“Alternative data is one of the hottest topics in the investment management industry today. Whether it is used to forecast global economic growth in real time, to parse the entrails of a company with more granularity than that offered by a quarterly report, or to better understand stock market behaviour, alternative data is something that everyone in asset management needs to get to grips with. Alexander Denev and Saeed Amen are able guides to a convoluted subject with many pitfalls, both technical and theoretical, even for those who still think Python is a snake best avoided.”
—Robin Wigglesworth, Global finance correspondent, Financial Times.
“Congratulations to the authors for producing such a timely, comprehensive, and accessible discussion of alternative data. As we move further into the twenty-first century, this book will rapidly become the go-to work on the subject.”
—Professor David Hand, Imperial College London
“Over the last decade, alternative data has become central to the quest for temporary monopoly of information. Yet, despite its frequent use, little has been written about the end-to-end pipeline necessary to extract value. This book fills the omission, providing not just practical overviews of machine learning methods and data sources, but placing as much importance on data ingestion, preparation, and pre-processing as on the models that map to outcomes. The authors do not consider methodology alone, but also provide insightful case studies and practical examples, and highlight the importance of cost-benefit analysis throughout. For value extraction from alternative data, they provide informed insights and deep conceptual understanding – crucial if we are to successfully embed such technology at the heart of trading.”
—Stephen Roberts, Royal Academy of Engineering/Man Group Professor of Machine Learning, University of Oxford, UK, and Director of the Oxford-Man Institute of Quantitative Finance
“True investment outperformance comes from the triad of data plus machine learning plus supercomputing. Alexander Denev and Saeed Amen have written the first comprehensive exposition of alternative data, revealing sources of alpha that are not tapped by structured datasets. Asset managers unfamiliar with the contents of this book are not earning the fees they charge to investors.”
—Dr. Marcos López de Prado, Professor of Practice at Cornell University, and CIO at True Positive Technologies LP
“Alexander and Saeed have written an important book about an important topic. I am involved with alternative data every day, but I still enjoyed the perspectives in the book, and learned a lot. I highly recommend it to everybody looking to harness the power of alt data (and avoid the pitfalls!).”
—Jens Nordvig, Founder and CEO of Exante Data
© 2020 by Alexander Denev and Saeed Amen. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data:
Names: Denev, Alexander, author. | Amen, Saeed, 1982- author.
Title: The book of alternative data : a guide for investors, traders and risk managers / Alexander Denev, Saeed Amen.
Description: Hoboken, New Jersey : Wiley, [2020] | Includes bibliographical references and index.
Identifiers: LCCN 2020008783 (print) | LCCN 2020008784 (ebook) | ISBN 9781119601791 (hardback) | ISBN 9781119601814 (adobe pdf) | ISBN 9781119601807 (epub)
Subjects: LCSH: Investments | Financial risk management. | Big data.
Classification: LCC HG4529 .D47 2020 (print) | LCC HG4529 (ebook) | DDC 332.63/204—dc23
LC record available at https://lccn.loc.gov/2020008783
LC ebook record available at https://lccn.loc.gov/2020008784
Cover Design: Wiley
Cover Image: © akindo/Getty Images
To Natalie, with all my love. –Alexander
For Gido and Baba, in life, in time, in spirit, your path is forever my guide. –Saeed
Data permeates through our world, in ever increasing amounts. This fact alone is not sufficient for data to be useful. Indeed, data has no utility, if it is devoid of information, which could aide our understanding. Data needs to be insightful for it to be of use and it also needs to be processed in the appropriate way. In the pre-Big Data age days, statistics such as averages, standard deviation, correlations were calculated on structured datasets to illuminate our understanding of the world. Models were calibrated on (a small number of) input variables which were often well “understood” to obtain an output via well-trodden methods like, say, linear regression.
