The book Investment Science by David G. Luenberger is a widely used textbook in quantitative finance and financial engineering programs. First published in 1998, it saw the release of its second edition in 2013. In the 15 years between the two editions, the field of investment science experienced significant developments. As a result, the second edition features important updates and new material. In particular, the second edition expands coverage of several key topics such as behavioral finance, options pricing, risk management and machine learning techniques in investment management. It also includes new chapters on equity valuation, fixed income securities as well as metaheuristic optimization methods. With a balanced mix of theory and application, the second edition of Investment Science continues to provide an accessible yet rigorous introduction to the field.
Expanded coverage of behavioral finance concepts
The second edition devotes more space to behavioral finance, which has grown tremendously as a field since the 1990s. Key topics like prospect theory, heuristics and framing are introduced and integrated throughout several chapters. For example, prospect theory is explained in the context of portfolio selection problems. Similarly, framing effects are illustrated through examples in portfolio insurance and investment policy statement drafting. This reflects the book’s aim of providing a contemporary overview of investment science.
New options pricing material
Relative to the first edition, the second edition contains significantly expanded coverage of options pricing. There are new sections on the binomial model and its applications in Chapter 3. Chapter 9 is entirely new and dedicated to options pricing theory. It covers essential concepts like put-call parity, early exercise of American options, exotic options and the volatility smile. The treatment bridges theory and practice by also discussing implementation aspects like Monte Carlo valuation. This updated options pricing material equips readers with important techniques for quantitative finance roles.
Inclusion of machine learning techniques
Modern investment management is increasingly adopting machine learning techniques like classification algorithms and neural networks. The second edition acknowledges this by introducing machine learning in relevant contexts. For example, Chapters 19 and 20 discuss the use of classification methods and neural networks for equity valuation. Readers learn about applying tools like logistic regression, discriminant analysis, and backpropagation networks for predicting stock returns. The book also touches on more advanced concepts like ensemble methods and deep learning. Overall, the machine learning coverage provides useful exposure to readers interested in quantitative investment roles.
New chapters on equity valuation and fixed income securities
The first edition of Investment Science did not cover equity valuation or fixed income securities in detail. The second edition addresses this gap through two new chapters – Chapter 19 on equity valuation and Chapter 21 on fixed income securities. Chapter 19 examines multi-factor models like the Fama-French model, dividend discount models and relative valuation methods. It also discusses the use of classification algorithms for predicting returns. Chapter 21 covers essential fixed income concepts like spot rates, forward rates, duration and convexity. As these two asset classes are central to investment practice, the additional chapters make the book more comprehensive.
Stronger emphasis on mathematical rigor
The second edition adopts a more rigorous mathematical approach overall. Many proofs and technical derivations have been added or expanded, especially in the options pricing chapter. There are also more illustrative diagrams and graphs to aid conceptual understanding. Chapter exercises see a boost in difficulty too. This increased rigor strengthens the book’s usefulness for readers looking to develop stronger mathematical finance skills.
The second edition of Investment Science by David Luenberger delivers important updates to reflect the growth of investment science over 15 years. From behavioral finance to machine learning, readers are introduced to contemporary concepts and techniques for quantitative investment analysis and management.