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Choice of Benchmark When Forecasting Long-term Stock Returns

Author

Listed:
  • Ioannis Kyriakou

    (Cass Business School, City, University of London, UK)

  • Parastoo Mousavi

    (Cass Business School, City, University of London, UK)

  • Jens Perch Nielsen

    (Cass Business School, City, University of London, UK)

  • Michael Scholz

    (University of Graz, Austria)

Abstract

Recent advances in pension product development seem to favour alternatives to the risk free asset so often used in financial theory. In this paper, we investigate other benchmarks of the financial model than the classical risk free asset; for example, a benchmark following an inflation index is just one important case. Modelling extra returns above the inflation of risky assets is important, for example, for actuarial applications aiming at providing real income forecasts for pensioners. We study market timing when three alternative benchmarks are considered: the risk free rate, inflation and the long bond yield. We conclude that forecasting future stock returns is of similar complexity for all three considered benchmarks. From a pension fund modelling perspective, it therefore seems that one should use the most convenient benchmark for the problem at hand.

Suggested Citation

  • Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2018. "Choice of Benchmark When Forecasting Long-term Stock Returns," Graz Economics Papers 2018-08, University of Graz, Department of Economics.
    • Handle: RePEc:grz:wpaper:2018-08
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    References listed on IDEAS

    as
    1. Porfirio Guevara & Robert Hill & Michael Scholz, 2017. "Hedonic indexes for public and private housing in Costa Rica," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 10(1), pages 140-155, February.
    2. Nielsen, Jens Perch & Sperlich, Stefan, 2003. "Prediction of Stock Returns: A New Way to Look at It," ASTIN Bulletin, Cambridge University Press, vol. 33(2), pages 399-417, November.
    3. Scholz, Michael & Sperlich, Stefan & Nielsen, Jens Perch, 2016. "Nonparametric long term prediction of stock returns with generated bond yields," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 82-96.
    4. Wendner, Ronald, 2015. "Do positional preferences for wealth and consumption cause inter-temporal distortions?," MPRA Paper 64086, University Library of Munich, Germany.
    5. Sofie R. Waltl, 2016. "A hedonic house price index in continuous time," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 9(4), pages 648-670, October.
    6. Miriam Steurer & Caroline Bayr, 2015. "Quantifying Urban Sprawl using Land Use Data," Graz Economics Papers 2015-06, University of Graz, Department of Economics.
    7. Gabriel Bachner, 2015. "Land Transport Systems under Climate Change: A Macroeconomic Assessment of Adaptation Measures for the Case of Austria," Graz Economics Papers 2015-01, University of Graz, Department of Economics.
    8. Donnelly, Catherine & Guillen, Montserrat & Nielsen, Jens Perch & Pérez-Marín, Ana Maria, 2018. "Implementing Individual Savings Decisions For Retirement With Bounds On Wealth," ASTIN Bulletin, Cambridge University Press, vol. 48(1), pages 111-137, January.
    9. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Benchmark; Cross validation; Prediction; Stock returns;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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