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Measurement errors in stock markets

Author

Listed:
  • Hachmi Ben Ameur

    (INSEEC Business School)

  • Fredj Jawadi

    (Universit of Evry)

  • Abdoulkarim Idi Cheffou

    (EDC Paris Business School)

  • Wael Louhichi

    (ESSCA Business School)

Abstract

This paper points to further measurement errors in stock markets. In particular, we show that the application of usual performance ratios to evaluate financial assets can lead to inappropriate findings and consequently wrong conclusions. To this end, we analyze standard performance ratios as well as extreme loss-based financial ratios and compare the conclusions with those provided by systemic risk measures. The application of these different measures to both conventional and Islamic stock indexes for developed and emerging countries in the context of the financial crisis yields two interesting results. First, the analysis of financial performance exhibits further measurement errors. Second, the consideration of extreme loss and systemic risk in computing performance measures increases the reliability of performance analysis.

Suggested Citation

  • Hachmi Ben Ameur & Fredj Jawadi & Abdoulkarim Idi Cheffou & Wael Louhichi, 2018. "Measurement errors in stock markets," Annals of Operations Research, Springer, vol. 262(2), pages 287-306, March.
    • Handle: RePEc:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-016-2138-z
    DOI: 10.1007/s10479-016-2138-z
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    1. Heitor Almeida & Murillo Campello & Antonio F. Galvao, 2010. "Measurement Errors in Investment Equations," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3279-3328.
    2. Drerup, Tilman & Enke, Benjamin & von Gaudecker, Hans-Martin, 2014. "Measurement Error in Subjective Expectation and the Empirical Content of Economic Models," MEA discussion paper series 201414, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    3. Barnett, William A. & Chauvet, Marcelle, 2011. "How better monetary statistics could have signaled the financial crisis," Journal of Econometrics, Elsevier, vol. 161(1), pages 6-23, March.
    4. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
    5. Graham, John R. & Harvey, Campbell R., 1996. "Market timing ability and volatility implied in investment newsletters' asset allocation recommendations," Journal of Financial Economics, Elsevier, vol. 42(3), pages 397-421, November.
    6. Chia-Ching Chang & Sheng-Syan Chen & Robin Chou & Chin-Wen Hsin, 2011. "Intraday return spillovers and its variations across trading sessions," Review of Quantitative Finance and Accounting, Springer, vol. 36(3), pages 355-390, April.
    7. Barnett, William A., 2012. "Getting it Wrong: How Faulty Monetary Statistics Undermine the Fed, the Financial System, and the Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262516888, December.
    8. William A. Barnett & Marcelle Chauvet & Heather L. R. Tierney, 2011. "Measurement Error in Monetary Aggregates: A Markov Switching Factor Approach," World Scientific Book Chapters, in: Financial Aggregation And Index Number Theory, chapter 7, pages 207-249, World Scientific Publishing Co. Pte. Ltd..
    9. Barnett, William, 2015. "Collaboration with or without Coauthorship: Rocket Science Versus Economic Science," Studies in Applied Economics 38, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    10. Xiaohong Chen & Han Hong & Denis Nekipelov, 2011. "Nonlinear Models of Measurement Errors," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 901-937, December.
    11. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Sociological Methods & Research, , vol. 46(3), pages 303-341, August.
    12. Barnett, William, 2015. "Collaboration with and without Coauthorship: Rocket Science Versus Economic Science," MPRA Paper 65236, University Library of Munich, Germany.
    13. Dowd, Kevin, 2000. "Adjusting for risk:: An improved Sharpe ratio," International Review of Economics & Finance, Elsevier, vol. 9(3), pages 209-222, July.
    14. Darolles, Serge & Gourieroux, Christian & Jasiak, Joann, 2009. "L-performance with an application to hedge funds," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 671-685, September.
    15. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    16. Michael Szenberg, 1996. "Intellectual Collaborative Experiences," The American Economist, Sage Publications, vol. 40(2), pages 3-4, October.
    17. Eling, Martin & Schuhmacher, Frank, 2007. "Does the choice of performance measure influence the evaluation of hedge funds?," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2632-2647, September.
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    Cited by:

    1. M. Karanasos & S. Yfanti & J. Hunter, 2022. "Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises," Annals of Operations Research, Springer, vol. 313(2), pages 1077-1116, June.
    2. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy, 2022. "Proper use of the modified Sharpe ratios in performance measurement: rearranging the Cornish Fisher expansion," Annals of Operations Research, Springer, vol. 313(2), pages 691-712, June.
    3. Chinnadurai Kathiravan & Murugesan Selvam & Sankaran Venkateswar & S. Balakrishnan, 2021. "Investor behavior and weather factors: evidences from Asian region," Annals of Operations Research, Springer, vol. 299(1), pages 349-373, April.
    4. Fredj Jawadi, 2016. "What Have We Learned from the 2007-08 Financial Crisis? Papers Presented at the Second International Workshop on Financial Markets and Nonlinear Dynamics (Paris, June 4-5, 2015)," Open Economies Review, Springer, vol. 27(5), pages 819-823, November.

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    More about this item

    Keywords

    Measurement error; Financial performance; Systemic risk; Var; CoVaR and MES;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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