Abstract
This paper proposes a dynamic proportion portfolio insurance (DPPI) strategy based on the popular constant proportion portfolio insurance (CPPI) strategy. The constant multiplier in CPPI is generally regarded as the risk multiplier. Since the market changes constantly, we think that the risk multiplier should change accordingly. This research identifies factors relating to market volatility. These factors are built into equation trees by genetic programming. Experimental results show that our DPPI strategy is more profitable than traditional CPPI strategy.
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References
Aby Jr., C.D., Simpson Jr., C.L., Simpson, P.M.: Common stock selection with an emphasis on mispriced assets: Some evidence from technical analysis. Journal of Pension Planning & Compliance 23 (1998)
Black, F., Jones, R.: Simplifying portfolio insurance. Journal of Portfolio Management (1987)
Brock, W., Lakonishok, J., LeBaron, B.: Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47 (1992)
Huang, C.-K.: Apply Genetic Model to Strategy Operation of Dynamic Portfolio Insurance. Master Thesis, Institute of Information Management, National Chiao-Tung University (2001)
Chiu, Y.-M.: Portfolio Insurance Strategy: Taiwan Evidence. Master Thesis, Institute of Money and Banking at National Chengchi University (2000)
Hung, C.: An Empirical Test of the Components of the Aggregate Volatility. Master Thesis, Institute of Business Administration at National Cheng Kung University (2002)
Fyfe, C., Marney, J.P.: Technical Analysis versus Market Efficiency–A Genetic Programming Approach. Applied Financial Economics 9, 183–191 (1999)
Hakanoglu, E., Kopprasch, R., Roman, E.: Constant Proportion Portfolio Insurance for Fixed-Income Investment. Journal of Portfolio Management (1989)
Fama, E.F., French, K.R.: The Cross-Section of Expected Stock Returns. Journal of Finance 47 (1992)
Hsu, W.C.: An Assessment of Stock Market Volatility: the Case of Taiwan. Master Thesis, Institute of Finance at National Sun Yat-Sen University (1996)
Kaboudan, M.A.: Genetic Programming Prediction of Stock Prices. Computational Economics 16, 207–236 (2000)
Kenneth, R., French, G., Schwert, W., Stambaugh, R.F.: Expected stock returns and volatility. Journal of Financial Economics 19 (1987)
Kim, K.J., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications 19, 125–132 (2000)
Koza, J.R.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge (1992)
Potvina, J.-Y., Soriano, P., Vallee, M.: Generating trading rules on the stock markets with genetic programming. Computers & Operations Research 31, 1033–1047 (2004)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)
Levy, R.A.: Conceptual Foundation of Technical Analysis. Financial Analysis Journal (1966)
Levy, R.A.: Random Walks: Reality or Myth. Financial Analysts Journal 23 (1967)
Neely, C.J., Weller, P., Dittmar, R.: Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach. Centre for Economic Policy Research, Discussion Paper, p. 1480 (1996)
Pruitt, S.W., White, R.E.: The CRISMA Trading System: Who Says Technical Analysis Can’t Beat the Market? Journal of Portfolio Management (1988)
Nison, S.: Beyond Candlesticks: New Japanese Charting Techniques Revealed. Wiley, Chichester (1994)
Schwert, G.W.: Stock Market Volatility. Financial Analysts Journal 46 (1990)
Schwert, G.W., Seguin, P.J.: Heteroskedasticity in Stock Returns. Journal of Finance 45 (1990)
Schwert, G.W.: Why Does Stock Market Volatility Change Over Time? Journal of Finance 44 (1989)
Chen, S.-H., Yeh, C.-H.: Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics & Control 25, 363–393 (2001)
Thawornwong, S., Enke, D., Dagli, C.: Neural Networks as a Decision Maker for Stock Trading: A Technical Analysis Approach. International Journal of Smart Engineering System Design 5, 313–325 (2003)
Lensberg, T.: Investment behavior under Knightian uncertainty–an evolutionary approach. Journal of Economic Dynamics & Control 23, 1587–1604 (1999)
Tsaih, R., Hsu, Y., Lai, C.C.: Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Systems 23, 161–174 (1998)
Zhu, Y., Kavee, R.C.: Performance of Portfolio Insurance Strategies. Journal of Portfolio Management (1988)
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Chen, JS., Chang, CL. (2005). Dynamical Proportion Portfolio Insurance with Genetic Programming. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_104
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DOI: https://doi.org/10.1007/11539117_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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