As the students of investment management domain the one thing which we have learnt is that every professional investor or fund manager is under pressure to generate alpha with the rise in the allocation to index funds and ETF’s by investors globally the portfolio managers are trying numerous strategies to create the edge in the markets, some strategy which has gained popularity – Value, growth, momentum, smart beta, Factor Investing, Quant based algorithms to find mispriced companies to name a few.
As an individual investor one must find out what works for him given the risk he is taking with his hard-earned money and not fall into the complexity of investments which professional money managers try to implement because it may be good or bad but finding out what works for you will help you in generating long term wealth. Sometimes we like complexity as it challenges our intellectual capacity but for an individual investor or good money manager goal should be to generate alpha whether that can be as simple as buying companies that are near their 52-week highs or some complex long-short strategy.
Though there isn’t one right strategy, but an investor who has generated an annualized return of more than 40% between 1985 and 2006 must have done something great, and learning and applying his strategy and his Factors of investing are worth the efforts. Joel Greenblatt, a legendary value investor, a writer and an adjunct professor at Columbia University Graduate School of Business, founded the hedge fund, Gotham Capital, in 1985 with an initial investment of $7 million. Greenblatt is the author of 2 best-sellers ‘You Can Be a Stock Market Genius’ and ‘The Little Book That Beats the Market’, which have inspired and changed the outlook of many investors all across the world. He has also recently released a new book, ‘The Big Secret for the Small Investor’. As I mentioned, his firm Gotham Capital, returned 40% annualized returns over the twenty years of 1985 to 2005.
In his book, The Little Book that Beats the Market, Greenblatt explains his magic formula in which he uses Earnings Yield and Return on Capital employed as his choice of measurement (“Factors”) for constructing a portfolio method on how to buy mispriced stocks in the market. Greenblatt prefers companies that have High RoCE but at the same time should also have high earnings yield, therefore, being a long-term investment strategy designed to help investors buy a group of above-average companies but only when they are available at below-average prices.
This is a combination of factor-based investing and value investing. Factor investing is one such domain in the investment industry, the concept behind factor investing is as old as investing itself which is direct exposure to the drivers of return, which have shown more stable relationships with each other.
The concept of value investing dates back to Benjamin Graham and David Dodd’s classic work in 1934. You can use the various choice of measurement as factors for value. Fama and French chose to formalize the idea by measuring value as the ratio of the book value of a company’s assets to its market value. There are numerous ways of measuring value, including, for example, earnings relative to a company’s market value. The choice of measurement is critical: cumulative returns of these portfolios can be quite different.
Greenblatt calculated hypothetical returns for the 1988-2004 period. The magic formula generated 30.8% CAGR, while the S&P 500 generated 12.4 CAGR%. So this formula beats the market by 18% per year. It seems too good to be true…
To understand and apply the strategy we back-tested the magic formula’s factors and process in India equity markets from 2002 and results are pretty astonishing.
Generally, investors consider a certain benchmark for market cap to define the universe of stocks to be considered while backtesting an investment strategy. But for backtesting an investment strategy for a decade or two, this is not the appropriate way to define the universe. For example, an individual defines the universe of stocks with a market capitalization of more than 500 crores. Though the numbers of stocks under the universe in 2020 may be more than 1000 but in 2002 the numbers of stocks with a market cap of more than 500 crores were less than 150.
So to define the stock universe, we have taken the largest 750 companies. In India, the largest 750 companies form nearly 99% of the total public equity market.
The magic formula is built based on the two factors: Return on capital employed (RoCE) and Earnings Yield (EY).
Return on capital employed (RoCE) helps us to understand how much profit is generated for each unit of capital employed. It indicates how efficiently a company uses its capital.
Return on capital employed is measured by calculating the ratio of pre-tax operating earnings (EBIT) to Invested Capital. Invested capital is the summation of Net Worth and Net Debt. EBIT is the pre-tax operating earnings of a business. EBIT is used in place of Net Profit because the companies operate with different levels of debt and differing tax rates. Using EBIT allowed us to view and compare the operating earnings of different companies without the distortions arising from differences in tax rates and debt levels.
Given the fact that we have considered pre-tax operating earnings (EBIT), to remain consistent, we have considered the total capital employed and not only the Networth (used in an ROE calculation). We have calculated net debt as follows: Net Debt = Long term borrowings + Short term borrowings – Cash and cash equivalents – non-core investments.
Earnings yield is measured by calculating the ratio of pre-tax operating earnings (EBIT) to Enterprise value. Enterprise value is the summation of Market Capitalization (Value of Equity) and Net Debt. The basic idea behind the concept of earnings yield is simply to figure out how much a business earns relative to the purchase price of the business.
This ratio was used rather than the more commonly used P/E ratio (price/earnings ratio) or E/P ratio (earnings/price ratio) to remain consistent because E/P ratio is measured by Net Income to Value of Equity while we have taken pre-tax operating income and total capital employed for calculating RoCE. By using EBIT to enterprise value, we can calculate the pre-tax earnings yield on the full purchase price of a business.
Based on these two factors, the entire magic formula is created. The point for using these two ratios is that you would like to own a business that earns a higher return on capital than one that earns a low return on capital. And you would like to own a business that earns more relative to the price you are paying rather than owning a business that earns less relative to the price you are paying.
So, if you just stick to buying good companies (that have a high return on capital) and to buying those companies only at bargain prices (that have a high earnings yield), you can build a portfolio of good companies at bargain prices using this investment strategy.
