Bookshelf and Earbuds: Bookshelf

I'm often asked for material to get started on some quantitative finance topics; so here are my recommended reads and listens, in no particular order.

Reading List

  • Quant - Applied Maths

  • Stochastic Calculus for Finance

    Pr Shreeve is often credited as having created the first modern quantitative finance curriculum at Carnegie Mellon University. He published two books, the Stochastic Calculus for Finance series, from those lectures and the second volume dedicated to continuous-time models is one of the most known and most used books at the graduate level in quantitative finance.

    Shreve, S.E., 2010. Continuous-Time models, Stochastic calculus for finance / Steven E. Shreve. Springer, New York, NY.
  • Analysis of Financial Time Series

    Analysis of Financial Time Series is a practical catalogue of most of the common concepts and techniques in quantitative finance. I recommend this book to anyone interested in the field, it's an investment that will yield over and over again. I have found myself referring back to this book on multiple occasions, Tsay's style is clear and the book is well-organised for easy referencing.

    Tsay, R.S., 2010. Analysis of financial time series, 3rd ed. ed, Wiley series in probability and statistics. Wiley, Cambridge, Mass.

  • Quant - Liquidity

  • Market Microstructure (Equities)

    Lehalle and Laruelle conduct a detailed analysis of the market microstructure properties of European equities. The book only has a few chapters and is better read from start to finish to capture the authors' perspective. This is one of my favourite books in the field of liquidity fragmentation, and the ideas from this book can be applied to any asset class.
    Both first and second editions are good, the second includes updates for regulations, covers more modern topics and a foreword by Almgren.

    Lehalle, C.-A., Laruelle, S., 2018. Market microstructure in practice, Second edition. ed. World Scientific, Singapore.
  • Market Liquidity

    In this book, Olivier Guéant introduces most of the mathematical tools needed in the day-to-day of algorithmic trading: Almgren-Chriss, alongside numerical methods; Avellaneda-Stoikov; and there is also one chapter about options. The proofs are clear, elegant and concise.

    Guéant, O. 2016. The Financial Mathematics of Market Liquidity: From Optimal Execution to Market Making, First Edition. ed. CRC press, Taylor & Francis Group, New York.

  • Quant - Other

  • The Black Swan

    This book, part of Taleb's Incerto series, does not need an introduction. 😃
    The other titles in the series are Fooled by Randomness, The Bed of Procrustes (closer to a philosophical essay) and Antifragile.

    Nassim, N.T., 2007. The black swan: the impact of the highly improbable. NY: Random House.
  • Flash Boys

    Michael Lewis is my favourite author on finance-related topics. His works are mostly nonfiction but they read like novels. Flash Boys is the true story of the birth of high-frequency stock trading in the US.

    Lewis, M., 2014. Flash boys: A Wall Street Revolt. WW Norton & Company.

  • Machine Learning

  • Deep Learning (a.k.a. "the" Goodfellow)

    The authors are among the most famous experts in the field. Deep Learning is a comprehensive catalogue of models, with mathematical concepts. There is a free version of the book available online.

    Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press.
  • Reinforcement Learning (Sutton, Barto)

    This book is the foundation of modern reinforcement learning and is still the reference in the field. Any of the two editions is an excellent introduction to that specific class of algorithms.

    Sutton, R.S., Barto, A., 2020. Reinforcement learning: an introduction, Second edition. ed, Adaptive computation and machine learning. The MIT Press, Cambridge, Massachusetts London, England.
  • A Probabilistic Perspective

    Murphy's book is a comprehensive study of machine learning models and is, in my humble opinion, a must-have for anyone who uses such models in high-stakes contexts. Its compelling probabilistic interpretation of models, alongside the broad range of algorithms, make it a perfect companion when choosing which implementation suits best a given problem. The book is very dense and I recommend a digital edition if possible.