1st Edition

Navigating the Factor Zoo The Science of Quantitative Investing

By Michael Zhang, Tao Lu, Chuan Shi Copyright 2025
    296 Pages 21 B/W Illustrations
    by Routledge

    296 Pages 21 B/W Illustrations
    by Routledge

    Bridging the gap between theoretical asset pricing and industry practices in factors and factor investing, Zhang et al. provides a comprehensive treatment of factors, along with industry insights on practical factor development.

    Chapters cover a wide array of topics, including the foundations of quantamentals, the intricacies of market beta, the significance of statistical moments, the principles of technical analysis, and the impact of market microstructure and liquidity on trading. Furthermore, it delves into the complexities of tail risk and behavioral finance, revealing how psychological factors affect market dynamics. The discussion extends to the sophisticated use of option trading data for predictive insights and the critical differentiation between outcome uncertainty and distribution uncertainty in financial decision-making. A standout feature of the book is its examination of machine learning's role in factor investing, detailing how it transforms data preprocessing, factor discovery, and model construction. Overall, this book provides a holistic view of contemporary financial markets, highlighting the challenges and opportunities in harnessing alternative data and machine learning to develop robust investment strategies.

    This book would appeal to investment management professionals and trainees. It will also be of use to graduate and upper undergraduate students in quantitative finance, factor investing, asset management and/or trading.

    Table of Contents

    Preface

     

    Chapter 1. Factor Investing

     

    Chapter 2. Quantamentals

     

    Chapter 3. Statistical Moments as Factors

     

    Chapter 4. Market Beta

     

    Chapter 5. Technical Analysis Factors

     

    Chapter 6. Microstructure and Liquidity

     

    Chapter 7. Tail Risk

     

    Chapter 8. Behavioral Finance

     

    Chapter 9. Option Information

     

    Chapter 10. Uncertainty

     

    Chapter 11. Alternative Data

     

    Chapter 12. Machine Learning in Factor Investing

     

    Epilogue

    Biography

    Michael Zhang is the founder of Super Quantum Fund. He has over 20 years of experience in quantitative investing. He publishes in the most prestigious academic journals and has been highly cited. He holds a PhD from MIT, an MSc, and two bachelor’s degrees from Tsinghua University.

    Tao Lu is the CEO of Super Quantum Fund. He has extensive practical experience in portfolio management through quantitative methods and leading quantitative research teams. He holds a PhD from the Chinese University of Hong Kong and two bachelor’s degrees from Tsinghua University.

    Chuan Shi is the chief data scientist at Beijing Liangxin Investment Management, specializing in factor investing, portfolio allocation, and risk management. He holds a PhD from MIT and bachelor's and master's degrees from Tsinghua University. He is the lead author of "Factor Investing: Methodology and Practice."

    This book delivers a rigorous and stimulating discussion of fundamental questions. What does a human know that a machine learning algorithm cannot possibly know and vice versa?  When does a serious investor know enough to act and when does the irreducible uncertainty lead to caution? This book offers a deeply insightful window into the modern frontier, and how to think about balancing sophisticated algorithms with practical challenges at the edges.

    Shane Greenstein, PhD
    Professor
    Harvard Business School

    Factor investing is the cornerstone of active portfolio management. For both students and investment professionals, this book provides a comprehensive analysis of the foundations of factor investing as well as practical implementation. Importantly, the authors detail not just the investment opportunities but also the risks. Recommended.

    Campbell Harvey, PhD
    Professor
    Duke University

    Information technology has transformed most areas of finance, including driving the growth of quantitative investing. This book provides a survey of the different kinds of data and factors behind this trend. The book’s overview of the science, art, technology, and techniques helps the reader comprehensively understand the current knowledge frontier while pointing out future directions for research and practice. 

    Terrence Hendershott, PhD
    Professor
    University of California, Berkeley

    It is instantly clear that the authors combine deep academic knowledge with a wealth of practical market experience. What really sets this book apart, however, is that they are not just telling you what used to work in the past, but try to look to the future – what new methods and ways of thinking will deepen our understanding of financial markets.

    Yuriy Nevmyvaka, PhD
    Managing Director, Head of Machine Learning Research, 
    Morgan Stanley

    Ivory Tower meets Wall Street – Tapping into their wealth of knowledge and experience as both academics and hedge fund managers, the authors cover a multitude of factors that affect returns in the financial market.  Packed with academic rigor and practical relevance, this is an important and compelling read. 

    Michael Saunders, PhD
    Professor
    Stanford University

    A well-curated exploration of factor investing, systematically charting a map of important sources of financial returns. It is a solid treatment of how certainty can be generated from the uncertain financial world and an essential read for practitioners and students seeking a deeper understanding of quantitative investing.

    Feng Zhu, PhD
    Professor
    Harvard Business School