Al Naqvi - Artificial Intelligence for Asset Management and Investment: A Strategic Perspective

1. Chapter 3
2. Chapter 5
3. Chapter 14

1. Chapter 1
2. Chapter 3
3. Chapter 5
4. Chapter 6
5. Chapter 7
6. Chapter 9
7. Chapter 18
8. Chapter 19

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AL NAQVI

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Library of Congress Cataloging-in-Publication Data:

Names: Naqvi, Al, author. | John Wiley & Sons, Inc., publisher.

Title: Artificial intelligence for asset management and investment : a strategic perspective / Al Naqvi.

Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2021] | Series: Wiley finance series | Includes index.

Identifiers: LCCN 2020029614 (print) | LCCN 2020029615 (ebook) | ISBN 9781119601821 (hardback) | ISBN 9781119601876 (adobe pdf) | ISBN 9781119601845 (epub)

Subjects: LCSH: Asset allocation. | Artificial intelligence. | Financial services industryTechnological innovations.

Classification: LCC HG4529.5 .N366 2021 (print) | LCC HG4529.5 (ebook) | DDC 332.60285/63dc23

LC record available at https://lccn.loc.gov/2020029614

LC ebook record available at https://lccn.loc.gov/2020029615

Cover Design: Wiley

Cover Image: katjen/Shutterstock, whiteMocca/Shutterstock

For Shakila

ARE YOU SEEKING A BOOK on artificial intelligence (AI) in finance? Good news and not so good news. Good news is that you are likely to find many books; bad news is that most of those are written by quants and for quants. Riddled with complex math equations, proofs, and theorems, these books speak a language that many people do not understand.

It is as if authors want to demonstrate how much they know about machine learning but not tell you what you need to know. The tone is often ridiculing, even insulting, as if each sentence is coded language to discourage nonmembers from entering the exclusive club of AI. In some cases, the tone is demeaning toward even other quants, with the connotation of you don't know, we know position. The subtle undertone is clear: if you do not understand complex math and data science, you do not deserve to enter the amazing world of AI. This esoteric, closed, and limited membership in AI is problematic at many levels.

If you have not spent decades in the investment world and you talk to some hardcore finance professionals, they will remind you that if you are an experienced data scientist, then you don't belong in the industry. You will be labeled as too naive or too young or too inexperienced. If you are an expert in deep learning and reinforcement learning, they will tell you that you have no use in the finance world. They will argue that deep learning and reinforcement learning are not being extensively used in finance (what they are really saying is that they are not using these models, and they have not seen those being widely used in practice). This criticism of machine learning professionals can be viewed as a mix of some reality and a bit of fear of the unknown.

Do not get me wrong. Certain authors are well-meaning and direct. They point out the gaps and show how to close them. They recognize that one must be blunt and direct to show the weaknesses. For instance, De Prado's approach is a passionate wake-up call for many quant organizations, and I am confident his work saved billions of dollars and avoided many unnecessary catastrophes (De Prado, Advances in Financial Machine Learning, Wiley 2018). I am referring to those who point out problems but never provide solutions.

It is true that finance machine learning is different. The signal-to-noise ratio is low. You are dealing with a dynamic and constantly changing system. Your every action is under scrutiny. You are dealing with significant amounts of unstructured data. You could be identifying relationships and then trying to discover the theory of attempting to explain what is transpiring. Many interesting finds are prone to overfitting. You are operating in an environment that is not only constantly changingyour interaction with it is exposing your strategy, and hence your strategy is subject to constant reinvention.

Now come to the non-quant consulting club. There are several people who are trivializing AI. This is the hype club that opens every AI conversation with a vague, astrology-styled notion of future of work, and the next words in those conversations are almost always deep learning, AlphaGo, and IBM AI winning the