The M6 forecasting competition, the sixth in the Makridakis’ competition sequence, focused on financial forecasting. Two key objectives of the M6 competition included assessing the validity of the Efficient Market Hypothesis (EMH) and determining whether competition participants were able to efficiently predict the market, thereby challenging the EMH. To address these objectives, the M6 competition investigated forecasting accuracy and investment performance on a universe of 100 publicly traded assets. The competition employed live evaluation on real data across multiple periods, a cross-sectional setting where participants predicted asset performance relative to that of other assets, and a direct evaluation of the utility of forecasts. In this way, we were able to measure the benefits of accurate forecasting and assess the importance of forecasting when making investment decisions.

Our findings to date based on our own analysis of the competition data highlight the challenges that participants faced when attempting to accurately forecast the relative performance of assets, the great difficulty associated with trying to consistently outperform the market, the limited connection between submitted forecasts and investment decisions, the value added by information exchange and the “wisdom of crowds”, as well as the usefulness of utilizing risk models when attempting to connect prediction and investing decisions. A draft paper describing the M6 forecasting competition, the data used, and our main results and major findings can be found here. The data and submissions made to the M6 forecasting competition can be found in the M6 GitHub repository.

Similar to the M4 and M5 competitions, we are organizing a special issue for the International Journal of Forecasting (IJF) exclusively devoted to all aspects of the M6 forecasting competition. The special issue will include, among others, invited methodological papers based on the top-performing submissions, and full research papers. The purpose of this call is to attract high-quality papers for inclusion in this special issue. Such papers should focus on the following areas.

● Applicability and implications of results – Views of practitioners and academics.
● Insights on the factors and modeling strategies that determined the performance of the winning submissions.
● Insights on the difficulties present in financial forecasting and linking forecasts with investment strategies.
● Insights on the importance of judgment in financial forecasting.
● Recent advances in statistical and machine learning methods used for financial forecasting and investment applications.

We encourage authors to use the rich dataset collected by the organizers of the M6 forecasting competition (which can be found here) to perform their own analyses as well as expanding on the analysis presented by the organizers of the competition (also authors of the M6 main results paper), which can be found here.

We invite you to indicate your interest in contributing a discussion paper for the M6 special issue by sending a proposal (500 words maximum) by November 30, 2023 to Fotios Petropoulos. We will then evaluate all proposals, and notify the authors by December 31, 2023. Selected papers are to be submitted to the journal’s submission management system by May 31, 2023. The maximum length for each full paper should not exceed 8000 words. All submitted papers will undergo IJF’s rigorous double-blind refereeing process. In-line with the journal’s guidelines, the authors of accepted papers are expected to share their data and code (where relevant), while paper’s findings are expected to be reproduced before publication.

For further information about the Special Issue, please contact the guest editors:

Professor Fotios Petropoulos, University of Bath, UK, [email protected]
Professor Spyros Makridakis, University of Nicosia, Cyprus, [email protected]
Dr. Evangelos Spiliotis, National Technical University of Athens, Greece, [email protected]
Professor Norman Swanson, Rutgers University, USA, [email protected]