A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictability on the German Market

  • Esther MohrEmail author
  • Günter Schmidt
  • Sebastian Jansen
Part of the Studies in Computational Intelligence book series (SCI, volume 447)


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.


Genetic Programming Competitive Ratio Excess Return Trading Rule Genetic Programming Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51(2), 245–271 (1999)CrossRefGoogle Scholar
  2. 2.
    Ammann, M., Zenkner, C.: Tactical asset allocation mit genetischen algorithmen. Schweizerische Zeitschrift für Volkswirtschaft und Statistik 139(1), 1–40 (2003)Google Scholar
  3. 3.
    Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press (1996)Google Scholar
  4. 4.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)zbMATHGoogle Scholar
  5. 5.
    Bessembinder, H., Chan, K.: The profitability of technical trading rules in the Asian stock markets. Pacific-Basin Finance Journal 3(2-3), 257–284 (1995)CrossRefGoogle Scholar
  6. 6.
    Bessembinder, H., Chan, K.: Market efficiency and the returns to technical analysis. Financial Management 27(2), 5–17 (1998)CrossRefGoogle Scholar
  7. 7.
    Bhattacharyya, S., Mehta, K.: Evolutionary induction of trading models. In: Chen, S.-H. (ed.) Evolutionary Computation in Economics and Finance. STUDFUZZ, pp. 311–331. Physica-Verlag (2002)Google Scholar
  8. 8.
    Bokhari, J., Cai, C., Hudson, R., Keasey, K.: The predictive ability and profitability of technical trading rules: Does company size matter? Economics Letters 86(1), 21–27 (2005)CrossRefGoogle Scholar
  9. 9.
    Brock, W.A., Lakonishok, J., LeBaron, B.: Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47(5), 1731–1764 (1992)CrossRefGoogle Scholar
  10. 10.
    Chang, E.J., Lima, E.J.A., Tabak, B.M.: Testing for predictability in emerging equity markets. Emerging Markets Review 5(3), 295–316 (2004)CrossRefGoogle Scholar
  11. 11.
    Coutts, J.A., Cheung, K.: Trading rules and stock returns: some preliminary short run evidence from the hang seng 1985-1997. Applied Financial Economics 10(6), 579–586 (2000)CrossRefGoogle Scholar
  12. 12.
    Day, T.E., Wang, P.: Dividends, nonsynchronous prices, and the returns from trading the dow jones industrial average. Journal of Empirical Finance 9(4), 431–454 (2002)CrossRefGoogle Scholar
  13. 13.
    El-Yaniv, R., Fiat, A., Karp, R.M., Turpin, G.: Optimal search and one-way trading algorithm. Algorithmica 30(1), 101–139 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Fyfe, C., Marney, J.P., Tarbert, H.: Risk adjusted returns from technical trading: A genetic programming approach. Applied Financial Economics 15, 1073–1077 (2005)CrossRefGoogle Scholar
  15. 15.
    Gunasekarage, A., Power, D.M.: The profitability of moving average trading rules in South Asian stock markets. Emerging Markets Review 2(1), 17–33 (2001)CrossRefGoogle Scholar
  16. 16.
    Hatgioannides, J., Mesomeris, S.: On the returns generating process and the profitability of trading rules in emerging capital markets. Journal of International Money and Finance 26(6), 948–973 (2007)CrossRefGoogle Scholar
  17. 17.
    Hudson, R., Dempsey, M., Keasey, K.: A note on the weak form efficiency of capital markets: The application of simple technical trading rules to UK stock prices - 1935 to 1994. Journal of Banking and Finance 20(6), 1121–1132 (1996)CrossRefGoogle Scholar
  18. 18.
    Ito, A.: Profits on technical trading rules and time-varying expected returns: evidence from pacific-basin equity markets. Pacific-Basin Finance Journal 7(3-4), 283–330 (1999)CrossRefGoogle Scholar
  19. 19.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press (1994)Google Scholar
  20. 20.
    Koza, J.R., Bennet, F.H., Andre, D., Keane, M.: Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann (1999)Google Scholar
  21. 21.
    Kwon, K.-Y., Kish, R.J.: A comparative study of technical trading strategies and return predictability: An extension of Brock, Lakonishok, and LeBaron (1992) using NYSE and NASDAQ indices. Quarterly Review of Economics and Finance 42(3), 611–631 (2002)Google Scholar
  22. 22.
    Lagoarde-Segot, T., Lucey, B.M.: Efficiency in emerging markets - evidence from the MENA region. Journal of International Financial Markets, Institutions and Money 18(1), 94–105 (2008)CrossRefGoogle Scholar
  23. 23.
    LeBaron, B.: Technical trading rule profitability and foreign exchange intervention. Journal of International Economics 49(1), 125–143 (1999)CrossRefGoogle Scholar
  24. 24.
    Lento, C., Gradojevic, N.: The profitability of technical trading rules: A combined signal approach. Journal of Applied Business Research 23(1), 13–28 (2007)Google Scholar
