Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
Contributors
Preface
Other Books You May Enjoy
Index

VAEs for time series


This section covers the how and why of time series VAEs and gives a couple of examples where they have been used. Time series are such a big topic in finance that Chapter 4, Understanding Time Series, is heavily focused to it.

Autoencoders have found applications in connection to time series as they are able to encode a long time series into a single, descriptive vector. This vector can then, for example, be used to efficiently compare one time series to another time series, based on specific and complex patterns that cannot be captured with a simple correlation, for instance.

Consider the 2010 "Flash Crash." On May 6, 2010, starting at 02:32, US markets saw a major loss of value. The Dow Jones Industrial Average lost about 9%, which equates to about a trillion dollars' worth of value being wiped out in a couple of minutes. 36 minutes later, the crash was over, most of the lost value was regained, and people started wondering what on earth had just happened.

Five years...