  This article describes the methods that achieved 4th and 6th place in the
forecasting and investment challenges, respectively, of the M6 competition,
ultimately securing the 1st place in the overall duathlon ranking. In the
forecasting challenge, we tested a novel meta-learning model that utilizes
hypernetworks to design a parametric model tailored to a specific family of
forecasting tasks. This approach allowed us to leverage similarities observed
across individual forecasting tasks while also acknowledging potential
heterogeneity in their data generating processes. The model's training can be
directly performed with backpropagation, eliminating the need for reliance on
higher-order derivatives and is equivalent to a simultaneous search over the
space of parametric functions and their optimal parameter values. The proposed
model's capabilities extend beyond M6, demonstrating superiority over
state-of-the-art meta-learning methods in the sinusoidal regression task and
outperforming conventional parametric models on time-series from the M4
competition. In the investment challenge, we adjusted portfolio weights to
induce greater or smaller correlation between our submission and that of other
participants, depending on the current ranking, aiming to maximize the
probability of achieving a good rank.
