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Machine Learning for Economics and Finance in TensorFlow 2

Deep Learning Models for Research and Industry

  • Book
  • © 2021

Overview

Authors:
  1. Isaiah Hull

    1. Nacka, Sweden

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  • Gain a full pipeline of tools needed to structure and develop an ML economics project

  • Apply a variety of deep learning models to economic problems with an empirical dimension

  • Define and solve any mathematical model with TensorFlow

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. TensorFlow 2

    • Isaiah Hull
    Pages 1-59

  3. Machine Learning and Economics

    • Isaiah Hull
    Pages 61-86

  4. Regression

    • Isaiah Hull
    Pages 87-125

  5. Trees

    • Isaiah Hull
    Pages 127-147

  6. Image Classification

    • Isaiah Hull
    Pages 149-187

  7. Text Data

    • Isaiah Hull
    Pages 189-248

  8. Time Series

    • Isaiah Hull
    Pages 249-279

  9. Dimensionality Reduction

    • Isaiah Hull
    Pages 281-306

  10. Generative Models

    • Isaiah Hull
    Pages 307-330

  11. Theoretical Models

    • Isaiah Hull
    Pages 331-356

  12. Back Matter

    Pages 357-368
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Keywords

About this book

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 You'll 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

Authors and Affiliations

  • Nacka, Sweden

    Isaiah Hull

About the author

Isaiah Hull received his PhD in Economics from Boston College in 2013 and has since worked in the Research Division at Sweden’s Central Bank. He has published numerous articles in academic journals primarily concentrated in computational economics with applications in macroeconomics, finance, and housing. Most of his recent work makes use of techniques from machine learning. He also regularly presents at conferences on machine learning and big data in economics. And Isaiah is an accomplished teacher with experience teaching TensorFlow 2.0. Currently, he’s working on a project to introduce quantum computing to economists.

Bibliographic Information

  • Book Title: Machine Learning for Economics and Finance in TensorFlow 2

  • Book Subtitle: Deep Learning Models for Research and Industry

  • Authors: Isaiah Hull

  • DOI: https://doi.org/10.1007/978-1-4842-6373-0

  • Publisher: Apress Berkeley, CA

  • eBook Packages: Professional and Applied Computing, Professional and Applied Computing (R0), Apress Access Books

  • Copyright Information: Isaiah Hull 2021

  • Softcover ISBN: 978-1-4842-6372-3Published: 26 November 2020

  • eBook ISBN: 978-1-4842-6373-0Published: 25 November 2020

  • Edition Number: 1

  • Number of Pages: XIII, 368

  • Number of Illustrations: 5 b/w illustrations, 61 illustrations in colour

  • Topics: Artificial Intelligence

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