Book review: The Little Book that Still Beats the Market, by Joel Greenblatt

September 28, 2013

(9/30/2013 update: The American Association of Individual Investors tested the performance of the magic formula described in this book. The test results are published in the AAII.com stock screens web site. 4/5/2016 update: A ‘revisit’ is added to General Comments)

about the author

Joel Greenblatt is a celebrity among formula investors for devising the “magic formula” to screen stocks. Greenblatt received his bachelors and MBA degrees from the Wharton School at the University of Pennsylvania in 1979 and 1980. He is the founder and managing partner at Gotham Capital, a hedge fund, and is the author of several books on investing. He is an adjunct professor at the Columbia University Graduate School of Business.

General comments

For the benefit of the reader, Greenblatt attempts to explain to his young son a method for earning profits by investing in a portfolio of 20-30 stocks. His method depends on using a magic formula to select ‘good’, ‘cheap’ stocks for purchase and annual replacement. Greenblatt’s method is designed for ordinary investors who commit to the plan at least 3-5 years. It solves the perennial problem of deciding when and which stocks to trade. As of this year, 2013, Greenblatt provides free access to an updated list of screened stocks on his website.  Shortcomings of the method and book are described at the end of this review.

Revisited in Apri, 2016:  Greenblatt’s book reminds us that buying stocks on pure speculation is likely headed for a loss.  Instead, good buying decisions need research and measurements.  His method is to condense the company’s financial statements to 2 measurements in the belief that earning cash from customers is the key element of a successful business: 1) “Earnings yield” is a measurement of market valuation that shows how highly the earned cash is valued by investors in the stock market; higher is better.  2) “Return on invested capital” is a measurement of management efficiency that shows how much earned cash is derived from the company’s assets; higher is better.  Greenblatt claims that a new basket, every year, of high-scoring stocks will collectively beat the stock market after 5 years.  About 50-60% of the purchased stocks will outperform the remainder of 20-30 stocks in his baskets.  Greenblatt’s method is not designed for dividend reinvestment plans and other long term strategies for buying smaller baskets of stocks.  I’m inclined to believe that more than 2 measurements are needed for making small-basket, multiyear investments.

What is a good business?

Jason was an 11 year old business man who sold sticks of gum at school for a huge profit. Suppose Jason opened a chain of gum stores after graduation from high school and achieved success. Now he wants to sell half the business for $6 million. He plans to split the ownership into 1 million equal pieces, called shares, and sell ½ million shares at $12/share (Jason will keep ½ million shares for himself). Is that a good price for the investor? According to Jason’s income statement from last year:

Gum sales   from 10 stores $10 million
Cost of gum $6 million
Other   expenses (rent, salaries, etc.) $2 million
Profit   before taxes $2 million
Taxes (40%   tax rate) $0.8 million
Net profit $1.2   million  ($1.20/share)

One share costing $12 today earned $1.20 last year. That earning, called an earnings yield, is 10% of share price ($1.20/$12.00 = 10%). The 10% yield is better than a risk-free return of 6%, so the share price seems to be acceptable. Will Jason’s earnings grow? That depends on how well he operates the business. Here’s Jason’s return on capital:

Cost of   property and equipment (capital) $4 million
Profit   before taxes (return) $2 million
Yearly return on capital 50%

Jason’s 50% return on capital is better than if he invested $4 million in U.S. government bonds for a risk-free return of 6%. He has a very good business. Good businesses attract competition that may eventually reduce profits. Until then, a high return on capital for one year shows temporary success and reflects a competitive advantage. Warren Buffet, a stock market master, buys stocks of good businesses at bargain prices that show signs of growth in value over time.

Stock Market Master

The stock market master earns higher returns than otherwise earned from risk-free U.S. government bonds (assume the minimum U.S. government bond rate is 6%). The classic method is to buy stocks of good companies at bargain prices. Benjamin Graham was a stock market master who invested with a “margin of safety” by paying less than the company was worth. Graham, who knew that market prices fluctuate according to moods of pessimism and optimism, sold his holdings when an optimistic market was paying more than the company was worth. The success of Graham’s method depended on the availability of many bargains during the era of the Great Depression. Today, very few stocks fit Graham’s requirements.

Greenblatt claims that a revision of Graham’s method will beat today’s market, and future markets, with low risk to the investor. His revision, called the magic formula, is to buy stocks of profitable companies when they are trading at low prices.

