  Financial portfolio management investment policies computed quantitatively by
modern portfolio theory techniques like the Markowitz model rely on a set on
assumptions that are not supported by data in high volatility markets. Hence,
quantitative researchers are looking for alternative models to tackle this
problem. Concretely, portfolio management is a problem that has been
successfully addressed recently by Deep Reinforcement Learning (DRL)
approaches. In particular, DRL algorithms train an agent by estimating the
distribution of the expected reward of every action performed by an agent given
any financial state in a simulator. However, these methods rely on Deep Neural
Networks model to represent such a distribution, that although they are
universal approximator models, they cannot explain its behaviour, given by a
set of parameters that are not interpretable. Critically, financial investors
policies require predictions to be interpretable, so DRL agents are not suited
to follow a particular policy or explain their actions. In this work, we
developed a novel Explainable Deep Reinforcement Learning (XDRL) approach for
portfolio management, integrating the Proximal Policy Optimization (PPO) with
the model agnostic explainable techniques of feature importance, SHAP and LIME
to enhance transparency in prediction time. By executing our methodology, we
can interpret in prediction time the actions of the agent to assess whether
they follow the requisites of an investment policy or to assess the risk of
following the agent suggestions. To the best of our knowledge, our proposed
approach is the first explainable post hoc portfolio management financial
policy of a DRL agent. We empirically illustrate our methodology by
successfully identifying key features influencing investment decisions, which
demonstrate the ability to explain the agent actions in prediction time.
