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Beating the Market with Generalized Generating Portfolios

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  • Patrick Mijatovic

Abstract

Stochastic portfolio theory aims at finding relative arbitrages, i.e. trading strategies which outperform the market with probability one. Functionally generated portfolios, which are deterministic functions of the market weights, are an invaluable tool in doing so. Driven by a practitioner point of view, where investment decisions are based upon consideration of various financial variables, we generalize functionally generated portfolios and allow them to depend on continuous-path semimartingales, in addition to the market weights. By means of examples we demonstrate how the inclusion of additional processes can reduce time horizons beyond which relative arbitrage is possible, boost performance of generated portfolios, and how investor preferences and specific investment views can be included in the context of stochastic portfolio theory. Striking is also the construction of a relative arbitrage opportunity which is generated by the volatility of the additional semimartingale. An in-depth empirical analysis of the performance of the proposed strategies confirms our theoretical findings and demonstrates that our portfolios represent profitable investment opportunities even in the presence of transaction costs.

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  • Patrick Mijatovic, 2021. "Beating the Market with Generalized Generating Portfolios," Papers 2101.07084, arXiv.org.
    • Handle: RePEc:arx:papers:2101.07084
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    References listed on IDEAS

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    Cited by:

    1. Erhan Bayraktar & Donghan Kim & Abhishek Tilva, 2023. "Quantifying dimensional change in stochastic portfolio theory," Papers 2303.00858, arXiv.org, revised Apr 2023.

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