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arXiv Statistics ML By Andrea Macr\`i, Sebastian Jaimungal, Fabrizio Lillo

Deep reinforcement learning for optimal trading with partial information

Read the original on arXiv Statistics ML →

The paper explores optimal trading under partial information, modeling the trading signal as an Ornstein‑Uhlenbeck process with regime switching. It proposes three reinforcement‑learning approaches that integrate recurrent neural networks to filter latent market regimes, comparing a one‑step method and two two‑step methods that use posterior regime probabilities or signal forecasts. Simulations and an equity pairs‑trading case study show that using posterior probability estimates of the latent regime yields the best cumulative rewards and more interpretable strategies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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