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Nonlinear Goal-Directed CPPI Strategy

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Computational Intelligence in Economics and Finance

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Abstract

An investor will in general designate a goal in the investment process. The traditional constant proportion portfolio insurance (CPPI) strategy considers only the floor constraint but not the goal aspect. This paper proposes a goal-directed (GD) strategy to express an investor’s goal-directed trading behavior and combines it with the portfolio insurance perspective to form a piecewise linear goal-directed CPPI (GDCPPI) strategy. This piecewise linear GDCPPI strategy shows that there is a wealth position M at the intersection of the linear GD strategy and linear CPPI strategy. This M position guides investors to apply the CPPI strategy or GD strategy depending on whether the current wealth is less or greater thanM, respectively. In addition, we extend the piecewise linear GDCPPI strategy to a piecewise nonlinear GDCPPI strategy with a minimum function. These piecewise GDCPPI strategies when applying the minimum function can fully maintain the features of the CPPI strategy and the GD strategy without considering the explicitM. This minimum function in fact can obtain the concept of the explicit M, but it operates the M implicitly. Furthermore, we argue that the piecewise nonlinear GDCPPI strategy owns a larger solution space and it can then outperform the piecewise linear GDCPPI strategy in terms of the return rate performance measure. This paper performs some experiments using the Brownian, GA and forest GP techniques to prove with statistical significance that the piecewise nonlinear GDCPPI strategy can outperform the piecewise linear GDCPPI strategy and that there are some data-driven techniques that can find better piecewise linear GDCPPI strategies than strategies based on the Brownian technique.

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References

  1. Andrew M, Prager R (1994) Genetic programming for the acquisition of double auction market strategies. In: Kinnear KE (ed) Advances in genetic programming. The MIT Press, Cambridge, MA:355–368

    Google Scholar 

  2. Bäck T, Fogel DB, Michalewicz T (eds) (2000) Evolutionary computation 1: basic algorithms and operators. IOP

    Google Scholar 

  3. Bäck T, Fogel DB, Michalewicz T (eds) (2000) Evolutionary computation 2: advanced algorithms and operators. IOP

    Google Scholar 

  4. Bauer RJ (1994) Genetic algorithms and investment strategies. John Wiley & Sons, New York

    Google Scholar 

  5. Bhattachary S, Pictet O, Zumbach G (1998) Representational semantics for genetic programming based learning in high-frequency financial data. In: Koza J, Banzhaf W, Chellapilla K, Beb K, Dorigo M, Fogel D, Garzon M, Iba H, Riolo R (eds) Genetic programming 1998: proceedings of the third annual conference. Morgan Kaufmann, San Francisco, CA:11–16

    Google Scholar 

  6. Black F, Perold AF (1992) Theory of constant proportion portfolio insurance. Journal of Economic Dynamics and Control 16:403–426

    Article  MATH  Google Scholar 

  7. Brown K, Harlow W, Starks L (1996) Of tournaments and temptations: an analysis of managerial incentives in the mutual fund industry. Journal of Finance 51:85–110

    Article  Google Scholar 

  8. Browne S (1997) Survival and growth with a liability: optimal portfolio strategies in continuous time. Mathematics of Operations Research 22(2):468–493

    Article  MATH  MathSciNet  Google Scholar 

  9. Busse JA (2001) Another look at mutual fund tournaments. Journal of Financial and Quantitative Analysis 36(1):53–73

    Article  Google Scholar 

  10. Chen JS (2005) Trading strategy generation using genetic algorithms. Asian Journal of Information Technology 4(4):310–322

    Google Scholar 

  11. Chen JS, Lin PC (2003) Multi-valued stock valuation based on the fuzzy genetic programming approach. In: Proceedings of the 7th joint conference on information sciences (The third international workshop on computational intelligence in economics and finance):1124–1127

    Google Scholar 

  12. Chen JS, Wu PC, Liao PY (2000) Neural network forecasting of TAIMEX index futures. In: Proceedings of the second Asia-Pacific conference on genetic algorithms and applications. Global-Link Publishing Company, Hong Kong:403–408

    Google Scholar 

  13. Chen SH (1996) Genetic programming, predictability, and stock market efficiency. In: Vlacic L, Nguyen T, Cecez-Kecmanovic D (eds) Modelling and control of national and regional economies. Pergamon Press, Oxford, Great Britain:283–288

