Macroeconomic Forecasting Using Alternative Data - 1st Edition - ISBN: 9780128191217

Macroeconomic Forecasting Using Alternative Data

1st Edition

Techniques for Applying Big Data and Machine Learning

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Authors: Apurv Jain
Paperback ISBN: 9780128191217
Imprint: Academic Press
Published Date: 1st August 2020
Page Count: 250
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Description

Macroeconomic Forecasting Using Alternative Data: Techniques for Applying Big Data and Machine Learning applies computer science to the demands of macroeconomic forecasting. It is the first book to combine machine learning methods with macroeconomics. By using artificial intelligence and machine learning techniques, it unlocks the increased forecasting accuracy offered by alternative data sources. Through its interdisciplinary approach, readers learn how to use big datasets efficiently and effectively.

Key Features

  • Combines big data/machine learning with macroeconomic forecasting
  • Explains how alternative data improves forecasting accuracy when controlled for traditional data sources
  • Provides new innovative methods for handling large databases and improving forecasting accuracy

Readership

Upper-division undergraduates, graduate students, and professionals working in economic forecasting, in macroeconomics, and in data applications in economics

Table of Contents

  1. The Importance of Macro Prediction
    2. Macro Data are Noisy
    3. Our Goal: Macro Data with Less Noise and Lag
    4. Alternate Data
    5. A Framework for Alternate Data
    6. Predicting Data Releases with Search
    7. Modeling Case Study: Non-Farm Payrolls
    8. Accounting Data
    9. Prediction in Practice
    10. Public Good: Visualizing World Economic Growth in Real Time
    11. Interviews with Policy Makers and Asset Managers

Details

No. of pages:
250
Language:
English
Copyright:
© Academic Press 2020
Published:
1st August 2020
Imprint:
Academic Press
Paperback ISBN:
9780128191217

About the Author

Apurv Jain

Apurv Jain is the Senior Finance Lead and Co-Founder of the Economic Measurement Group at Microsoft. His team of scientists from Microsoft Research, ML experts from BingPredicts, and traders from Capital Markets Group use web-scale data (search, twitter etc.) to understand and predict the economy and the financial markets. Apurv sets the external product and research agenda, and he is the portfolio manager for an internal $150 mm portfolio devoted to testing our ideas. His alternate data and AI based strategies have a positive 3 year track record. He is also a visiting researcher at Harvard Business School.

Affiliations and Expertise

Microsoft Research and Harvard Business School

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