\( \newcommand{\bm}[1]{\boldsymbol{#1}} \newcommand{\textm}[1]{\textsf{#1}} \def\T{{\mkern-2mu\raise-1mu\mathsf{T}}} \newcommand{\R}{\mathbb{R}} % real numbers \newcommand{\E}{{\rm I\kern-.2em E}} \newcommand{\w}{\bm{w}} % bold w \newcommand{\bmu}{\bm{\mu}} % bold mu \newcommand{\bSigma}{\bm{\Sigma}} % bold mu \newcommand{\bigO}{O} %\mathcal{O} \renewcommand{\d}[1]{\operatorname{d}\!{#1}} \)

1.5 Reading guidelines

This book has been written under the premise that each chapter can be read independently. For example, a reader who is already familiar with portfolio optimization can jump directly to Chapter 16 on deep learning portfolios or to Chapter 15 on pairs trading.

Some suggested ways for reading the book include the following approaches:

  • A “reader in a rush” can go directly to Chapter 6 for portfolio basics and Chapter 7 that covers modern portfolio theory, perhaps also taking a quick look at Chapter 2 on stylized facts of financial data, and then jump to any other chapter, e.g., Chapter 14 on robust portfolios or Chapter 9 on high-order portfolios.

  • A “reader with a bit more time”, apart from the basic Chapters 2, 6, and 7, could also read Chapter 3 on the i.i.d. data modeling and Chapter 8 on portfolio backtesting to get a better grasp of the fundamentals.

  • For a full coverage of all the different portfolio designs, a reader can go over any chapter in Part II, that is, apart from the fundamental Chapters 6-8, one can explore (in any particular order):

    • high-order portfolios (Chapter 9);
    • portfolios with alternative risk measures (Chapter 10);
    • risk parity portfolios (Chapter 11);
    • graph-based portfolios (Chapter 12);
    • index tracking portfolios (Chapter 13);
    • robust portfolios (Chapter 14);
    • pairs trading or statistical arbitrage portfolios (Chapter 15); and
    • deep learning portfolios (Chapter 16).

  • To complete the financial data modeling, one should go over all chapters in Part I: that is, apart from Chapters 2 and 3, Chapter 4 covers time series modeling, and Chapter 5 explores the more recent topic of graph modeling of financial assets.

  • In order to grasp a more solid understanding of the portfolio optimization formulations and algorithms, a reader may want to go over Appendices A and B, i.e., the basics of convex optimization theory in Appendix A and optimization algorithms in Appendix B.