IDEAS home Printed from https://ideas.repec.org/a/eee/ecanpo/v78y2023icp995-1009.html
   My bibliography  Save this article

An empirical analysis of exchange-traded funds in the US

Author

Listed:
  • Valadkhani, Abbas
  • Moradi-Motlagh, Amir

Abstract

This paper evaluates the performance of 110 exchange-traded funds (ETFs) in the US by adopting a frontier directional distance model, assigning different weights to three measures of net return and risk in the last three, five and ten years. While the ranking of most ETFs changes through time, some remain fully efficient in terms of both return and risk. Not only can the results mimic traditional performance measures such as Sharpe or Sortino ratios, but also the proposed approach provides more flexibility in assessing the efficiency of the sample ETFs for long-term investors who may have different appetites for risk. This study reveals a few super-efficient ETFs that regardless of the weights allocated to return and risk perform as fully efficient in both aspects. The findings also show that top-performing ETFs are predominantly within the tech, medical devices, and semiconductor sectors, enjoying relatively higher gains and lower drawdowns.

Suggested Citation

  • Valadkhani, Abbas & Moradi-Motlagh, Amir, 2023. "An empirical analysis of exchange-traded funds in the US," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 995-1009.
  • Handle: RePEc:eee:ecanpo:v:78:y:2023:i:c:p:995-1009
    DOI: 10.1016/j.eap.2023.05.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0313592623000905
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eap.2023.05.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Basso, Antonella & Funari, Stefania, 2001. "A data envelopment analysis approach to measure the mutual fund performance," European Journal of Operational Research, Elsevier, vol. 135(3), pages 477-492, December.
    2. Mukashov, A., 2023. "Parameter uncertainty in policy planning models: Using portfolio management methods to choose optimal policies under world market volatility," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 187-202.
    3. John McHale, 1967. "Science, Technology, and Change," The ANNALS of the American Academy of Political and Social Science, , vol. 373(1), pages 120-140, September.
    4. Moradi-Motlagh, Amir & Jubb, Christine, 2020. "Examining irresponsible lending using non-radial inefficiency measures: Evidence from Australian banks," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 96-108.
    5. David Easley & David Michayluk & Maureen O’Hara and Tālis & J Putniņš, 2021. "The Active World of Passive Investing [Mutual fund’s R2 as predictor of performance]," Review of Finance, European Finance Association, vol. 25(5), pages 1433-1471.
    6. Martin Lettau & Ananth Madhavan, 2018. "Exchange-Traded Funds 101 for Economists," Journal of Economic Perspectives, American Economic Association, vol. 32(1), pages 135-154, Winter.
    7. Emrouznejad, Ali & Yang, Guo-liang, 2018. "A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 4-8.
    8. Boon L. Lee, 2011. "Efficiency of Research Performance of Australian Universities: A Reappraisal using a Bootstrap Truncated Regression Approach," Economic Analysis and Policy, Elsevier, vol. 41(3), pages 195-204, December.
    9. repec:eme:mfppss:v:42:y:2016:i:3:p:225-243 is not listed on IDEAS
    10. Po-Chi Chen & Ming-Miin Yu & Ching-Cheng Chang & Shih-Hsun Hsu & Shunsuke Managi, 2015. "Nonradial Directional Performance Measurement with Undesirable Outputs: An Application to OECD and Non-OECD Countries," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(03), pages 481-520.
    11. Arvid O. I. Hoffmann & Thomas Post & Tom Smith, 2017. "How return and risk experiences shape investor beliefs and preferences," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(3), pages 759-788, September.
    12. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    13. Barros, Carlos Pestana & Managi, Shunsuke & Matousek, Roman, 2012. "The technical efficiency of the Japanese banks: Non-radial directional performance measurement with undesirable output," Omega, Elsevier, vol. 40(1), pages 1-8, January.
