In this Great Recession, quants have become notorious again, since Black Monday, October 19th, 1987. At that time, it was called “program trading”. Fast forward to May 6th, 2010, the “flash crash” happened, causing the Dow to drop more than 1000 points in couple of minutes. Now, it is called high-frequency trading, which accounts for 40% to 70% of all trading on every stock market in the U.S. Regardless of program trading or high-frequency trading, it is based on quantitative techniques, which makes the book “Quantitative Equity Investing — Techniques and Strategies” interesting, particularly so for those who want to understand what these “crazy” quants from Wall Street are doing and to outsmart the markets or market makers.
Modern quantitative techniques are based on modern portfolio theory, introduced by Harry Markowitz in 1952, in which he suggested that,
investors should decide the allocation of their investment funds on the basis of the trade-off between portfolio risk, as measured by the standard deviation of investment returns, and portfolio return, as measured by the expected value of the investment return. …… Developing the necessary inputs for constructing portfolios based on modern portfolio theory has been facilitated by the development of Bayesian statistics, shrinkage techniques, factor models, and robust portfolio optimization (with the help of powerful computers).
All these techniques have been skillfully depicted by the export authors, who have all worked closely with hedge fund and quantitative asset management firms, who are famous university professors with series of books focusing on related financial topics.
The book starts with the role and use of mathematical techniques in finance. The authors’ argument is very powerful:
As there are unpredictable events with a potentially major impact on the economy, it is claimed that financial economics cannot be formalized as a mathematical methodology with predictive power. In a nutshell, the answer is that black swans exist not only in financial markets but also in the physical sciences. But no one questions the use of mathematics in the physical sciences because there are major events that we cannot predict.
The book continues with financial model building, which covers modern regression theory, applications of Random Matrix Theory, dynamic time series model, vector autoregressive models, and cointegration analysis. Then, it moves on to include financial engineering, static and dynamic factor models, asset allocation, portfolio models, transaction costs, trading strategies, etc.
Overall, the book is math-heavy except the first chapter. It is an excellent textbook for students majoring in finance. It is also a good guide book for traders who focus on quantitative trading techniques in a daily basis. It is also a recommendation for power investors who start to leverage brokerages’ open trading APIs. It is not recommended for those who don’t have a math background and who don’t understand any mathematical terms mentioned in the earlier paragraphs of this review.