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Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models

Benhamou, Éric; Saltiel, David; Tabachnik, Serge; Wong, Sui Kai; Chareyron, François (2021), Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models. https://basepub.dauphine.psl.eu/handle/123456789/22204

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DTU-VolatilityTargetting.pdf (1.287Mb)
Type
Document de travail / Working paper
Date
2021
Series title
Preprint Lamsade
Published in
Paris
Metadata
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Author(s)
Benhamou, Éric
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Saltiel, David
Tabachnik, Serge
Wong, Sui Kai
Chareyron, François
Abstract (EN)
Can an agent efficiently learn to distinguish extremely similar financial models in an environment dominated by noise and regime changes? Standard statistical methods based on averaging or ranking models fail precisely because of regime changes and noisy environments. Additional contextual information in Deep Reinforcement Learning (DRL), helps training an agent distinguish different financial models whose time series are very similar. Our contributions are four-fold: (i) we combine model-based and modelfree Reinforcement Learning (RL). The last model-free RL allows us selecting the different models, (ii) we present a concept, called "walk-forward analysis", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent, (iii) we present a method based on the importance of features that looks like the one in gradient boosting methods and is based on features sensitivities, (iv) last but not least, we introduce the concept of statistical difference significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms the benchmarks in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe ratio, Sortino, maximum drawdown, maximum drawdown over volatility.
Subjects / Keywords
Features sensitivity; Walk forward; Portfolio allocation; Model-free; Model-based; Deep Reinforcement learning

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