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Size matters! How position sizing determines risk and return of technical timing strategies

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  • Scholz, Peter
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    Abstract

    The application of a technical trading rule, which just provides long and short signals, requires the investor to decide upon the exposure to stake in each trade. Although this position sizing (or money management) crucially affects the risk and return characteristics, recent academic literature has largely ignored this effect, leaving reported results incomparable. This work systematically analyzes the impact of position sizing on timing strategies and clarifies the relation to the Kelly criterion, which proposes to bet relative fractions from the remaining gambling budget. Both erratic as well as different relative positions, i.e. fixed proportions of the remaining portfolio value, are compared for simple moving average trading rules. The simulation of parametrized return series allows systematically varying those asset price properties, which are most in uential on timing results: drift, volatility, and autocorrelation. The study reveals that the introduction of relative position sizing has a severe impact on trading results compared to erratic positions. In contrast to a standard Kelly framework, however, an optimal position size does not exist. Interestingly, smaller trading fractions deliver the highest risk-adjusted returns in most scenarios. --

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    Bibliographic Info

    Paper provided by Frankfurt School of Finance and Management, Centre for Practical Quantitative Finance (CPQF) in its series CPQF Working Paper Series with number 31.

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    Date of creation: 2012
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    Handle: RePEc:zbw:cpqfwp:31

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    Related research

    Keywords: Kelly criterion; money management; parameterized simulation; position sizing; technical analysis; technical trading; timing strategy;

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    1. John Anderson & Robert Faff, 2004. "Maximizing futures returns using fixed fraction asset allocation," Applied Financial Economics, Taylor & Francis Journals, vol. 14(15), pages 1067-1073.
    2. Suzanne Fifield & David Power & C. Donald Sinclair, 2005. "An analysis of trading strategies in eleven European stock markets," The European Journal of Finance, Taylor & Francis Journals, vol. 11(6), pages 531-548.
    3. Marc Potters & Jean-Philippe Bouchaud, 2005. "Trend followers lose more often than they gain," Papers physics/0508104, arXiv.org.
    4. Zhu, Yingzi & Zhou, Guofu, 2009. "Technical analysis: An asset allocation perspective on the use of moving averages," Journal of Financial Economics, Elsevier, vol. 92(3), pages 519-544, June.
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    7. MacLean, Leonard C. & Sanegre, Rafael & Zhao, Yonggan & Ziemba, William T., 2004. "Capital growth with security," Journal of Economic Dynamics and Control, Elsevier, vol. 28(5), pages 937-954, February.
    8. Merton, Robert C. & Samuelson, Paul A., 1974. "Fallacy of the log-normal approximation to optimal portfolio decision-making over many periods," Journal of Financial Economics, Elsevier, vol. 1(1), pages 67-94, May.
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    10. Samuelson, Paul A, 1969. "Lifetime Portfolio Selection by Dynamic Stochastic Programming," The Review of Economics and Statistics, MIT Press, vol. 51(3), pages 239-46, August.
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    Cited by:
    1. Libman, Alexander & Kozlov, Vladimir & Schultz, André, 2012. "Roving bandits in action: Outside option and governmental predation in autocracies," Frankfurt School - Working Paper Series 190, Frankfurt School of Finance and Management.
    2. Inklaar, Robert & Koetter, Michael & Noth, Felix, 2012. "Who's afraid of big bad banks? Bank competition, SME, and industry growth," Frankfurt School - Working Paper Series 197, Frankfurt School of Finance and Management.
    3. Boeing, Philipp & Mueller, Elisabeth & Sandner, Philipp, 2012. "What makes Chinese firms productive? Learning from indigenous and foreign sources of knowledge," Frankfurt School - Working Paper Series 196, Frankfurt School of Finance and Management.
    4. Dietmar Harhoff & Elisabeth Mueller & John Van Reenen, 2013. "What are the channels for technology sourcing? Panel data evidence from German companies," LSE Research Online Documents on Economics 51524, London School of Economics and Political Science, LSE Library.
    5. Harhoff, Dietmar, 2012. "What are the Channels for Technology Sourcing? Panel Data Evidence from German Companies," Discussion Papers in Business Administration 14327, University of Munich, Munich School of Management.
    6. Böing, Philipp & Müller, Elisabeth, 2012. "Technological Capabilities of Chinese Enterprises: Who is Going to Compete Abroad?," Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62081, Verein für Socialpolitik / German Economic Association.

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