Preface

Miquel Noguer i Alonso, Daniel Bloch and David Pacheco Aznar

Preface

Contents

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

10.

Pricing options with temporal difference backpropagation

11.

Pricing American options

12.

Daily price limits

13.

Portfolio optimisation

Appendix

Artificial intelligence (AI) and machine learning are profoundly reshaping how industries operate, from automating processes to optimizing efficiency and supporting more informed decision-making. In the financial sector, these technologies have led to new methods in portfolio management, option pricing, hedging, risk management and retail banking, particularly within the quantitative finance domain.

This two-volume work provides a thorough examination of AI’s role in finance, blending rigorous theoretical underpinnings with practical insights. Artificial Intelligence in Finance, Volume 1: Fundamentals and Applications lays the groundwork by introducing the key mathematical and algorithmic principles of machine learning, with detailed coverage of supervised and unsupervised learning as well as reinforcement learning. It delves into established models such as neural networks, deep learning architectures and Transformers, and it explores the expanding capabilities of large language models (including ChatGPT, LLaMA, LaMDA and GPT-4) in areas such as sentiment analysis and generative modelling.

Building upon this foundation, ArtificialIntelligence in Finance, Volume 2: Reinforcement

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