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Backward/forward optimal combination of performance measures for equity screening

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  • Billio, Monica
  • Caporin, Massimiliano
  • Costola, Michele

Abstract

We introduce a novel criterion for performance measure combination designed to be used as an equity screening algorithm. The proposed approach follows the general idea of linearly combining selected performance measures with positive weights and combination weights are determined by means of an optimisation step. The underlying criterion function takes into account the risk-return trade-off potentially associated with the equity screens, evaluated on a historical and rolling basis. By construction, performance combination weights can vary over time, allowing for changes in preferences across performance measures. An empirical example shows the benefits of our approach compared to naive screening rules based on the Sharpe ratio.

Suggested Citation

  • Billio, Monica & Caporin, Massimiliano & Costola, Michele, 2015. "Backward/forward optimal combination of performance measures for equity screening," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 63-83.
    • Handle: RePEc:eee:ecofin:v:34:y:2015:i:c:p:63-83
    DOI: 10.1016/j.najef.2015.08.002
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    Cited by:

    1. Caporin, Massimiliano & Costola, Michele & Jannin, Gregory & Maillet, Bertrand, 2018. "“On the (Ab)use of Omega?”," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 11-33.
    2. Monica Billio & Bertrand Maillet & Loriana Pelizzon, 2022. "A meta-measure of performance related to both investors and investments characteristics," Annals of Operations Research, Springer, vol. 313(2), pages 1405-1447, June.
    3. León, Angel & Navarro, Lluís & Nieto, Belén, 2019. "Screening rules and portfolio performance," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 642-662.
    4. Michele Costola & Massimiliano Caporin, 2016. "Rational Learning For Risk-Averse Investors By Conditioning On Behavioral Choices," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-26, March.
    5. Massimiliano Caporin & Luca Corazzini & Michele Costola, 2014. "Measuring the Behavioral Component of Financial Fluctuations: An Analysis Based on the S&P 500," CREATES Research Papers 2014-33, Department of Economics and Business Economics, Aarhus University.
    6. León, Ángel & Moreno, Manuel, 2015. "Lower Partial Moments under Gram Charlier Distribution: Performance Measures and Efficient Frontiers," QM&ET Working Papers 15-3, University of Alicante, D. Quantitative Methods and Economic Theory.
    7. José Luis Miralles-Quirós & María Mar Miralles-Quirós, 2021. "Alternative Financial Methods for Improving the Investment in Renewable Energy Companies," Mathematics, MDPI, vol. 9(9), pages 1-25, May.

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    More about this item

    Keywords

    Performance measures; Combining performance measures; Portfolio allocation; Equity screening; Differential evolution;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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