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Machine Learning for Finance

You're reading from Machine Learning for Finance Principles and practice for financial insiders

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789136364

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Jannes Klaas

James Le

View More author details

Table of Contents (15) Chapters

Machine Learning for Finance

Contributors

Preface

Other Books You May Enjoy

1. Neural Networks and Gradient-Based Optimization

2. Applying Machine Learning to Structured Data FREE CHAPTER

3. Utilizing Computer Vision

4. Understanding Time Series

5. Parsing Textual Data with Natural Language Processing

6. Using Generative Models

7. Reinforcement Learning for Financial Markets

8. Privacy, Debugging, and Launching Your Products

9. Fighting Bias

10. Bayesian Inference and Probabilistic Programming

Index

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...

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Authors (2)

James Le

James Le

James Le is currently a master's student studying Computer Science at Rochester Institute of Technology. He is pursuing a research path in Computer Vision using Deep Learning. Moreover, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.

See other products by James Le

Jannes Klaas

Jannes Klaas

Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies. Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.

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