Deep Learning in Quantitative Finance (Wiley Finance) - Hardcover

Green, Andrew

Hardcover

ISBN 10: 1119685249 ISBN 13: 9781119685241

Publisher: Wiley, 2026

Synopsis

The complete and practical guide to one of the hottest topics in quantitative finance

Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance. Inside, you’ll find over ten chapters which apply deep learning to multiple use cases across quantitative finance. You’ll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text. Readers will be able to work through these examples directly.

This book is a complete resource on how deep learning is used in quantitative finance applications. It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics. You’ll also learn about the most important software frameworks. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.

• Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniques
• Offers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learning
• Demonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion website
• Introduces the most important software frameworks for applying deep learning within finance

This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.

About this title

About the Author

ANDREW GREEN FIMA MINSTP BA MA MAST DPHIL is a Managing Director, and Lead Rates and XVA Quant at Scotiabank with over twenty-five years of experience in quantitative finance. He has previously held leadership roles in XVA modelling at Lloyds Banking Group and Barclays Capital. He is also the author of XVA: Credit, Funding and Capital Valuation Adjustments (Wiley, 2015). Andrew has worked on interest rate, credit, and equity derivative model development and implementation during his career.

From the Back Cover

A STARTLINGLY INSIGHTFUL AND HANDS-ON GUIDE TO APPLYING DEEP LEARNING TECHNIQUES TO QUANTITATIVE FINANCE

Deep Learning in Quantitative Finance is a uniquely incisive and hands-on look at applying the latest advances in deep learning and machine learning technology to the rapidly evolving field of quantitative finance. Author Andrew Green walks you through basic and advanced subjects in quantitative finance deep learning, moving from the foundations of neural networks to the latest discoveries in CNNs, sequence models, autoencoders, generative AI like VAEs, GANs and diffusion models, and deep reinforcement learning.

You’ll learn how to approximate derivative values, solve PDEs and BSDEs with neural networks, enhance Monte Carlo models with deep learning, use deep semi-static replication for Bermudan and American options, map credit curves and generate exposure profiles, calibrate models and volatility surfaces, generate realistic market data, apply hedging methodologies and develop predictive market models.

A can’t-miss, hands-on guide―complete with complimentary access to a collection of Jupyter notebooks filled with tested, working Python code examples of the concepts discussed in the book―Deep Learning in Quantitative Finance is an essential read for quantitative finance practitioners everywhere.

From the Inside Flap

In Deep Learning in Quantitative Finance, XVA expert and quantitative finance and development practitioner Andrew Green delivers a startlingly insightful and up-to-date discussion of how to apply deep learning to quantitative finance. The author offers a complete resource on how deep learning is used in quantitative finance applications, introducing the basics of neural networks― including feedforward networks, optimization, and training―before covering more advanced topics.

Deep Learning in Quantitative Finance dives into advanced deep learning topics, including sequence models, generative AI―like VAEs, GANs, and diffusion models ― and deep reinforcement learning.

You’ll explore the latest deep learning research in quantitative finance, like approximate derivative values, solve PDEs and BSDEs with neural networks, enhance Monte Carlo models with deep learning, use deep semi-static replication for Bermudan and American options, map credit curves and generate exposure profiles, calibrate models and volatility surfaces, generate realistic market data, apply hedging methodologies and develop predictive market models. It offers case studies and access to a hands-on, companion GitHub repo complete with Jupyter notebooks containing working and tested Python code examples.

Perfect for quantitative finance practitioners interested in getting involved with deep learning in a practical and hands-on way, Deep Learning in Quantitative Finance is a practical handbook on a subject that will likely become even more influential in the field than it is today.

Edition details

• Publisher: Wiley
• Publication date: 2026
• Language: English
• ISBN 10: 1119685249
• ISBN 13: 9781119685241
• Binding: Hardcover
• Edition number: 1
• Number of pages: 736