Machine Learning in Quantitative Finance: History, Theory, and Applications

By William McGhee

Written by a senior and well-known member of the Quantitative Finance community who currently runs a research group at a major investment bank, the book will demonstrate the use of machine learning techniques to tackle traditional data science type problems - time-series analysis and the prediction of realised volatility but will also look at novel applications. For example, the Universal Approximation Theorem of Neural Networks shows that a neural network can be used to approximate any function (subject to a number of weak conditions), although how the network is trained is not given. This will be explored within the book. Specific applications will include using a trained neural network to represent market-standard volatility smile models (such as SABR) as well as complex derivative pricing. The book will also potentially look at training a network via reinforcement learning to risk manage a derivatives portfolio. Readers will be attracted by a comprehensive presentation of the techniques available, with the historical perspective providing intuitive understanding of their development, combined with a range of practical examples from the trading floor.

Key features:

Describes modern machine learning techniques including deep neural networks, reinforcement learning, long-short term memory networks, etc.Provides applications of these techniques to problems within Quantitative Finance (including applications to derivatives modelling)Presents the historical development of the subject from MENACE to Alpha Go Zero and AlphaZero

• Format: Hardcover
• Language: English
• ISBN: 1119524342
• ISBN13: 9781119524342
• Release Date: August 2023
• Publisher: Wiley
• Length: 304 Pages