    14. Tarnaud, Albane Christine & Leleu, Hervé, 2018. "Portfolio analysis with DEA: Prior to choosing a model," Omega, Elsevier, vol. 75(C), pages 57-76.
    15. Nemoto, Jiro & Goto, Mika, 1999. "Dynamic data envelopment analysis: modeling intertemporal behavior of a firm in the presence of productive inefficiencies," Economics Letters, Elsevier, vol. 64(1), pages 51-56, July.
    16. Carlos Pestana Barros & Stephanie Leach, 2006. "Performance evaluation of the English Premier Football League with data envelopment analysis," Applied Economics, Taylor & Francis Journals, vol. 38(12), pages 1449-1458.
    17. Hidemichi Fujii & Shunsuke Managi & Roman Matousek & Aarti Rughoo, 2018. "Bank efficiency, productivity, and convergence in EU countries: a weighted Russell directional distance model," The European Journal of Finance, Taylor & Francis Journals, vol. 24(2), pages 135-156, January.
    18. Díaz, Antonio & Esparcia, Carlos & López, Raquel, 2022. "The diversifying role of socially responsible investments during the COVID-19 crisis: A risk management and portfolio performance analysis," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 39-60.
    19. Lartey, Theophilus & James, Gregory A. & Danso, Albert, 2021. "Interbank funding, bank risk exposure and performance in the UK: A three-stage network DEA approach," International Review of Financial Analysis, Elsevier, vol. 75(C).
    20. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    21. Paul Simshauser, 2014. "The cost of capital for power generation in atypical capital market conditions," Economic Analysis and Policy, Elsevier, vol. 44(2), pages 184-201.
    22. Murthi, B. P. S. & Choi, Yoon K. & Desai, Preyas, 1997. "Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach," European Journal of Operational Research, Elsevier, vol. 98(2), pages 408-418, April.
    23. Valadkhani, Abbas & Roshdi, Israfil & Smyth, Russell, 2016. "A multiplicative environmental DEA approach to measure efficiency changes in the world's major polluters," Energy Economics, Elsevier, vol. 54(C), pages 363-375.
    24. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    25. Schuhmacher, Frank & Eling, Martin, 2011. "Sufficient conditions for expected utility to imply drawdown-based performance rankings," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2311-2318, September.
    26. Aparicio, Juan & Cordero, Jose M. & Gonzalez, Martin & Lopez-Espin, Jose J., 2018. "Using non-radial DEA to assess school efficiency in a cross-country perspective: An empirical analysis of OECD countries," Omega, Elsevier, vol. 79(C), pages 9-20.
    27. Blume, Marshall E, 1970. "Portfolio Theory: A Step Toward Its Practical Application," The Journal of Business, University of Chicago Press, vol. 43(2), pages 152-173, April.
    28. Tavana, Madjid & Izadikhah, Mohammad & Toloo, Mehdi & Roostaee, Razieh, 2021. "A new non-radial directional distance model for data envelopment analysis problems with negative and flexible measures," Omega, Elsevier, vol. 102(C).
    29. Martin Eling, 2006. "Performance measurement of hedge funds using data envelopment analysis," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(4), pages 442-471, December.
    30. Ioannis E. Tsolas, 2022. "Performance Evaluation of Utility Exchange-Traded Funds: A Super-Efficiency Approach," JRFM, MDPI, vol. 15(7), pages 1-10, July.
    31. Auer, Benjamin R. & Schuhmacher, Frank, 2013. "Robust evidence on the similarity of Sharpe ratio and drawdown-based hedge fund performance rankings," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 153-165.
    32. Carla Oliveira Henriques & Maria Elisabete Neves & Licínio Castelão & Duc Khuong Nguyen, 2022. "Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach," Annals of Operations Research, Springer, vol. 313(1), pages 341-366, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Saleh, Ali Salman & Moradi-Motlagh, Amir & Zeitun, Rami, 2020. "What are the drivers of inefficiency in the Gulf Cooperation Council banking industry? A comparison between conventional and Islamic banks," Pacific-Basin Finance Journal, Elsevier, vol. 60(C).
