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Portfolio Performance Gauging in Discrete Time Using a Luenberger Productivity Indicator

Author

Listed:
  • Olivier Brandouy

    (CNRS-LEM(UMR 8179),IAE,University of Lille 1)

  • Walter Briec

    (University of Perpignan, LAMPS)

  • Kristiaan Kerstens

    (CNRS-LEM (UMR 8179), IESEG School of Management)

  • Ignace Van de Woestyne

    (HUB University College Brussels)

Abstract

This paper proposes a pragmatic, discrete time indicator to gauge the performance of portfolios over time. Integrating the shortage function (Luenberger, 1995) into a Luenberger portfolio productivity indicator (Chambers, 2002), this study estimates the changes in the relative positions of portfolios with respect to the traditional Markowitz mean-variance efficient frontier, as well as the eventual shifts of this frontier over time. Based on the analysis of local changes relative to these mean-variance and higher moment (in casu, mean-variance-skewness) frontiers, this methodology allows to neatly separate between on the one hand performance changes due to portfolio strategies and on the other hand performance changes due to the market evolution. This methodology is empirically illustrated using a mimicking portfolio approach (Fama and French 1996; 1997) using US monthly data from January 1931 to August 2007.

Suggested Citation

  • Olivier Brandouy & Walter Briec & Kristiaan Kerstens & Ignace Van de Woestyne, 2008. "Portfolio Performance Gauging in Discrete Time Using a Luenberger Productivity Indicator," Working Papers 2008-ECO-12, IESEG School of Management, revised Oct 2009.
    • Handle: RePEc:ies:wpaper:e200812
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    Cited by:

    1. E. Avisoa, 2016. "European banks’ technical efficiency and performance: do business models matter? The case of European co-operatives banks," Débats Economiques et financiers 25, Banque de France.
    2. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    3. Wanke, Peter & Maredza, Andrew & Gupta, Rangan, 2017. "Merger and acquisitions in South African banking: A network DEA model," Research in International Business and Finance, Elsevier, vol. 41(C), pages 362-376.
    4. Xiao, Helu & Zhou, Zhongbao & Ren, Teng & Liu, Wenbin, 2022. "Estimation of portfolio efficiency in nonconvex settings: A free disposal hull estimator with non-increasing returns to scale," Omega, Elsevier, vol. 111(C).
    5. Wanke, Peter & Tsionas, Mike G. & Chen, Zhongfei & Moreira Antunes, Jorge Junio, 2020. "Dynamic network DEA and SFA models for accounting and financial indicators with an analysis of super-efficiency in stochastic frontiers: An efficiency comparison in OECD banking," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 456-468.
    6. G.A. Vijayalakshmi Pai & Thierry Michel, 2012. "Integrated Metaheuristic Optimization Of 130–30 Investment‐Strategy‐Based Long–Short Portfolios," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 43-74, January.
    7. Kerstens, Kristiaan & Mounir, Amine & de Woestyne, Ignace Van, 2011. "Non-parametric frontier estimates of mutual fund performance using C- and L-moments: Some specification tests," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1190-1201, May.
    8. Jin-Li Hu & Tzu-Pu Chang & Ray Chou, 2014. "Market conditions and the effect of diversification on mutual fund performance: should funds be more concentrative under crisis?," Journal of Productivity Analysis, Springer, vol. 41(1), pages 141-151, February.
    9. Carlos P. Barros & Qi Bin Liang & Nicolas Peypoch, 2014. "Technical Efficiency in the Angolan Banking Sector with the B-convexity Model," South African Journal of Economics, Economic Society of South Africa, vol. 82(3), pages 443-454, September.
    10. Miao, Zhuang & Chen, Xiaodong & Baležentis, Tomas, 2021. "Improving energy use and mitigating pollutant emissions across “Three Regions and Ten Urban Agglomerations”: A city-level productivity growth decomposition," Applied Energy, Elsevier, vol. 283(C).
    11. Jens J. Krüger, 2021. "Nonparametric portfolio efficiency measurement with higher moments," Empirical Economics, Springer, vol. 61(3), pages 1435-1459, September.
    12. Krüger, Jens J., 2021. "Nonparametric portfolio efficiency measurement with higher moments," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130825, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Ren, Tiantian & Kerstens, Kristiaan & Kumar, Saurav, 2024. "Risk-aversion versus risk-loving preferences in nonparametric frontier-based fund ratings: A buy-and-hold backtesting strategy," European Journal of Operational Research, Elsevier, vol. 319(1), pages 332-344.
    14. Laurens Cherchye & Bram De Rock & Dieter Saelens, 2024. "Nonparametric analysis of financial portfolio performance," Working Papers ECARES 2024-08, ULB -- Universite Libre de Bruxelles.
    15. Wanke, Peter & Barros, Carlos P. & Faria, João R., 2015. "Financial distress drivers in Brazilian banks: A dynamic slacks approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 258-268.
    16. Peter Wanke & Carlos Barros & Nkanga Pedro João Macanda, 2016. "Predicting Efficiency in Angolan Banks: A Two-Stage TOPSIS and Neural Networks Approach," South African Journal of Economics, Economic Society of South Africa, vol. 84(3), pages 461-483, September.

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    Keywords

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    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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