  25. 25.
    Levich, R.M., Thomas, L.R.: The significance of technical trading-rule profits in the foreign exchange market: A bootstrap approach. Journal of International Money and Finance 12(5), 451–474 (1993)CrossRefGoogle Scholar
  26. 26.
    Marshall, B.R., Cahan, R.H.: Is technical analysis profitable on a stock market which has characteristics that suggest it be inefficient? Research in International Business and Finance 19(3), 384–398 (2005)CrossRefGoogle Scholar
  27. 27.
    Mills, T.C.: Technical analysis and the London Stock Exchange: Testing trading rules using the FT30. International Journal of Finance & Economics 2(4), 319–331 (1997)CrossRefGoogle Scholar
  28. 28.
    Ming-Ming, L., Siok-Hwa, L.: The profitability of the simple moving averages and trading range breakout in the Asian stock markets. Journal of Asian Economics 17(1), 144–170 (2006)CrossRefGoogle Scholar
  29. 29.
    Mohr, E.: Online Algorithms for Conversion Problems. PhD thesis, Saarland University and Max Planck Institute for Informatics (July 2011)Google Scholar
  30. 30.
    Navet, N., Chen, S.-H.: On predictability and profitability. would gp induced trading rules be sensitive to the observed entropy of time series? In: Brabazon, T., O’Neill, M. (eds.) Natural Computing in Computational Finance, pp. 197–210. Springer, Berlin (2008)CrossRefGoogle Scholar
  31. 31.
    Neely, C.: Risk-adjusted, ex ante, optimal technical trading rules in equity markets. International Review of Economics & Finance 12(1), 69–87 (2003)CrossRefGoogle Scholar
  32. 32.
    Neely, C., Weller, P., Dittmar, R.: Is technical analysis in the foreign exchange market profitable? a genetic programming approach. Journal of Financial and Quantitative Analysis 32(4), 405–426 (1997)CrossRefGoogle Scholar
  33. 33.
    Neely, C.J.: The temporal pattern of trading rule returns and exchange rate intervention: intervention does not generate technical trading profits. Journal of International Economics 58(1), 211–232 (2002)CrossRefGoogle Scholar
  34. 34.
    O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers (2003)Google Scholar
  35. 35.
    Parisi, F., Vasquez, A.: Simple technical trading rules of stock returns: Evidence from 1987 to 1998 in Chile. Emerging Markets Review 1(2), 152–164 (2000)CrossRefGoogle Scholar
  36. 36.
    Park, C.-H., Irwin, S.H.: What do we know about the profitability of technical analysis. Journal of Economic Surveys 21(4), 786–826 (2007)CrossRefGoogle Scholar
  37. 37.
    Pereira, R.: Forecasting ability but no profitability: An empirical evaluation of genetic algorithm-optimised technical trading rules. In: Chen, S.-H. (ed.) Evolutionary Computation in Economics and Finance. STUDFUZZ, pp. 287–310. Physica-Verlag (2002)Google Scholar
  38. 38.
    Potvin, J.-Y., Sorianoa, P., Vallée, M.: Generating trading rules on the stock markets with genetic programming. Computers & Operations Research 31(7), 1033–1047 (2004)zbMATHCrossRefGoogle Scholar
  39. 39.
    Raj, M., Thurston, D.: Effectiveness of simple technical trading rules in the hong kong futures markets. Applied Economics Letters 3(1), 33–36 (1996)CrossRefGoogle Scholar
  40. 40.
    Ratner, M., Leal, R.P.C.: Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. Journal of Banking and Finance 23(12), 1887–1905 (1999)CrossRefGoogle Scholar
  41. 41.
    Schmidt, G., Mohr, E., Kersch, M.: Experimental analysis of an online trading algorithm. Electronic Notes in Discrete Mathematics 36, 519–526 (2010)CrossRefGoogle Scholar
  42. 42.
    Sortino, F.A., Price, L.: Performance measurement in a downside risk framework. Journal of Investing 3(3), 59–64 (1994)CrossRefGoogle Scholar
  43. 43.
    Sortino, F.A., van der Meer, R.: Downside risk. The Journal of Portfolio Management 17(4), 27–31 (1991)CrossRefGoogle Scholar
  44. 44.
    Tabak, B.M., Lima, E.J.A.: Market efficiency of Brazilian exchange rate: Evidence from variance ratio statistics and technical trading rules. European Journal of Operational Research 194(3), 814–820 (2009)zbMATHCrossRefGoogle Scholar
  45. 45.
    Zelinka, I., Oplatkova, Z., Nolle, L.: Analytic programming - symbolic regression by means of arbitrary evolutionary algorithms. Journal of Simulation 6(9), 44–56 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Esther Mohr
    • 1
    Email author
  • Günter Schmidt
    • 1
  • Sebastian Jansen
    • 2
  1. 1.Saarland UniversitySaarbrückenGermany
  2. 2.Banking and Financial ServicesUniversity of HohenheimStuttgartGermany

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