    Google Scholar 

  14. Chen SH (1998) Hedging derivative securities with genetic programming. In: Application of machine learning and data mining in finance. Workshop at ECMI-98 Dorint-Parkhotel, Chemnitz, Germany

    Google Scholar 

  15. Chen SH, Yeh CH (1995) Predicting stock returns with genetic programming: do the short-term nonlinear regularities exist? In: Fisher D (ed) Proceedings of the fifth international workshop on artificial intelligence and statistics:95–101

    Google Scholar 

  16. Chen SH, Yeh CH (1997) Toward a computable approach to the efficient market hypothesis: an application of genetic programming. Journal of Economic Dynamics and Control 21:1043–1063

    Article  MATH  MathSciNet  Google Scholar 

  17. Chen SH, Yeh CH (1997) Using genetic programming to model volatility in financial time series. In: Koza J, Deb J, Dorigo M, Fogel D, Garzon M, Iba H, Riolo R (eds) Genetic programming 1997: proceedings of the second annual conference. Morgan Kaufmann, Stanford University, CA:58–63

    Google Scholar 

  18. Chen SH, Yeh CH, Lee WC (1998) Option pricing with genetic programming. In: Koza J, Banzhaf W, Chellapilla K, Beb K, Dorigo M, Fogel D, Garzon M, Iba H, Riolo R (eds) Genetic programming 1998: proceedings of the third annual conference. Morgan Kaufmann, San Francisco, CA:32–37

    Google Scholar 

  19. Chevalier J, Ellison G (1997) Risk taking by mutual funds as a response to incentives. Journal of Political Economy 105:1167–1200

    Article  Google Scholar 

  20. Chidambaran N, Trigueros J, Lee C (2002) Option pricing via genetic programming. In: Chen SH (ed) Evolutionary computation in economics and finance. Physica-Verlag, Heidelberg, New York:383–397

    Google Scholar 

  21. Colin AM (1994) Genetic algorithms for financial modeling. In: Deboeck GJ (ed) Trading on the edge: neural, genetic and fuzzy systems for chaotic financial markets. Wiley:148–173

    Google Scholar 

  22. Deboeck GJ (ed) (1994) Trading on the edge: neural, genetic and fuzzy systems for chaotic financial markets. John Wiley & Sons, New York

    Google Scholar 

  23. Deboeck GJ (1994) Using GAs to optimize a trading system. In: Deboeck GJ (ed) Trading on the edge: neural, genetic and Fuzzy Systems for chaotic financial markets. Wiley:174–188

    Google Scholar 

  24. Eglit JT (1994) Trend prediction in financial time series. In: Koza J (ed) Genetic algorithms at stanford. Stanford Bookstore, Stanford, CA:31–40

    Google Scholar 

  25. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley

    Google Scholar 

  26. Grossman SJ, Zhou Z (1993) Optimal investment strategies for controlling drawdowns. Mathematical Finance 3(3):241–276

    Article  MATH  Google Scholar 

  27. Hall JW (1994) Adaptive selection of U.S. stocks with neural nets. In: Deboeck GJ (ed) Trading on the edge: neural, genetic and fuzzy systems for chaotic financial markets. Wiley:45–65

    Google Scholar 

  28. Jackwerth JC (2000) Recovering risk aversion from option prices and realized returns. The Review of Financial Studies 13(2):433–451

    Article  Google Scholar 

  29. Jang GS, Lai F (1994) Intelligent trading of an emerging market. In: Deboeck GJ (ed) Trading on the edge: neural, genetic and fuzzy systems for chaotic financial markets. John Wiley & Sons:80–101

    Google Scholar 

  30. Kaboudan M (1998) Forecasting stock returns using genetic programming in C++. In: Cook D (ed) FLAIRS proceedings of the eleventh international Florida artificial intelligence research symposium conference. AAAI Press, Menlo Park, CA:73–77

    Google Scholar 

  31. Kaboudan M (2002) GP forecasts of stock prices for profitable trading. In: Chen SH (ed) Evolutionary computation in economics and finance. Physica-Verlag, Heidelberg, New York:359–381

    Google Scholar 

  32. Kamijo K, Tanigawa T (1990) Stock price pattern recognition: a recurrent neural network approach. In: Proceedings of the 1990 international joint conference on neural networks:215–221

    Google Scholar 

  33. Keber C (2002) Evolutionary computation in option pricing: determining implied volatilities based on American put options. In: Chen SH (ed) Evolutionary computation in economics and Finance. Physica-Verlag, Heidelberg, New York:399–415

