Over the holidays, I read Quantitative Strategies for Achieving Alpha by Richard Tortoriello. I found this book to be very different than most of the books I’ve read on investing. Tortoriello spends a substantial portion of the book walking the reader though his process for evaluating various stock fundamentals. He then tests each of these fundamentals using twenty years of high quality backtesting data from the Standard & Poor’s Compustat Point in Time database. Unlike other books that discuss investment theory and qualitative analysis, this book is very empirical. The stock fundamentals Tortoriello analyzed include many value investor favorites. Based on that alone, I think many intermediate to advanced value investors could benefit from this book. Beginners might find this book a bit overwhelming.
While Quantitative Strategies for Achieving Alpha would obviously appeal to so called “quants”, I also think it is a book that value investors should not overlook. Tortoriello introduces the book by stating, “This book was written with qualitative investors in mind, particularly those who wish to “understand” the stock market from a quantitative (empirical) point of view and who desire to integrate quantitative screens, tests, or models into their investment process—or simply into their thinking.” While quantitative approaches often tend to focus on technical analysis, this book will appeal more to value investors given that it examines the theory of why each fundamental factor works and it relegates technical analysis to just one chapter on price momentum.
From page 39 of The Science of Hitting.
As I read through each chapter and flipped through the extensive results tables in the appendix, I started imagining Ted Williams’ strike zone graphic in The Science of Hitting. Could I use Tortoriello’s process and analysis to start building my own strike zone chart of where to find the fattest pitches in the stock market? By the time I made it through the second chapter, I was pretty excited with the idea of using Tortoriello’s method myself. With access to Portfolio123 data and this method, I might just be able to build myself a map of when and where to swing for the fat pitches.
Before I get too far ahead of myself, let me discuss why I like this book so much. Unlike Joel Greenblatt’s The Little Book That Beats the Market, Tortoriello’s Quantitative Stategies for Achieving Alpha provides all the details necessary for replicating his work. The method is clearly and fully described in a 26-page methodology chapter. There are no secret or ambiguous steps to his stock screens. I was never able to replicate Greenblatt’s Magic Formula (and I haven’t seen anyone else replicate his backtest results), but I feel confident that I can replicate Tortoriello’s work. In fact, I’ve already started and will start sharing those results soon.
The method Tortoriello lays out begins with investment basics, building bocks, and finally mosaics. An investment basic in this context is a grouping of investment strategies that generally work. The basics listed by Tortoriello are as follows:
- Profitability
- Valuation
- Cash Flow
- Growth
- Capital Allocation
- Price Momentum
- Red Flags
A chapter is dedicated to each of these investment basics in Quantitative Strategies. Each of these basics are then composed of building blocks. Tortoriello states, “A building block is a specific strategy that has investment value and works for a clearly understandable, nonstatistical reason.” Finally, these building blocks can then be pieced together to form a mosaic. The mosaics take the form of two factor screens, multi-factor models, and integrated investment strategies that combine quantitative results with qualitative vetting and risk management.
Each of the building blocks tested in this book are sorted by the value of the factor being tested and then divided into five equal sized portfolios. Each of these quintiles are backtested with 12-month holding periods from 1987 to 2006. The portfolios are rebalanced every 12-months and compounded annually to more realistically replicate what an individual investor might be expected to do to avoid higher short-term capital gains tax and trading costs. To help ensure that the test is not impacted by seasonal or statistical effects, the backtest is also started at four different points during the calendar year. The results of the quarterly tests are used to calculate the average excess returns for each quintile. The test results for the Enterprise Value to EBITDA ratio are provide below as an example of the many result summaries presented in this book.
When two factor tests are constructed, both factors are not weighted equally by Tortoriello. Instead, he first forms a set of stocks based on the first factor and then from that set he selects stocks based on the second factor. Not much rationale is provided for why this method was preferred, except to say that the first factor ends up being weighted more heavily and that the portfolio sizes end up consistent. This is a bit different from the way Greenblatt equally weighted both factors in his Magic Formula.
As you saw in the backtest example, many statistics are provided for each of the quintiles. These include your typical compound annual growth rate, average excess returns, and the percent of periods outperforming. In addition, the author includes some more academic statistics such as Sharpe Ratio, Beta, and Alpha. The author gets points with value investors for disparaging Beta as a measure of risk and its use in calculating Alpha. However, he still provides these statistics with every summary, I guess to cater to some quants that expect to see those stats. In addition to these statistics, many of the basic building blocks are also tested by industry sector.
Tortoriello uses a fairly intuitive and and basic set of criteria to evaluate each of the strategies tested in this book. He write, “Our criteria for a strategy that works are (1) the top quintile outperforms the market by a significant margin; (2) the bottom quintile significantly underperforms; (3) outperformance and/or underperformance have been consistent over the years; and (4) there is some linearity in the performance of the quintiles, indicating a strong relationship between the strategy and excess returns.” In addition, he also mentions that he prefers strategies with low volatility and low maximum loss for the top quintile and high volatility and high maximum loss for the bottom quintile. Most importantly, Tortoriello doesn’t forget to emphasize that a strategy that worked well in the past may not work as well in the future.
The book concludes with two chapters on pulling together the most successful building blocks. These included, but are not limited to, the following factors: EV to EBITDA, Free Cash Flow to Price, ROIC, Cash ROIC, 52-Week Price Range, 7-Month Relative Strength, External Financing, 1-Yr Reduction in Shares, EPS Score, and FCF Per Share Score. Several two-factor, three factor, and a complex multi-factor model built using Monte Carlo simulation to optimize weightings are presented. The use of Monte Carlo simulation makes me a bit uncomfortable with the potential for data mining and model overfitting, but at least it introduced me to another approach.
While the analysis in Quantitative Strategies for Achieving Alpha is valuable, this book is not an easy read. I would not recommend taking this book to the beach. Instead, I found it ideal to read this book over short intervals in a quiet study or during the quiet morning subway ride I take to work. I found myself referring back to the methods section and some individual building block backtest results several times. This is one of those books you’ll want to own and markup versus borrowing it from the library.
This book has inspired me to test out many of the stock fundamentals that are popular with value investors. Within the next few days, I’ll provide my own backtest of the Enterprise Value to EBITDA ratio from 2001 to 2011 using StockScreen123. What other fundamentals would you like to see tested?
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