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MAXIMIZE PROFIT WHILE MINIMIZING RISK - A FITNESS FUNCTION EVALUATION STUDY USING GENETIC ALGORITHMS

Pinto, J. ; Neves, R.

MAXIMIZE PROFIT WHILE MINIMIZING RISK - A FITNESS FUNCTION EVALUATION STUDY USING GENETIC ALGORITHMS, Proc Jožef Stefan Institute Bioinspired Optimization Methods and their Applications - BIOMA, Bohinj, Slovenia, Vol. NA, pp. 333 - 345, May, 2012.

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Abstract
One of the stock markets key troubles is the related risk. The last decade disastrous sequence of world financial crises has warned the investors that not only the absolute expected return should be considered when selecting assets. This paper presents an approach to optimize an investment strategy based on simple moving averages. The proposed approach tunes the entry and exit points using an evolutionary algorithm. Alternative approaches to classical absolute return fitness functions which consider solely the absolute return are presented. It is shown that the proposed approach outperform both the B&H and the S&H strategies, and also the classical proposals. The enhanced capability of the novel proposed approach is verified to get consistent gains and avoid large draw-downs for the main indexes of the most developed economies, such as: S&P 500, FTSE100, DAX30, NIKKEI225 and also NASDAQ.