IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v41y2025i4p1404-1412.html

Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition

Author

Listed:
  • Kaltsounis, Anastasios
  • Theodorou, Evangelos
  • Spiliotis, Evangelos
  • Assimakopoulos, Vassilios

Abstract

The M6 competition evaluated investment performance over a period of one year, contributing to the efficient market hypothesis debate. This paper provides further insights into the outcomes of the competition by unraveling the effect that team engagement and performance consistency had on the final results. First, we identify three different types of engagement and investigate their relationship with portfolio efficiency, also making useful observations about the learning effect implied by a re-submission process. Then, we analyze the monthly performance of the teams and determine whether it aligned with their global performance or was affected significantly by extreme instances. Our results suggest that consistency is more important than engagement for making profitable investments. Nevertheless, we identify many cases where both regular portfolio updates and luck provided an advantage.

Suggested Citation

  • Kaltsounis, Anastasios & Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Unraveling the effect of engagement and consistency in the results of the M6 forecasting competition," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1404-1412.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:4:p:1404-1412
    DOI: 10.1016/j.ijforecast.2025.04.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016920702500038X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2025.04.002?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Athanasopoulos, George & Hyndman, Rob J., 2011. "The value of feedback in forecasting competitions," International Journal of Forecasting, Elsevier, vol. 27(3), pages 845-849.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. Syntetos, Aris A. & Boylan, John E., 2005. "The accuracy of intermittent demand estimates," International Journal of Forecasting, Elsevier, vol. 21(2), pages 303-314.
    6. Mark Kritzman & Sébastien Page & David Turkington, 2010. "In Defense of Optimization: The Fallacy of 1/N," Financial Analysts Journal, Taylor & Francis Journals, vol. 66(2), pages 31-39, March.
    7. Spyros Makridakis & Chris Fry & Fotios Petropoulos & Evangelos Spiliotis, 2022. "The Future of Forecasting Competitions: Design Attributes and Principles," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 96-113, April.
    8. Sujit R. Das & Dmitri Kaznachey & Mukul Goyal, 2014. "Computing optimal rebalance frequency for log-optimal portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1489-1502, January.
    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. Gelmini, Matteo & Uberti, Pierpaolo, 2024. "The equally weighted portfolio still remains a challenging benchmark," International Economics, Elsevier, vol. 179(C).
    2. Max Heide & Benjamin R. Auer & Frank Schuhmacher, 2025. "Optimal Versus Naive Diversification in Commodity Futures Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(1), pages 3-22, January.
    3. Cirulli, Antonello & Walker, Patrick S., 2025. "Outperforming equal weighting," Economics Letters, Elsevier, vol. 255(C).
    4. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    5. Sleire, Anders D. & Støve, Bård & Otneim, Håkon & Berentsen, Geir Drage & Tjøstheim, Dag & Haugen, Sverre Hauso, 2022. "Portfolio allocation under asymmetric dependence in asset returns using local Gaussian correlations," Finance Research Letters, Elsevier, vol. 46(PB).
    6. Lan, Hairong & Wang, Liukai & Li, Mengting & Xiong, Yu & Li, Yuqing, 2025. "Network centrality, diversification, and portfolio returns: Economic insights from blockchain industry," Economic Modelling, Elsevier, vol. 152(C).
    7. Bao, Te & Diks, Cees & Li, Hao, 2018. "A generalized CAPM model with asymmetric power distributed errors with an application to portfolio construction," Economic Modelling, Elsevier, vol. 68(C), pages 611-621.
    8. Jacobs, Heiko & Müller, Sebastian & Weber, Martin, 2014. "How should individual investors diversify? An empirical evaluation of alternative asset allocation policies," Journal of Financial Markets, Elsevier, vol. 19(C), pages 62-85.
    9. Francesco Lautizi, 2015. "Large Scale Covariance Estimates for Portfolio Selection," CEIS Research Paper 353, Tor Vergata University, CEIS, revised 07 Aug 2015.
    10. Wang, Jianzhou & Lv, Mengzheng & Wang, Shuai & Gao, Jialu & Zhao, Yang & Wang, Qiangqiang, 2024. "Can multi-period auto-portfolio systems improve returns? Evidence from Chinese and U.S. stock markets," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    11. Zhang, Caibin & Liang, Zhibin, 2022. "Optimal time-consistent reinsurance and investment strategies for a jump–diffusion financial market without cash," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    12. Yao, Haixiang & Huang, Jinbo & Li, Yong & Humphrey, Jacquelyn E., 2021. "A general approach to smooth and convex portfolio optimization using lower partial moments," Journal of Banking & Finance, Elsevier, vol. 129(C).
    13. Ledoit, Olivier & Wolf, Michael, 2025. "Markowitz portfolios under transaction costs," The Quarterly Review of Economics and Finance, Elsevier, vol. 100(C).
    14. Michele Costola & Massimiliano Caporin, 2016. "Rational Learning For Risk-Averse Investors By Conditioning On Behavioral Choices," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 1-26, March.
    15. Peralta, Gustavo & Zareei, Abalfazl, 2016. "A network approach to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 157-180.
    16. Wu, Zhongming & Sun, Kexin & Ge, Zhili & Allen-Zhao, Zhihua & Zeng, Tieyong, 2024. "Sparse portfolio optimization via ℓ1 over ℓ2 regularization," European Journal of Operational Research, Elsevier, vol. 319(3), pages 820-833.
    17. Liu, Weiyi, 2019. "Portfolio diversification across cryptocurrencies," Finance Research Letters, Elsevier, vol. 29(C), pages 200-205.
    18. Juan F. Monge & Mercedes Landete & Jos'e L. Ruiz, 2016. "Sharpe portfolio using a cross-efficiency evaluation," Papers 1610.00937, arXiv.org, revised Oct 2016.
    19. Guo, Xu & Wong, Wing-Keung, 2016. "Multivariate Stochastic Dominance for Risk Averters and Risk Seekers," MPRA Paper 70637, University Library of Munich, Germany.
    20. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    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:intfor:v:41:y:2025:i:4:p:1404-1412. 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.elsevier.com/locate/ijforecast .

    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.