Deep Reinforcement Learning Research Directions in Quantitative Finance
Summary
The document is a short reading list for exploring deep reinforcement learning and related machine-learning methods in quantitative finance. It points readers toward work on optimal execution, foreign-exchange trading, price formation, options pricing, and cryptocurrency trading, while also mentioning broader research by several authors. One cited introductory example applies distributional reinforcement learning to execution and reportedly includes code; other references are described as more comprehensive or focused on prediction and price formation.
The material offers directions for finding papers rather than explaining or evaluating a specific method. It gives no performance data, experimental design, or comparisons that would establish trading effectiveness. A respondent also asserts that reinforcement learning is not used in industry, while another points to a more recent algorithmic-trading paper; these are brief opinions and pointers, not evidence resolving practical adoption. Readers would need to examine the cited studies directly, especially for assumptions, validation, and market relevance.
Key ideas
- The suggested literature spans execution, foreign exchange, price formation, options, and crypto trading.
- Distributional reinforcement learning is mentioned as an approach to optimal execution.
- The response recommends academic search tools and researcher publication pages for further reading.
- The document summarizes pointers and opinions rather than presenting results or evaluating strategies.
- Claims about industry adoption are unsupported within the discussion.
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Full text
# Deep Reinforcement Learning in Quant Finance? # Deep Reinforcement Learning in Quant Finance? I've been struggling to find engaging papers on the application of deep reinforcement learning in quantitative risk analysis, portfolio management, algorithmic trading and/or options pricing. What are some papers with interesting findings on this topic that you know of? ## Answer by pSrIoGcNeAsLs - bye stackGPT (score 3) https://quant.stackexchange.com/a/60438 You can find a lot of good papers by just typing keywords like "deep reinforcement learning finance" in the arXiv or Google Scholar or looking at top researchers websites which provides an overflow of applications and research directions to engage with. Anyway, here are a few off the top of my head: If you are looking for a more introductory level paper, this master's thesis applied distributional reinforcement learning to the problem of optimal execution and provides code which is quite rare & nice. If you want more comprehensive, recent work, I enjoyed "Optimal Execution of Foreign Securities" by Cartea & Arribas which was a unique application of machine learning to optimal trading in the FX market - found here. Rama Cont and Justin Sirgnano wrote a short paper back in 2018 with respect to the problem of detecting price formation using deep learning here. Charles Lehalle also has some interesting work on not so much deep learning, but reinforcement learning you could find on his Google Scholar page. I really liked this paper by Zhang et al (2019) on deep learning to also predict price formation - it was covered in a lot of detail and they include a lot of citations if you want to look around some more. While you asked for deep reinforcement learning, here's an application of q-learning to options pricing (there are many more). You can also find older, but still interesting papers like this which applies deep reinforcement learning to trading cryptos - warning: littered with typos the last time I read it. ## Answer by DeepRL (score 1) https://quant.stackexchange.com/a/60495 I've worked in algorithmic trading for years. RL (or deep RL for that matter) is not used in this industry. ## Answer by OxAT (score 0) https://quant.stackexchange.com/a/63760 You can check the recent article "Deep Reinforcement Learning for Algorithmic Trading" by Cartea et. al. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3812473
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