Main Conference Day 2 (Please note this is the 2023 agenda*) - GMT (Greenwich Mean Time, GMTZ)
Main Conference Day 2 (Please note this is the 2023 agenda*) - GMT (Greenwich Mean Time, GMTZ)
- Barney Rowe - Senior Quantitative Analyst, Fidelity International
- Raphaƫl Douady - Research Professor, University of Paris 1 Pantheon Sorbonne
- Blanka Horvath - Associate Professor in Mathematical and Computational Finance, University of Oxford
This paper presents the development and implementation of a novel Deep Distributional Reinforcement Learning (DDRL) approach in the field of quantitative finance: the Distributional Soft Actor-Critic (DSAC) with an LSTM embedding. The model is built to further stabilize the performance of the widely used deep reinforcement learning model Soft Actor Critic (SAC) and is compared against traditional baselines such as Hierarchical Risk Parity, Minimum Variance Portfolio, DJIA and equal weight portfolio. The results show increased returns with less risk associated and stability over Soft Actor Critic and traditional baselines both in random path validation and backtest with daily frequency. The distributional component allows the model to incorporate an inherent sense of risk. The embedding enhances the temporal-dependency awareness and the observation space is composed of multiple features based upon past returns. Thus, this paper opens the door to further research in the use of deep distributional reinforcement learning models in the context of finance.