Deep Learning has been successfully applied to many application domains, yet its
advantages have been slow to emerge for time series forecasting. For example, in the well …

The goal of the M6 forecasting competition was to shed light on the efficient market
hypothesis by evaluating the forecasting abilities of participants and performance of their …

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 …

We perform a comparative analysis of a wide array of approaches for the problem of
forecasting the relative performance among different tradable assets in the framework of the …

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 …

In this paper, we address the problem of probabilistic forecasting using an adaptive volatility
method rooted in classical time-varying volatility models and leveraging online stochastic …

Regardless of the selected asset class and the level of model complexity (Transformer
versus LSTM versus Perceptron/RNN), the GMADL loss function produces superior results …

Active investing strategies have poor historical long-term performance compared to passive
strategies. Furthermore, many active strategies use forecasting of various signals even …

Abstract The Makridakis M6 Financial Duathalon competition builds on prior M-competitions
that focus on the properties of point and probabilistic forecasts of random variables by also …

It is well known that, in many areas, experts are no better at forecasting than laypersons. For
example, it has been shown that stock portfolio managers and other experts are in general …