However, interpreting Big Data, and hence alternative data, comes with many challenges. Big Data is characterized by properties such as volume, velocity and variety and other Vs, which we will discuss in this book. It is impossible to calculate statistics, unless datasets are well structured and relevant features are extracted. When it comes to prediction, the input variables derived from Big Data are numerous and traditional statistical methods can be prone to overfitting. Moreover, nowadays calculating statistics or building models on this data must be done sometimes frequently and in a dynamic way to account for the always changing nature of the data in our high frequency world.
Thanks to technological and methodological advances, understanding Big Data and by extension alternative data, has become a tractable problem. Extracting features from messy enormous volumes of data is now possible thanks to the recent developments in artificial intelligence and machine learning. Cloud infrastructure allows elastic and powerful computation to manage such data flows and to train models both quickly and efficiently. Most of the programming languages in use today are open source and many such as Python have a large number of libraries in the sphere of machine learning and data science more broadly, making it easier to develop tech stacks to number crunch large datasets.
When we decided to write this book, we felt that there was a gap in the book market in this area. This gap seemed at odds with the ever growing importance of data, and in particular, alternative data. We live in a world, which is rich with data, where many datasets are accessible and available at a relatively low cost. Hence, we thought that it was worth writing a lengthy book to address how to address the challenges of how to use data profitably. We do admit though that the world of alternative data and its use cases is and will be subject to change in the near future. As a result, the path we paved with this book is also subject to change. Not least the label “alternative data” might become obsolete as it could soon turn mainstream. Alternative data may simply become “data”. What might seem to be great technological and methodological feats today to make alternative data usable, may soon become trivial exercises. New datasets from sources we could not even imagine could begin to appear, and quantum computing could revolutionise the way we look at data.
We decided to target this book at the investment community. Applications, of course, can be found elsewhere, and indeed everywhere. By staying within the financial domain, we could also have discussed areas such as credit decisions or insurance pricing, for example. We will not discuss these particular applications in this book, as we decided to focus on questions that an investor might face. Of course, we might consider adding these applications in future editions of the book.
At the time of writing, we are living in a world afflicted by COVID-19. It is a world, in which it is very important for decision makers to make the right judgement, and furthermore, these decisions must be done in a timely manner. Delays or poor decision making can have fatal consequences in the current environment. Having access to data streams that track the foot traffic of people can be crucial to curb the spread of the disease. Using satellite or aerial images could be helpful to identify mass gatherings and to disperse them for reasons of public safety. From an asset manager's point of view, creating nowcasts before official macroeconomic figures and company financial statements are released, results better investment decisions. It is no longer sufficient to wait several months to find out about the state of the economy. Investors want to have be able to estimate such points on a very high frequency basis. The recent advances in technology and artificial intelligence makes all this possible.
So, let us commence on our journey through alternative data. We hope you will enjoy this book!
We would like to thank our friends and colleagues who have helped us by providing suggestions and correcting our errors.
In first place, we would like to express our gratitude to Dr. Marcos Lopez de Prado who gave us the idea of writing this book. We would like to thank Kate Lavrinenko without whom the chapter on outliers would not have been possible; Dave Peterson, who proofread the entire book and provided useful and thorough feedback; Henry Sorsky for his work with us on the automotive fundamental data and missing data chapters, as well as proofreading many of the chapters and pointing out mistakes; Doug Dannemiller for his work around the risks of alternative data which we leveraged; Mike Taylor for his contribution to the data vendors section; Jorge Prado for his ideas around the auctions of data.
We would also like to extend our thanks to Paul Bilokon and Matthew Dixon for their support during the writing process. We are very grateful to Wiley, and Bill Falloon in particular, for the enthusiasm with which they have accepted our proposal, and for the rigor and constructive nature of the reviewing process by Amy Handy. Last but not least, we are thankful to our families. Without their continuous support this work would have been impossible.
Chapter 1: Alternative Data: The Lay of the Land
Chapter 2: The value of Alternative Data
Chapter 3: Alternative Data Risks and Challenges
Chapter 4: Machine Learning Techniques
Chapter 5: The Processes behind the Use of Alternative Data
Chapter 6: Factor Investing