IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v41y2025i4p1395-1403.html

Avoiding overconfidence: Evidence from the M6 financial competition

Author

Listed:
  • Makridakis, Spyros
  • Spiliotis, Evangelos
  • Michailidis, Maria

Abstract

The M6 competition aimed to identify methods that can accurately forecast asset returns and exploit such forecasts to make efficient investments. Specifically, the forecasting track of the competition required participants to estimate the probability that each of the 100 selected assets would be ranked within the first, second, third, fourth, or fifth quintile with regards to their relative percentage returns. Overall, less than 25% of the teams managed to estimate the probabilities more precisely than a benchmark that assumed equal probabilities for all quintiles. Moreover, those that did so reported inconsistent performance across the 12 submission points and minor forecast accuracy improvements. We identify price volatility as a key driver of forecast deterioration and show that avoiding overconfidence by assuming similar probabilities for symmetric quintiles can improve both forecast accuracy and portfolio efficiency. Interestingly, our findings hold true even when simple methods are employed to estimate the base predictions and investment weights.

Suggested Citation

  • Makridakis, Spyros & Spiliotis, Evangelos & Michailidis, Maria, 2025. "Avoiding overconfidence: Evidence from the M6 financial competition," International Journal of Forecasting, Elsevier, vol. 41(4), pages 1395-1403.
    • Handle: RePEc:eee:intfor:v:41:y:2025:i:4:p:1395-1403
    DOI: 10.1016/j.ijforecast.2024.10.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016920702400102X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2024.10.001?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. Xiaoqian Wang & Rob J Hyndman, 2024. "Online Conformal Inference for Multi-Step Time Series Forecasting," Monash Econometrics and Business Statistics Working Papers 20/24, Monash University, Department of Econometrics and Business Statistics.
    2. Wang, Xiaoqian & Hyndman, Rob J. & Wickramasuriya, Shanika L., 2025. "Optimal forecast reconciliation with time series selection," European Journal of Operational Research, Elsevier, vol. 323(2), pages 455-470.
    3. Jun Wang & Lihong Wen & Lu Xiao & Chaojie Wang, 2024. "Time-series forecasting of mortality rates using transformer," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2024(2), pages 109-123, February.
    4. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    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. Gabor Petnehazi & Laith Al Shaggah & Jozsef Gall & Bernadett Aradi, 2025. "Zero-Shot Forecasting Mortality Rates: A Global Study," Papers 2505.13521, arXiv.org.
    2. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    3. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    4. Hasan Engin Duran, 2025. "The future of urban hierearchy and Zipf law: ARIMA and BATS forecasting," Letters in Spatial and Resource Sciences, Springer, vol. 18(1), pages 1-14, December.
    5. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    6. Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro & Kleyton da Costa & Nemias Saboya & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2025. "Time series forecasting via integrating a filtering method: an application to electricity consumption," Computational Statistics, Springer, vol. 40(9), pages 5023-5042, December.
    7. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    8. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    9. Tao XIONG & LI Chongguang & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    10. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    11. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    12. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    13. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    14. Thomas Horvath & Peter Huber & Ulrike Huemer & Helmut Mahringer & Philipp Piribauer & Mark Sommer & Stefan Weingärtner, 2022. "Mittelfristige Beschäftigungsprognose für Österreich und die Bundesländer. Berufliche und sektorale Veränderungen 2021 bis 2028," WIFO Studies, WIFO, number 32632284.
    15. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    16. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    17. Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org, revised Sep 2024.
    18. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    19. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on Manufacturing Sales in South Africa," Economies, MDPI, vol. 11(6), pages 1-17, May.
    20. Fijorek Kamil & Leśniewska Agnieszka, 2012. "Statistical Forecasting of the Indicators of Polish Airport’s Operations," Folia Oeconomica Stetinensia, Sciendo, vol. 11(1), pages 7-7, January.

    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:1395-1403. 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.