Knowledge discovery with Deep RL for selecting financial hedges

AAAI 2021

Knowledge discovery with Deep RL for selecting financial hedges

Feb 13, 2021
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Abstract: Can an asset manager gain knowledge from different data sources to select the right hedging strategy for his portfolio? We use Deep Reinforcement Learning (Deep RL or DRL) to extract information from not only past performances of the hedging strategies but also additional contextual information like risk aversion, correlation data, credit information and estimated earnings per shares. Our contributions are threefold: (i) the use of contextual information also referred to as augmented state in DRL, (ii) the impact of a one period lag between observations and actions that is more realistic for an asset management environment, (iii) the implementation of a new repetitive train test method called walk forward analysis, similar in spirit to cross validation for time series. Although our experiment is on trading bots, it can easily be translated to other bot environments that operate in sequential environment with regime changes and noisy data. Our experiment for an augmented asset manager interested in finding the best portfolio for hedging strategies achieves superior returns and lower risk. Authors: Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay, Jamal Atif, Rida Laraki (AI Square Connect, Dauphine PSL, ULCO)

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