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
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Chapter 5. Parsing Textual Data with Natural Language Processing

It's no accident that Peter Brown, Co-CEO of Renaissance Technologies, one of the most successful quantitative hedge funds of all time, had previously worked at IBM, where he applied machine learning to natural language problems.

As we've explored in earlier chapters, in today's world, information drives finance, and the most important source of information is written and spoken language. Ask any finance professional what they are actually spending time on, and you will find that a significant part of their time is spent on reading. This can cover everything from reading headlines on tickers, to reading a Form 10K, the financial press, or various analyst reports; the list goes on and on. Automatically processing this information can increase the speed of trades occurring and widen the breadth of information considered for trades while at the same time reducing overall costs.

Natural language processing (NLP) is making inroads...