  Can an asset manager plan the optimal timing for her/his hedging strategies
given market conditions? The standard approach based on Markowitz or other more
or less sophisticated financial rules aims to find the best portfolio
allocation thanks to forecasted expected returns and risk but fails to fully
relate market conditions to hedging strategies decision. In contrast, Deep
Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic
dependency between market information and hedging strategies allocation
decisions. In this paper, we present a realistic and augmented DRL framework
that: (i) uses additional contextual information to decide an action, (ii) has
a one period lag between observations and actions to account for one day lag
turnover of common asset managers to rebalance their hedge, (iii) is fully
tested in terms of stability and robustness thanks to a repetitive train test
method called anchored walk forward training, similar in spirit to k fold cross
validation for time series and (iv) allows managing leverage of our hedging
strategy. Our experiment for an augmented asset manager interested in sizing
and timing his hedges shows that our approach achieves superior returns and
lower risk.
