple-Asset Agent-Based Artificial Stock Market," presented at the 9th International Conference on Computing in Economics and Finance, University of Washington, Seattle, USA, 2003.Google Scholar
- S. Bhattacharyya and K. Mehta, "Evolutionary Induction of Trading Models," in Evolutionary Computation in Economics and Finance, Studies in Fuzziness and Soft Computing, S.-H. Chen, Ed.: Physica-Verlag, 2002, pp. 311–331.Google Scholar