Abstract
This paper evaluates the predictability of the heuristic conversion algorithms Moving Average Crossover and Trading Range Breakout in the German stock market. Hypothesis testing and a bootstrap procedure are used to test for predictive ability. Results show that the algorithms considered do not have predictive ability. Further, Genetic Programming is used to adapt the buying and selling rules of the investigated algorithms resulting in a new algorithm. Results show that a genetic programming approach does not lead to good new algorithms. We extend former works by using the Sortino Ratio as a measure of risk, and by applying competitive analysis.
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Mohr, E., Schmidt, G., Jansen, S. (2013). A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictability on the German Market. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_12
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DOI: https://doi.org/10.1007/978-3-642-32726-1_12
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