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Isaiah Hull - Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Research and Industry

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Isaiah Hull Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Research and Industry
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Machine learning has taken time to move into the space of academic economics. This is because empirical research in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for students, academics, and professionals who lack a standard reference on machine learning for economics and finance.
This book focuses on economic and financial problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, LSTMs, and DQNs), generative machine learning models (GANs and VAEs), and tree-based models. It also covers the intersection of empirical methods in economics and machine learning, including regression analysis, natural language processing, and dimensionality reduction.
TensorFlow offers a toolset that can be used to define and solve any graph-based model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. This simplifies otherwise complicated concepts, enabling the reader to solve workhorse theoretical models in economics and finance using TensorFlow.
What Youll Learn
  • Define, train, and evaluate machine learning models in TensorFlow 2
  • Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems
  • Solve theoretical models in economics

Who This Book Is For
Students, data scientists working in economics and finance, public and private sector economists, and academic social scientists

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Isaiah Hull - Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Research and Industry
Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Research and Industry
Isaiah Hull
Machine Learning for Economics and Finance in TensorFlow 2: Deep Learning Models for Research and Industry

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Book cover of Machine Learning for Economics and Finance in TensorFlow 2 - photo 1
Book cover of Machine Learning for Economics and Finance in TensorFlow 2
Isaiah Hull
Machine Learning for Economics and Finance in TensorFlow 2
Deep Learning Models for Research and Industry
1st ed.
Logo of the publisher Isaiah Hull Nacka Sweden Any source code or other - photo 2
Logo of the publisher
Isaiah Hull
Nacka, Sweden

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-6372-3 . For more detailed information, please visit http://www.apress.com/source-code .

ISBN 978-1-4842-6372-3 e-ISBN 978-1-4842-6373-0
https://doi.org/10.1007/978-1-4842-6373-0
Apress standard
Isaiah Hull 2021
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Distributed to the book trade worldwide by Springer Science+Business Media New York, 233 Spring Street, 6th Floor, New York, NY 10013. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.

For my wife, Jamie; my son, Moses; and my parents, James and Gale

Table of Contents
About the Author
Isaiah Hull
is a senior economist at the Research Division of Swedens Central Bank He - photo 3

is a senior economist at the Research Division of Swedens Central Bank. He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, macro-finance, and fintech. He also teaches courses on the DataCamp platform, including Introduction to TensorFlow in Python, and is working on an interdisciplinary research project to introduce quantum computing and quantum money to the economics discipline.

About the Technical Reviewer
Vishwesh Ravi Shrimali

graduated from BITS Pilani in 2018, where he studied mechanical engineering. Since then, he has worked with Big Vision LLC on deep learning and computer vision and was involved in creating official OpenCV AI courses. Currently, he is working at Mercedes Benz Research and Development India Pvt. Ltd. He has a keen interest in programming and AI and has applied that interest in mechanical engineering projects. He has also written multiple blogs about OpenCV and deep learning on Learn OpenCV, a leading blog on computer vision. He has also coauthored Machine Learning for OpenCV 4 (second edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.

Isaiah Hull 2021
I. Hull Machine Learning for Economics and Finance in TensorFlow 2 https://doi.org/10.1007/978-1-4842-6373-0_1
1. TensorFlow 2
Isaiah Hull
(1)
Nacka, Sweden

TensorFlow is an open source library for machine learning produced by the Google Brain Team. It was originally released to the public in 2015 and quickly became one of the most popular libraries for deep learning. In 2019, Google released TensorFlow 2, which was a substantial departure from TensorFlow 1. In this chapter, we will introduce TensorFlow 2, explain how it can be used in economics and finance, and then review preliminary material that will be necessary for understanding the material in later chapters. If you did not use TensorFlow 1, you may want to skip the Changes in TensorFlow 2 section.

Installing TensorFlow
In order to use TensorFlow 2 , you will need to install Python. Since Python 2 is no longer supported as of January 1, 2020, I recommend installing Python 3 via Anaconda, which bundles Python with 7,500+ commonly used modules for data science: www.anaconda.com/distribution/ . Once you have installed Anaconda, you can configure a virtual environment from the command line in your operating system. The following code will install an Anaconda virtual environment with Python 3.7.4 named tfecon , which is what we will use in this book:
conda create -n tfecon python==3.7.4
You can activate the environment using the following command:
conda activate tfecon
Within the environment, you can install TensorFlow using the following command:
(tfecon) pip install tensorflow==2.3.0
When you want to deactivate your virtual environment, you can do so using the following command:
conda deactivate

We will use TensorFlow 2.3 and Python 3.7.4 throughout the book. To ensure compatibility with the examples, you should configure your virtual environment accordingly .

Changes in TensorFlow 2

TensorFlow 1 was structured around static graphs. In order to perform a computation, you needed to first define a set of tensors and a sequence of operations. This formed the computational graph, which was fixed at runtime. Static graphs provided an ideal environment for constructing optimized production code, but also discouraged experimentation and increased the difficulty of debugging.

In Listing , we provide an example of the construction and execution of a static computational graph in TensorFlow 1. We will consider the familiar case where we want to use a set of regressors (features), X , to predict a dependent variable, Y , using an ordinary least squares (OLS) regression . The solution to this problem is the vector of coefficients, , which minimizes the sum of the squared regression residuals. Its analytical expression is given in Equation 1-1.

Equation 1-1. The solution to the least squares problem.

import tensorflow as tf printtfversion 1152 Define the data as - photo 4
import tensorflow as tf
print(tf.__version__)
'1.15.2'
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