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Portfolio optimization when expected stock returns are determined by exposure to risk

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  • Carl Lindberg
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    Abstract

    It is widely recognized that when classical optimal strategies are applied with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time. The predominant explanation for this is the difficulty of estimating expected returns accurately. In this paper, we modify the $n$ stock Black--Scholes model by introducing a new parametrization of the drift rates. We solve Markowitz' continuous time portfolio problem in this framework. The optimal portfolio weights correspond to keeping $1/n$ of the wealth invested in stocks in each of the $n$ Brownian motions. The strategy is applied out-of-sample to a large data set. The portfolio weights are stable over time and obtain a significantly higher Sharpe ratio than the classical $1/n$ strategy.

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    File URL: http://arxiv.org/pdf/0906.2271
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 0906.2271.

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    Date of creation: Jun 2009
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    Publication status: Published in Bernoulli 2009, Vol. 15, No. 2, 464-474
    Handle: RePEc:arx:papers:0906.2271

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    Web page: http://arxiv.org/

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    Cited by:
    1. Ankit Dangi, 2013. "Financial Portfolio Optimization: Computationally guided agents to investigate, analyse and invest!?," Papers 1301.4194, arXiv.org.
    2. Özge Alp & Ralf Korn, 2011. "Continuous-time mean-variance portfolio optimization in a jump-diffusion market," Decisions in Economics and Finance, Springer, Springer, vol. 34(1), pages 21-40, May.
    3. Thorsten Poddig & Albina Unger, 2012. "On the robustness of risk-based asset allocations," Financial Markets and Portfolio Management, Springer, Springer, vol. 26(3), pages 369-401, September.
    4. Teiletche, Jérôme & Roncalli, Thierry & Maillard, Sébastien, 2010. "The properties of equally-weighted risk contributions portfolios," Economics Papers from University Paris Dauphine 123456789/4688, Paris Dauphine University.

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