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).
Using a mosaic as a metaphor
Each test divides the companies in our back-test universe into quintiles (groups of five) based on their rank on one or more investment factors. For example, a p-e ratio test would put the 20 per cent of companies with the lowest PE ratios into the first quintile, the next 20 per cent into the second quintile, all the way down to the 20 per cent of companies with the highest p-e ratios, which would be put into the fifth quintile. Portfolios are formed every quarter over our test period, and the holding period for each portfolio is 12 months.
Returns for all portfolios in each quintile are then calculated, averaged over our 20-year test period, and compared to the average return over the same period for the overall universe. A strategy is said to have investment value if the top (first) quintile significantly outperforms the universe, the bottom (fifth) quintile significantly underperforms, and outperformance/underperformance is consistent over time.
I like to use the idea of a mosaic to describe the results of our quantitative tests. A mosaic is a picture or pattern made by putting together many small-colored tiles. In a real mosaic, each tile is meaningless when viewed alone, but when put together by an artist, a beautiful pattern emerges. In our investment mosaic, each tile is a strategy that has investment value (it consistently outperforms or underperforms the market) and is understood by the investor (we know why it works).
Identifying seven basics
The second point is critical. Data mining - the search for correlations between items in a database - can uncover investment strategies that work fabulously during the test period and fail to work thereafter. By basing the investment strategies we test on sound investment theory, we ensure that the results represent fundamental principles and tendencies in the investment markets and not statistical anomalies.
When all the investment strategies presented in my book are put together, a mosaic emerges that shows quite clearly what drives the market from a quantitative point of view, and what characteristics to look for or to avoid in the companies and stocks in which we plan to invest.
So what can investors learn from quantitative analysis? One important discovery we made was that most investment strategies that are predictive quantitatively fall into seven major categories. I call these categories "the basics" precisely because they are fundamental to achieving excess returns in the stock market.
They consist of profitability, valuation, cash flow, growth, capital allocation, price momentum, and red flags (risk). There are likely more basics than the seven we identified (and the seventh, red flags, is somewhat of a catchall), but investors looking for primary market drivers need look no further than these.
From a quantitative point of view, valuation and cash-flow generation are our two strongest basics - valuation and cash-flow factors should be included in almost all quantitative models or screens. The relative valuation tests we used were simple - such as enterprise value to earnings before interest, taxes, depreciation, and amortisation or price to sales - but generated strong excess returns that were consistent over time. It seems obvious, but investors often forget to check the price they are paying for an asset.
Profitability is key
Cash-flow tests also generated strong and consistent excess returns. (Cash flow is defined as cash generated by operating activities.) Why is cash flow so important? One reason is that cash represents a reality - purchasing power - while accounting earnings are at least one step removed from that reality. Another is that a company with excess cash has financial flexibility; it can use that cash to expand its business, pay dividends, repurchase shares, acquire other businesses, and so on.
When looking for stocks likely to outperform, the investor should also favor profitability and growth factors. A company's level of profitability and its ability to consistently increase earnings and cash flow provide investors with measures of the quality of the company's productive assets (whether those assets are manufacturing facilities, a strong brand name, has an excellent customer list or a talented work force).
