Quantitative Portfolio Management

with Applications in Python

  • Pierre Brugière

Part of the Springer Texts in Business and Economics book series (STBE)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Pierre Brugière
    Pages 1-18
  3. Pierre Brugière
    Pages 19-25
  4. Pierre Brugière
    Pages 27-50
  5. Pierre Brugière
    Pages 51-59
  6. Pierre Brugière
    Pages 61-94
  7. Pierre Brugière
    Pages 95-102
  8. Pierre Brugière
    Pages 103-123
  9. Pierre Brugière
    Pages 125-139
  10. Pierre Brugière
    Pages 141-154
  11. Pierre Brugière
    Pages 155-189
  12. Back Matter
    Pages 191-205

About this book


This self-contained book presents the main techniques of quantitative portfolio management and associated statistical methods in a very didactic and structured way, in a minimum number of pages. The concepts of investment portfolios, self-financing portfolios and absence of arbitrage opportunities are extensively used and enable the translation of all the mathematical concepts in an easily interpretable way.

All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. The statistical methods concern both parametric and non-parametric estimators and, to estimate the factors of a model, principal component analysis is explained. The presented Python code and web scraping techniques also make it possible to test the presented concepts on market data.

This book will be useful for teaching Masters students and for professionals in asset management, and will be of interest to academics who want to explore a field in which they are not specialists. The ideal pre-requisites consist of undergraduate probability and statistics and a familiarity with linear algebra and matrix manipulation. Those who want to run the code will have to install Python on their pc, or alternatively can use Google Colab on the cloud.  Professionals will need to have a quantitative background, being either portfolio managers or risk managers, or potentially quants wanting to double check their understanding of the subject.


Markowitz theory factor models APT models principal component analysis Python code 91G10, 91D70 risk measures

Authors and affiliations

  1. 1.CEREMADEUniversity Paris Dauphine-PSLParisFrance

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-37739-7
  • Online ISBN 978-3-030-37740-3
  • Series Print ISSN 2192-4333
  • Series Online ISSN 2192-4341
  • Buy this book on publisher's site