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Klaas - Machine Learning for Finance: the Practical Guide to Using Data-Driven Algorithms in Banking, Insurance, and Investments

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Klaas Machine Learning for Finance: the Practical Guide to Using Data-Driven Algorithms in Banking, Insurance, and Investments
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Cover; Copyright; Mapt upsell; Contributors; Table of Contents; Preface; Chapter 1: Neural Networks and Gradient-Based Optimization; Our journey in this book; What is machine learning?; Supervised learning; Unsupervised learning; Reinforcement learning; The unreasonable effectiveness of data; All models are wrong; Setting up your workspace; Using Kaggle kernels; Running notebooks locally; Installing TensorFlow; Installing Keras; Using data locally; Using the AWS deep learning AMI; Approximating functions; A forward pass; A logistic regressor; Python version of our logistic regressor;Machine Learning for Finance shows you how to build machine learning models for use in financial services organizations. It shows you how to work with all the key machine learning models, from simple regression to advanced neural networks. You will use machine learning to automate manual tasks, address systematic bias, and find new insights ...

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Machine Learning for Finance

Machine Learning for Finance

Copyright 2019 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Acquisition Editors: Frank Pohlmann, Ben Renow-Clarke

Acquisition Editor Peer Reviews: Suresh Jain

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Graphics: Sandip Tadge, Tom Scaria

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First published: May 2019

Production reference: 1290519

Published by Packt Publishing Ltd.

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ISBN 978-1-78913-636-4

www.packtpub.com

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Contributors
About the author

Jannes Klaas is a quantitative researcher with a background in economics and finance. Currently a graduate student at Oxford University, he previously led two machine learning bootcamps and worked with several financial companies on data-driven applications and trading strategies.

His active research interests include systemic risk as well as large-scale automated knowledge discovery.

About the reviewer

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. On the side, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.

Preface

Aided by the availability of vast amounts of data computing resources, machine learning ( ML ) has made big strides. The financial industry, which at its heart is an information processing enterprise, holds an enormous amount of opportunity for the deployment of these new technologies.

This book is a practical guide to modern ML applied in the financial industry. Using a code-first approach, it will teach you how the most useful ML algorithms work, and how to use them to solve real-world problems

Who this book is for

There are three kinds of people who would benefit the most from this book:

  • Data scientists who want to break into finance and would like to know about the spectrum of possible applications and relevant problems
  • Developers in any FinTech business or quantitative finance professionals who look to upgrade their skill set and want to incorporate advanced ML methods into their modeling process
  • Students who would like to prepare themselves for the labor market and learn some practical skills valued by employers

This book assumes you have some working knowledge in linear algebra, statistics, probability theory, and calculus. However, you do not have to be an expert in any of those topics.

To follow the code examples, you should be comfortable with Python and the most common data science libraries, such as pandas, NumPy, and Matplotlib. The book's example code is presented in Jupyter Notebooks.

Explicit knowledge of finance is not required.

What this book covers

, Neural Networks and Gradient-Based Optimization , will explore what kinds of ML there are, and the motivations for using them in different areas of the financial industry. We will then learn how neural networks work and build one from scratch.

, Applying Machine Learning to Structured Data , will deal with data that resides in a fixed field within, for example, a relational database. We will walk through the process of model creation: from forming a heuristic, to building a simple model on engineered features, to a fully learned solution. On the way, we will learn about how to evaluate our models with scikit-learn, how to train tree-based methods such as random forests, and how to use Keras to build a neural network for this task.

, UtilizingComputer Vision , describes how computer vision allows us to perceive and interpret the real world at scale. In this chapter, we will learn the mechanisms with which computers can learn to identify image content. We will learn about convolutional neural networks and the Keras building blocks we need to design and train state-of-the-art computer vision models.

, Understanding Time Series , looks at the large number of tools devoted to the analysis of temporally related data. In this chapter, we will first discuss the "greatest hits" that industry professionals have been using to model time series and how to use them efficiently with Python. We will then discover how modern ML algorithms can find patterns in time series and how they are complemented by classic methods.

, Parsing Textual Data with Natural Language Processing , uses the spaCy library and a large corpus of news to discuss how common tasks such as named entity recognition and sentiment analysis can be performed quickly and efficiently. We will then learn how we can use Keras to build our own custom language models. The chapter introduces the Keras functional API, which allows us to build much more complex models that can, for instance, translate between languages.

, Using Generative Models

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