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

Advantage actor-critic models


Q-learning, as we saw in the previous sections, is quite useful but it does have its drawbacks. For example, as we have to estimate a Q value for each action, there has to be a discrete, limited set of actions. So, what if the action space is continuous or extremely large? Say you are using an RL algorithm to build a portfolio of stocks.

In this case, even if your universe of stocks consisted only of two stocks, say, AMZN and AAPL, there would be a huge amount of ways to balance them: 10% AMZN and 90% AAPL, 11% AMZM and 89% AAPL, and so on. If your universe gets bigger, the amount of ways you can combine stocks explodes.

A workaround to having to select from such an action space is to learn the policy, , directly. Once you have learned a policy, you can just give it a state, and it will give back a distribution of actions. This means that your actions will also be stochastic. A stochastic policy has advantages, especially in a game theoretic setting.

Imagine you...