Magic formula

All businesses need working capital and fixed assets for successful operation. So, why not rank businesses on the effective use of those assets? The magic formula ranks stocks according to two criteria: return on capital and earnings yield –Greenblatt defines the earnings yield and return on capital differently from the generally used inverse of the price-to-earnings ratio (E/P) and the return on assets (ROA)–.

Good companies have high returns on capital; the higher, the better. The return on capital should exceed the return from a risk-free investment; otherwise, the company is better off investing in the risk-free U.S. government bond. Stocks with high earnings yields offer bargain prices, and higher yields are better bargains. The earnings yield should also exceed the return from the risk-free U.S. government bond.

A stock market index measures the average price of the average stock, but the magic formula selects the above average stock at a below average price. Therefore, it’s a good bet that a basket of magic formula stocks will beat the market. To test this bet, Greenblatt created a portfolio of 30 top-ranking stocks among 3,500 U.S. stocks screened by the magic formula. He used a 3-step screening process: First, all stocks were ranked from 1 (highest return on capital) to 3,500 (lowest return on capital). Second, the same stocks were ranked from highest to lowest earnings yield. Third, the combination of scores were ranked from best to worst (e.g., a ranking of 385 (232 + 153) was better than a ranking of 1,151 (1,150 + 1)). The test portfolio was replaced with a new set of top-ranked stocks every year during the 17 year period of 1988-2004. The market value of the test portfolio grew by 30.8% per year compared to the 12.4% annualized growth of the S&P 500 index. Greenblatt created other test portfolios derived from 2,500 U.S. stocks with market caps above $200 million and 1,000 U.S. stocks with market caps above $1 billion. Both test portfolios grew by 23.7% or 22.9% per year depending on the stock universe.

Did the magic formula make a few lucky picks? Greenblatt opined that a few lucky picks could not bias the outcome.

Will bargain stocks eventually disappear? Greenblatt divided the universe of 2,500 U.S. stocks into 10 subgroups of 250 stocks according to the magic formula’s rankings (i.e., the first subgroup had the highest rankings and the last subgroup had the lowest rankings). The first 6 subgroups of highest ranking stocks (a total of 1,500 new stocks each year) outperformed the S&P 500 during the 17 year test period, indicating a plentiful supply of bargain stocks.

Did subgroup returns correlate with subgroup rankings? It seems so since the returns from subgroup 1 beat the returns from subgroup 2, and so on; however, Greenblatt did not report the correlation.

What if everyone uses the magic formula? Greenblatt opined that they won’t. New participants eventually quit at the first sign of short-term bad news (most investors want to own the most popular stocks, but the magic formula finds less popular stocks!).

What is the risk of losing money over the long term? The good news is that the plan doesn’t lose money and always beats the market during rolling 3-year periods (rolling refers to the calculation of a previous time period every month).

Conclusions

The magic formula offers high returns at low risk based on the simple logic of screening stocks based on the earnings yield and return on capital. The earnings yield helps to sort the universe of stocks for companies that earned a lot last year compared to today’s stock market price. The return on capital is used to identify companies that earned a lot last year compared to the cost of operating the business. The advantage of using the magic formula is to screen for a number of undervalued stocks on a regular basis.

The disadvantage of the magic formula portfolio is the demand for frequent, long-term attention. There are repetitive screenings, many trades, and numerous tax records.  The portfolios described by Greenblatt have a 100% turnover that incur 2 trading fees per stock per year. Since the book does not discuss the potential impact of trading fees on investment return, I estimated the effect of trading fees (see chart) on one of Greenblatt’s test portfolios.  Don’t expect high returns unless you invest at least $15,000 in the portfolio.

magicformualcost

Click on the following links for additional book reviews: 1. A listing of returns from this and other formula investing plans.  2.  Why You Should Take Joel Greenblatt’s ‘Magic Formula’ Stocks Seriously.  3. video, What’s Joel Greenblatt’s Magical Investing Formula?

Joel Greenblatt. The Little Book that Still Beats the Market. John Wiley & Sons, Inc. 2010.


Book review: What Works on Wall Street, by James P O’Shaughnessy.

