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Why The Return Notion Matters

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  • GREGOR DORFLEITNER

    (Institut für Statistik und Mathematische Wirtschaftstheorie Augsburg, Universität Augsburg, D-86135 Augsburg, Germany)

Abstract

Returns can be defined as log returns or as simple returns. Whereas on a numerical level the difference between these two terms is small as long as the return values are close to zero, there can be non-negligible differences if we look at expected values and (co)variances in a stochastic context. This paper examines the consequences of mixing up the two return terms when variances and convariances are considered. Three applications show that these consequences can be severe in the sense of suboptimal portfolio selection or invalid betas. The paper argues that more awareness of the suited return term is necessary.

Suggested Citation

  • Gregor Dorfleitner, 2003. "Why The Return Notion Matters," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 73-86.
    • Handle: RePEc:wsi:ijtafx:v:06:y:2003:i:01:n:s0219024903001797
    DOI: 10.1142/S0219024903001797
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    References listed on IDEAS

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    1. Dale J. Poirier, 1995. "Intermediate Statistics and Econometrics: A Comparative Approach," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161494, April.
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    Cited by:

    1. M. Reza Bradrania & Maurice Peat & Stephen Satchell, 2022. "Liquidity Costs, Idiosyncratic Volatility and Expected Stock Returns," Papers 2211.04695, arXiv.org.
    2. Christian Klein & Bernhard Zwergel & Sebastian Heiden, 2009. "On the existence of sports sentiment: the relation between football match results and stock index returns in Europe," Review of Managerial Science, Springer, vol. 3(3), pages 191-208, November.
    3. Svetlozar Rachev & Nancy Asare Nyarko & Blessing Omotade & Peter Yegon, 2023. "Bachelier's Market Model for ESG Asset Pricing," Papers 2306.04158, arXiv.org.
    4. Pedro Antonio Martín-Cervantes & María del Carmen Valls Martínez, 2023. "Unraveling the relationship between betas and ESG scores through the Random Forests methodology," Risk Management, Palgrave Macmillan, vol. 25(3), pages 1-29, September.
    5. Asparouhova, Elena & Bessembinder, Hendrik & Kalcheva, Ivalina, 2010. "Liquidity biases in asset pricing tests," Journal of Financial Economics, Elsevier, vol. 96(2), pages 215-237, May.
    6. Gregor Dorfleitner & Carina Lung, 2018. "Cryptocurrencies from the perspective of euro investors: a re-examination of diversification benefits and a new day-of-the-week effect," Journal of Asset Management, Palgrave Macmillan, vol. 19(7), pages 472-494, December.
    7. Reza Bradrania, M. & Peat, Maurice & Satchell, Stephen, 2015. "Liquidity costs, idiosyncratic volatility and expected stock returns," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 394-406.
    8. Michael C. Nwogugu, 2020. "Decision-Making, Sub-Additive Recursive "Matching" Noise And Biases In Risk-Weighted Stock/Bond Index Calculation Methods In Incomplete Markets With Partially Observable Multi-Attribute Pref," Papers 2005.01708, arXiv.org.

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