    Google Scholar 

  34. Kimoto T, Asakawa K, Yoda M, Takeoka M (1990) Stock market prediction system with modular neural networks. In: Proceedings of the 1990 international joint conference on neural networks:1–6

    Google Scholar 

  35. Koza JR (1992) Genetic Programming: on the programming of computers by means of natural selection. MIT Press, Massachusetts

    MATH  Google Scholar 

  36. Koza JR (1994) Introduction to genetic programming. In: Kinnear KE (ed) Advances in genetic programming. The MIT Press, Cambridge, MA:21–42

    Google Scholar 

  37. Liao PY, Chen JS (2001) Dynamic trading strategy learning model using learning classifier systems. In: Proceedings of the 2001 IEEE congress on evolutionary computation:783–789

    Google Scholar 

  38. Lo AW, MacKinlay AC (1999) A non-random walk down Wall Street. Princeton University Press, Princeton, NJ

    Google Scholar 

  39. Mitchell M (1996) An introduction to genetic algorithms. MIT Press

    Google Scholar 

  40. Murphy JJ (1999) Technical analysis of the futures markets. New York Institute of Finance

    Google Scholar 

  41. Oussaidene M, Chopard B, Pictet O, Tomassini M (1996) Parallel genetic programming: an application to trading models evolution. In: Koza J, Goldberg D, Fogel D, Riolo R (ed) Genetic programming 1996: proceedings of the first annual conference. The MIT Press, Cambridge, MA:357–362

    Google Scholar 

  42. Perold AF, Sharpe WF (1988) Dynamic strategies for asset allocation. Financial Analyst Journal Jan/Feb:16–27

    Google Scholar 

  43. Pratt KB, Fink E (2002) Search for patterns in compressed times series. International Journal of Image and Graphics 2(1):89–102

    Article  Google Scholar 

  44. Sarma J, DeJong K (2000) Generation gap methods. In: Bäck T, Fogel DB, Michalewicz T (eds) Evolutionary computation 1: basic algorithms and operators. Institute of Physics Publishing, Bristol:205–227

    Google Scholar 

  45. Sharpe WF (1963) A simplified model for portfolio analysis. Management Science 9:277–293

    Google Scholar 

  46. Shieh JCP (2003) Fundamentals of investment. BestWise, Taipei

    Google Scholar 

  47. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. Computer 27(6):17–26

    Article  Google Scholar 

  48. Tanigawa T, Kamijo K (1992) Stock price pattern matching system: dynamic programming neural networks approach. In: Proceedings of the 1992 international joint conference on Neural Networks:465–471

    Google Scholar 

  49. Tayler J (2003) Risk-taking behavior in mutual fund tournaments. Journal of Economic Behavior & Organization 50:373–383

    Article  Google Scholar 

  50. Warren MA (1994) Stock price prediction using genetic programming. In: Koza J (ed) Genetic algorithms at stanford. Stanford Bookstore, Stanford, CA:180–184

    Google Scholar 

  51. Yoda M (1994) Predicting the Tokyo stock market. In: Deboeck GJ (ed) Trading on the edge. John Wiley & Sons:66–79

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Management, National Central University, Chungli, 320, Taiwan

    Jiah-Shing Chen

  2. Department of Information Management, Overseas Chinese Institute of Technology, Taichung, 407, Taiwan

    Benjamin Penyang Liao

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  1. Jiah-Shing Chen
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  2. Benjamin Penyang Liao
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Editor information

Editors and Affiliations

  1. AI-ECON Research Center Department of Economics, National Chengchi University, Taipei, 11623, Taiwan R.O.C.

    Shu-Heng Chen

  2. Electrical & Computer Engineering, Box 90291, PSE, Durham, NC, 27708, USA

    Paul P. Wang

  3. Department of Finance and Banking, Aletheia University, Tamsui, Taipei, 25103, Taiwan R.O.C.

    Tzu-Wen Kuo

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© 2007 Springer-Verlag Berlin Heidelberg

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Chen, JS., Liao, B.P. (2007). Nonlinear Goal-Directed CPPI Strategy. In: Chen, SH., Wang, P.P., Kuo, TW. (eds) Computational Intelligence in Economics and Finance. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72821-4_12

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  • DOI: https://doi.org/10.1007/978-3-540-72821-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72820-7

  • Online ISBN: 978-3-540-72821-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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