December 29, 2011

(9/29/2013 Update:  The American Association of Individual Investors created test portfolios of the Cornerstone Growth and Value strategy described in this book and of several best strategies from O’Shaughnessy’s newest book on formula investing (entitled Predicting the Markets of Tomorrow: A Contrarian Investment Strategy for the Next Twenty Years).  The test-portfolio returns are published free of charge in the AAII.com stock screens web site.)

Introduction

Author James O’Shaughnessy tested a variety of strategies for investing in stocks with the use of numerical models.  His winning strategies outperformed both the broad U.S. stock market and Standard & Poor’s 500 Stock Index by wide margins.

Approach

Mr. O’Shaughnessy cited publications from the scientific and financial literature to support the policy of investment-by-formulation rather than investment-by-intuition.  Formulation involves the application of stock data to a quantitative model and intuition depends on human judgment.  He formulated numerous single-factor and multifactor models of investment, then back-tested the models by analyzing historical returns over 40- or 52-year time periods.  The benchmark of performance was one of several stock universes that the author obtained from Compustat’s large database.   The universes were categorized according to levels of market capitalization among stocks.  The risks and returns of his test portfolios were compared to the appropriate universe.

Winning strategies

PERFORMANCE TABLE

The PERFORMANCE TABLE presents a selection of the author’s investment strategies that yielded exceptional returns.  Column headings are the labels of 7 investment strategies that were tested over 40 years (white columns) and 52 years (blue columns), both periods ending on 12/31/2003.  Notice that the cornerstone and S&P500 strategies were tested in both periods.  Row headings are the labels of 4 statistics commonly used to describe the risk-return performance of investment portfolios.  Cells contain numerical spreads.  Each spread is the difference in statistical results between an investment strategy and the All Stocks universe (described in the Appendix).  For example, a spread of 0 would mean that the outcomes of the strategy and universe are identical.

The spreads in the PERFORMANCE TABLE provide a comparison of exceptional strategies to the All Stocks UniverseCAGR spread: Compound annual growth rate (CAGR or geometric mean) is a statistic for the annualized growth rate of the portfolio’s market value.  Positive spreads show the desired result, namely that the strategy outperformed the universe.  All strategies outperformed the universe except the S&P500, which performed worse than the universe.  Std Deviation spread: The standard (Std) deviation is used to evaluate an investment’s risk, which is the chance that an investment unexpectedly increases or decreases in value.   A larger standard deviation implies a greater scatter of portfolio values over the time period of analysis.  In the performance table, a positive std deviation spread infers that investing according to strategy is riskier than investing in a representative sample of the universe.  All strategies except the S&P500 were riskier than the universe.   Downside risk spread:  Downside risk is the chance that the investment’s market value will decline.  In the performance table, the desired result is a negative downside risk spread.  The S&P500 and mending values(tri-ratio) had lower downside risks than the universe.  Investors who are risk averse might consider using these strategies.  Sharpe Ratio spread: Sharpe ratio is a statistic that relates investment return (numerator) to investment risk (denominator).  In the performance table, the desired result is a positive Sharpe ratio spread.  All strategies except the S&P500 outperformed the universe.

Here’s a description of exceptional strategies listed in the performance table:

