Artificial Intelligence in Finance, Volume 2: Reinforcement Learning Theory and Practice
First published:
ISBN: 9781782724544
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Applications of artificial intelligence in finance
Miquel Noguer i Alonso, Daniel Bloch and David Pacheco Aznar
Preface
Introduction
Markov decision problems
Learning the optimal policy
Reinforcement learning revisited
Temporal difference learning revisited
Stochastic approximation in Markov decision processes
Large language models: reasoning and reinforcement learning
Deep reinforcement learning
Applications of artificial intelligence in finance
Pricing options with temporal difference backpropagation
Pricing American options
Daily price limits
Portfolio optimisation
Appendix
9.1 REINFORCEMENT LEARNING FOR CREDIT VALUATION ADJUSTMENT HEDGING
Credit valuation adjustment (CVA) is the change in the market value of derivative instruments due to counterparty credit risk. It represents the discount to the standard derivative value that a buyer would offer after taking into account the possibility of a counterparty’s default. In this section, Miquel Noguer i Alonso and Ivan Zhdankin look at the use of reinforcement learning models to hedge the counterparty credit risk.11 This section is based on the working paper by Noguer i Alonso and Zhdankin (2022).
9.1.1 Literature review
Many authors and practitioners have explored different potential user cases on asset allocation, trading and delta hedging domains. Pioneers in the research and use of reinforcement learning in finance include Moody and Saffell (1998). There is a growing corpus of literature exploring modelling applications in reinforcement learning (see, for example, Kolm and Ritter 2019b; Kondratyev and Schwarz 2019; Dixon and Halperin 2020; Du et al 2021; Halperin 2020; Zhang et al 2019). In particular, we refer the reader to the excellent book by Dixon et al (2020) as well as to preprints (see, for
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