Quasi-average predictions and regression to the trend: An application to the M6 financial forecasting competition

Abstract

This paper presents the winning method that achieved fifth place overall in the M6 financial forecasting competition. The method is based on the idea that, under the efficient market hypothesis, it is often more effective to predict values close to the expected averages of categories and trends than to try to make precise predictions. By leveraging low-variability prediction methods, we forecast both the relative performance of multiple assets and their optimal investment positions. We demonstrate that combining asset-class and temporal averages yields modest but consistent advantages over reference indices. The results highlight the challenges of achieving above-average returns in efficient markets and the potential benefits of low-variability prediction methods in such contexts.