  • Cornerstone improved (book table 20-7), a strategy tested over 40 years while the portfolio is rebalanced at monthly intervals to account for stocks with a monthly depreciation of price.   The strategy selected 50 stocks from the All Stocks Universe (described in the Appendix of this article) with the best 1-year price appreciation among stocks with market capitalizations exceeding the deflated $200 million value, having a Price-to-Sales ratio (P/S) below 1.5, showing 3- and 6-month price appreciations above average, and showing a 12-month increase in earnings-per-share (EPS).  The selected stocks were equally weighted.  [notes: Multifactor strategies might reduce risk and increase return.  Betting on price momentum supports the theory that stock prices have “memory” and opposes the claim that past price performance cannot predict future price performance.]    
  • Mending small value (book table 18-3), a strategy tested over 40 years while the portfolio was rebalanced at monthly intervals.   The strategy selected 50 stocks from the All Stocks Universe with the best 3-, 6-, & 12-month price appreciations coupled with a low P/S from the sub-universe of small stocks.  The small stocks had market capitalizations above the inflation-adjusted value of $185 million USD and below the database average.  The selected stocks were equally weighted.
  • Cornerstone, a strategy tested over 52 years (book table 20-1) and 40 years (book table 20-7) while the portfolio was rebalanced at yearly or monthly intervals.  The strategy selected 50 stocks from the All Stocks Universe with the best 1-year price appreciation among stocks with market-capitalizations above $200 million USD, P/S ratios below 1, and 12-month increase in EPS.  The selected stocks were equally weighted.  [note: A side-benefit of annual rebalancing is the lower tax rate on annual capital gains compared to monthly capital gains.]
  • S&P500 (book tables 4-1, 17-2), an index of 500 U.S. stocks with the largest market capitalizations exclusive of foreign stocks traded in U.S. stock exchanges.  The test portfolio was weighted according to the market capitalization of the stocks.
  • Mending value(tri-ratio) (book table 16-4), a strategy tested over 52 years while the portfolio was rebalanced at yearly intervals.  The strategy selected 50 stocks from the All Stocks Universe with the best 1-year price appreciation among stocks pre-screened for desired ranges of low price-to-earnings ratio (P/E), low price-to-book ratio (P/B), and low P/S.  The selected stocks were equally weighted.  [notes: The author found that investing in bargain, single-value factors (i.e., low P/E, low P/B, low P/S, or low P/C) provided superior returns among several universes (i.e., all stocks, large stocks, small stocks, market leaders) whether using monthly or annually rebalanced test portfolios.  The disadvantage of using single-value factors was volatility, which makes it difficult for “jittery investors” to sustain the strategy in real time with real money.  “Jittery investors” tend to prefer index funds.]
  • Mending value(P/B) (book table 16-1), a strategy tested over 52 years while the portfolio was rebalanced at yearly intervals.  The strategy selected 50 stocks from the All Stocks Universe with the best 1-year price appreciation among stocks screened for P/B below 1.  The selected stocks were equally weighted.
  • Mending value(P/S) (book table 16-2), a strategy tested over 52 years while the portfolio was rebalanced at yearly intervals.  The strategy selected 50 stocks from the All Stocks Universe with the best 1-year price appreciation among stocks screened for P/S below 1.  The selected stocks were equally weighted.

Summary of the author’s strategies

The data published by author James O’Shaughnessy are re-plotted in the following chart to show that the Sharpe ratio is a predictor of long-term return.  The Sharpe ratio is the difference between a portfolio’s rate of return and that of a risk-free investment, such as the 10-year U.S. Treasury bond, divided by the standard deviation of the portfolio’s return.  The result is an expression of the portfolio’s risk-adjusted return, in which a high ratio is the desired value.

Chart.  Outcomes of the back-tests.

The chart’s X axis, labeled relative Sharpe Ratio, displays values for the quotient of a test portfolio’s Sharpe ratio divided by the Sharpe ratio of the benchmark universe.  X >1 is the domain for portfolios with Sharpe ratios exceeding (better than) the universe.  The Y axis, labeled relative Return, displays values for the quotient of the final market value of the test portfolio divided by the final market value of the universe.  Y >1 is the range for test portfolios with higher (better) investment outcomes than the universe.   The dashed line in the chart represents the best fit of all data to the exponential equation Y = aebX.  A regression analysis provided the values of a = 0.0144 and b = 4.309 for the equation, and R2 = 80.2% for the ‘predictability’ of the equation.  The data-point markers are black triangles for all back-tested portfolios except blue dots for the winning portfolios and yellow squares for the S&P500 Index.  The winning portfolios and S&P500 Index were discussed in the preceding table of this article

Conclusions

This is a book about picking stocks that yield high returns.  It was written to provide useful information for household and institutional investors.  Due to the book’s vast number of statistics, the more appropriate audience is the institutional investor who manages stock portfolios for clients.  The author’s winning strategies are based on historical data reviewed over 40-52 year time periods.  Readers should be cautioned that applying the winning strategies to 5-10 year time periods might not achieve the same fantastic results.

What Works on Wall Street, A Guide to the Best Performing Investment Strategies of All Time.  Third Edition.  James P. O’Shaughnessy.  McGraw-Hill, New York, 2005.

Appendix

All Stocks Universe table

Legend:  The All Stocks Universe (book tables 16-2, 17-2) was comprised of stocks in the Standard & Poor’s Computstat database with market capitalizations above $185 million USD.  Smaller market-capitalized stocks were excluded due to the high risk of illiquidity.  Compustat is the largest database for the U.S. Stock market


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