    3. Babalos, Vassilios & Caporale, Guglielmo Maria & Philippas, Nikolaos, 2012. "Efficiency evaluation of Greek equity funds," Research in International Business and Finance, Elsevier, vol. 26(2), pages 317-333.
    4. Tarnaud, Albane Christine & Leleu, Hervé, 2018. "Portfolio analysis with DEA: Prior to choosing a model," Omega, Elsevier, vol. 75(C), pages 57-76.
    5. Kouaissah, Noureddine, 2021. "Using multivariate stochastic dominance to enhance portfolio selection and warn of financial crises," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 480-493.
    6. Carlos S�nchez-Gonz�lez & Jos� Luis Sarto & Luis Vicente, 2013. "The efficiency of Spanish mutual funds companies: A slacks-based measure approach," Documentos de Trabajo dt2013-01, Facultad de Ciencias Económicas y Empresariales, Universidad de Zaragoza.
    7. Mohammad Reza TAVAKOLI BAGHDADABAD & Afsaneh NOORI HOUSHYAR, 2014. "Productivity and Efficiency Evaluation of US Mutual Funds," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(2), pages 120-143, March.
    8. Konstantinos Petridis & Nikolaos Kiosses & Ioannis Tampakoudis & Fouad Ben Abdelaziz, 2023. "Measuring the efficiency of mutual funds: Does ESG controversies score affect the mutual fund performance during the COVID-19 pandemic?," Operational Research, Springer, vol. 23(3), pages 1-29, September.
    9. Panayotis Alexakis & Ioannis Tsolas, 2011. "Appraisal of Mutual Equity Fund Performance Using Data Envelopment Analysis," Multinational Finance Journal, Multinational Finance Journal, vol. 15(3-4), pages 273-296, September.
    10. Tsolas, Ioannis E., 2014. "Precious metal mutual fund performance appraisal using DEA modeling," Resources Policy, Elsevier, vol. 39(C), pages 54-60.
    11. Pablo Solórzano-Taborga & Ana Belén Alonso-Conde & Javier Rojo-Suárez, 2020. "Data Envelopment Analysis and Multifactor Asset Pricing Models," IJFS, MDPI, vol. 8(2), pages 1-18, April.
    12. Carla Oliveira Henriques & Maria Elisabete Neves & Licínio Castelão & Duc Khuong Nguyen, 2022. "Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach," Annals of Operations Research, Springer, vol. 313(1), pages 341-366, June.
    13. Catarina Alexandra Neves Proença & Maria Elisabete Duarte Neves & Maria Castelo Baptista Gouveia & Mara Teresa Silva Madaleno, 2023. "Technological, healthcare and consumer funds efficiency: influence of COVID-19," Operational Research, Springer, vol. 23(2), pages 1-42, June.
    14. Andreu, Laura & Serrano, Miguel & Vicente, Luis, 2019. "Efficiency of mutual fund managers: A slacks-based manager efficiency index," European Journal of Operational Research, Elsevier, vol. 273(3), pages 1180-1193.
    15. Sepideh Kaffash & Marianna Marra, 2017. "Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds," Annals of Operations Research, Springer, vol. 253(1), pages 307-344, June.
    16. Moradi-Motlagh, Amir & Jubb, Christine, 2020. "Examining irresponsible lending using non-radial inefficiency measures: Evidence from Australian banks," Economic Analysis and Policy, Elsevier, vol. 66(C), pages 96-108.
    17. J. Francisco Rubio & Neal Maroney & M. Kabir Hassan, 2018. "Can Efficiency of Returns Be Considered as a Pricing Factor?," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 25-54, June.
    18. Henriques, C.O. & Marcenaro-Gutierrez, O.D., 2021. "Efficiency of secondary schools in Portugal: A novel DEA hybrid approach," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    19. Joro, Tarja & Na, Paul, 2006. "Portfolio performance evaluation in a mean-variance-skewness framework," European Journal of Operational Research, Elsevier, vol. 175(1), pages 446-461, November.
    20. Glawischnig, Markus & Sommersguter-Reichmann, Margit, 2010. "Assessing the performance of alternative investments using non-parametric efficiency measurement approaches: Is it convincing?," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 295-303, February.

    More about this item

    Keywords

    Performance measures; Exchange-traded funds; Risk; Return; Efficiency frontier;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecanpo:v:78:y:2023:i:c:p:995-1009. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/economic-analysis-and-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.