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Fractal Profit Landscape of the Stock Market

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  • Andreas Grönlund
  • Il Gu Yi
  • Beom Jun Kim

Abstract

We investigate the structure of the profit landscape obtained from the most basic, fluctuation based, trading strategy applied for the daily stock price data. The strategy is parameterized by only two variables, p and q Stocks are sold and bought if the log return is bigger than p and less than –q, respectively. Repetition of this simple strategy for a long time gives the profit defined in the underlying two-dimensional parameter space of p and q. It is revealed that the local maxima in the profit landscape are spread in the form of a fractal structure. The fractal structure implies that successful strategies are not localized to any region of the profit landscape and are neither spaced evenly throughout the profit landscape, which makes the optimization notoriously hard and hypersensitive for partial or limited information. The concrete implication of this property is demonstrated by showing that optimization of one stock for future values or other stocks renders worse profit than a strategy that ignores fluctuations, i.e., a long-term buy-and-hold strategy.

Suggested Citation

  • Andreas Grönlund & Il Gu Yi & Beom Jun Kim, 2012. "Fractal Profit Landscape of the Stock Market," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-5, April.
    • Handle: RePEc:plo:pone00:0033960
    DOI: 10.1371/journal.pone.0033960
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    References listed on IDEAS

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    1. Park, Cheol-Ho & Irwin, Scott H., 2004. "The Profitability of Technical Analysis: A Review," AgMAS Project Research Reports 37487, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    2. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    3. B. Tóth & E. Scalas & J. Huber & M. Kirchler, 2007. "The value of information in a multi-agent market model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(1), pages 115-120, January.
    4. Schulmeister, Stephan, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, Elsevier, vol. 18(4), pages 190-201, October.
    5. Parameswaran Gopikrishnan & Vasiliki Plerou & Luis A. Nunes Amaral & Martin Meyer & H. Eugene Stanley, 1999. "Scaling of the distribution of fluctuations of financial market indices," Papers cond-mat/9905305, arXiv.org.
    6. Yongjoo Baek & Sang Hoon Lee & Hawoong Jeong, 2010. "Market behavior and performance of different strategy evaluation schemes," Papers 1002.4744, arXiv.org, revised Aug 2010.
    7. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    8. Fengzhong Wang & Kazuko Yamasaki & Shlomo Havlin & H. Eugene Stanley, 2005. "Scaling and memory of intraday volatility return intervals in stock market," Papers physics/0511101, arXiv.org.
    9. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
    10. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    11. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    12. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Ioana-Andreea Boboc & Mihai-Cristian Dinică, 2013. "An Algorithm for Testing the Efficient Market Hypothesis," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
    2. Wiktor Kolwzan & Agnieszka Skowronek-Gradziel & Teresa Kupczyk & Jozef Ledzianowski, 2024. "Study of the Nature and Dynamics of Processes in Terms of Fractals on the Example of Selected Joint Stock Companies," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 375-402.
    3. repec:ers:journl:v:xxv:y:2022:i:2:p:172-197 is not listed on IDEAS
    4. repec:ers:journl:v:xxvi:y:2023:i:3:p:78-103 is not listed on IDEAS

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