Picking Stocks the Quant Way

After back-testing a wide variety of stock selection strategies, S&P analyst Richard Tortoriello finds that combining fundamental, valuation, and technical measures can help investors find winning stocks

By Richard Tortoriello

Benjamin Graham did more for the field of security analysis than any other writer or investor, in my view. Graham provided investors with a model for investment thinking that clearly delineated the difference between investment and speculation, defined the concept of intrinsic value, and provided investors with practical tools for dealing with the main problem in securities analysis—the inherent unpredictability of the future.

Graham believed that, although a company's historical record does not predict future results, past results do provide the investor with a useful guide for assessing a company's future potential. Writing as he did in the 1930s and 1940s, Graham did not have access to a computer. (The first electronic computer, the Electronic Numerical Integrator and Computer, was developed in the mid-1940s.) However, I believe that had the computer been available, Graham would have been a proponent of harnessing its power to provide investors with empirical evidence of those factors that drive stock market returns.

Nearly two years ago, I was asked to develop a series of quantitative stock-selection models for the Equity Research Dept. of Standard & Poor's. In preparation for this project, we back-tested more than 1,200 different investment strategies to determine which were predictive of future excess returns.

Taking a Data-Intensive Approach

My goal was to determine the basic factors that drive future stock market returns, from an empirical point of view, using only historical data as our raw material (balance sheet, income statement, cash-flow statement, and pricing data). In short, I set out to create a quantitatively drawn road map of the equity markets. To do our research, we used a sophisticated data-analysis program (Charter Oak Investment Systems' Venues data engine) and Standard & Poor's Point in Time database, which contains more than 20 years of originally reported (unrestated) data for about 150 data items and 25,000 individual companies.

This data-intensive approach to investment analysis yielded clear results. Certain strategies consistently outperformed the market over the two-decade test period, while others consistently underperformed. The results of this research are published in Quantitative Strategies for Achieving Alpha (McGraw-Hill, November 2008). In this book, I present a wide variety of investment strategies that predict excess returns, and I show investors how to combine individual investment strategies into more complex screens and models that can be used to generate strong potential investment ideas, create quantitative portfolios, or simply help investors better understand the market from a quantitative point of view.

In structuring our back tests, we kept in sight one basic principle: Numbers can lie. If a back test is not constructed carefully, or if too few years of data are used, back-test results will be unreliable. The researcher must consider different forms of statistical bias, such as look-ahead bias and survivorship bias. (Our database protected our tests from both.) Returns must be calculated consistently. We used a stock's annual price change plus dividends and cash-equivalent distributions of value (such as spinoffs). And a clear back-test universe must be defined: Our universe consists of the largest 2,200 stocks in our database selected by market capitalization with a minimum share price constraint ($2, to keep out volatile penny stocks).