  The trade off between risks and returns gives rise to multi-criteria
optimisation problems that are well understood in finance, efficient frontiers
being the tool to navigate their set of optimal solutions. Motivated by the
recent advances in the use of deep neural networks in the context of hedging
vanilla options when markets have frictions, we introduce the Efficient Hedging
Frontier (EHF) by enriching the pipeline with a filtering step that allows to
trade off costs and risks. This way, a trader's risk preference is matched with
an expected hedging cost on the frontier, and the corresponding hedging
strategy can be computed with a deep neural network.
  We further develop our framework to improve the EHF and find better hedging
strategies. By adding a random forest classifier to the pipeline to forecast
market movements, we show how the frontier shifts towards lower costs and
reduced risks, which indicates that the overall hedging performances have
improved. In addition, by designing a new recurrent neural network, we also
find strategies on the frontier where hedging costs are even lower.