Ranking the companies:
The formula starts with a list of the largest 750 companies listed on Indian stock exchanges. It then assigns a rank to those companies from 1 to 750 based on their return on capital. The company that has the highest return on capital would be assigned a rank of 1, and the company with the lowest return on capital (company losing money) would receive a rank of 750. Similarly, the company that had the 115th best return on capital would be assigned a rank of 115.
Next, the formula follows the same procedure on earnings yield. The company with the highest earnings yield is assigned a rank of 1, and the company with the lowest earnings yield receives a rank of 750. Likewise, the company with the 5th highest earnings yield out of our list of 750 companies would be assigned a rank of 5.
Finally, the formula just combines the rankings. The formula isn’t looking for the company that ranks best on return on capital or the one with the highest earnings yield. Rather, the formula looks for the companies that have the best combination of those two factors. So, a company that ranked 115 in return on capital and 353 highest in earnings yield would receive a combined ranking of 468 (115 + 353). A company that ranked 300 in return on capital but ranked 5 in earnings yield would receive a combined ranking of 305 (300 + 5).
The companies that receive the best-combined rankings are the ones that have the best combination of both factors. In this system, the company that had the 115 best return on capital could outrank the company that ranked 300 in return on capital because we could purchase the company that had the 300 rank in return on capital for a price low enough to give us a very high earnings yield.
Getting excellent rankings in both categories (though not the top-ranked in either) would be better under this ranking system than being the top-ranked in one category with only a pretty good ranking in the other. The company like Nestle India Limited or Hindustan Unilever Limited may be ranked in the top 10 in terms of return on capital but will be miss out because of extremely low earnings yield. On the other hand, companies like BPCL or HPCL may be ranked in the top 10 in terms of earnings yield but will be miss out because of extremely low return on capital.
One important point to consider is that Banks, NBFCs, and Insurance companies are not considered in the universe of stocks because their business model and ratios used to analyze those businesses are different and hence ratios used in these formulas will be inappropriate to use.
Also the companies with negative Net Worth are excluded because negative Net Worth amplifies RoCE. For example, in 2017 Jet airways has the Net Worth of approximately -6,500 crores and Net Debt of approximately 7500 crores. And the company had EBIT of around 820 crores which results in RoCE of nearly 82%, which is the wrong representation of RoCE.
In his book, Joel Greenblatt suggests to own 20 to 30 stocks. In the magic formula’s case, we want the average (i.e. the average return for a portfolio of stocks chosen by the magic formula). Since average results for the magic formula will, hopefully, mean extraordinary investment returns, owning many different stocks chosen by the magic formula should help ensure that we stay pretty close to that average.
In our backtesting, we have built an equal weighted portfolio of 25 stocks (i.e. 4% allocation each). We believe if an individual is investing based on a quantitative strategy, a portfolio of less than 15 stocks is very concentrated.
Let us take an example to understand churning of portfolio, we are in 2018, by at the end of June 2018, we have collected the data of FY18 useful for measuring RoCE and EY i.e. EBIT, Net Worth and Net Debt. Using the market cap on the last trading day of June 2018, we have calculated RoCE and EY of all the companies and giving the rankings we bought the top 25 companies. We will hold the 25 companies for 1 year and do the same process on June 19 and buy the companies which are new in the top 25 list and sell the companies which are not in the top 25 list. Hence, the portfolio is churned once a year on the last trading day of June.
We have done churning on the last trading day of June instead of the last trading day of March as the data used to select stocks weren’t available to investors at the time of March. Hence, we have tried to eliminate look-ahead bias.
We used this process and backtested the data for the last 18 years (2002-2020) and the results are surprising.
Results and Comparison with Benchmarks:
Over the last 18 years, owning a portfolio of 25 stocks that had the best combination of a high return on capital and a high earnings yield would have generated approximately 35.4 percent CAGR. Investing at that rate for 18 years, 1 crore would have turned into well over 235 crores.
On the other hand during those same 18 years, Nifty 50 generated a return of about 12.7 percent CAGR. At that rate, 1 crore would have turned into approximately 8.6 crores. Nifty 500 generated a return of about 13.5 percent CAGR. At that rate, 1 crore would have turned into approximately 9.7 crores. Nifty Midcap 100 generated a return of about 14.6 percent CAGR. At that rate, 1 crore would have turned into approximately 11.6 crores. Nifty Smallcap 100 generated a return of about 10.6 percent CAGR. At that rate, 1 crore would have turned into approximately 5.03 crores.
As cumulative return charts are prone to starting point bias and do not offer insight into the consistency of a strategy’s performance, the comparison of yearly and rolling returns provides a little more insight.
In the last 18 years, the portfolio has underperformed Nifty 50 and Nifty 500 in only 5 years. Also the portfolio has underperformed Nifty Midcap 100 and Nifty Smallcap 100 in 5 years and 2 years respectively.
3-Year rolling CAGR:
Over any 3 years between 2002 and 2020, the portfolio has unperformed Nifty 50 and Nifty 500 twice: 2010-2013 and 2017-2020. On the other hand, the portfolio has never unperformed Nifty Midcap 100 and Nifty Smallcap 100 over any 3 years.
5-Year rolling CAGR:
Over any five years between 2002 and 2020, the portfolio has never underperformed any of the indices.
As can be seen in the data, it doesn’t work all the time (like any other strategy). It might not work for years. If your time horizon in short i.e. less than 5 years, this may not work for you.
Most people just won’t wait that long. If a strategy works in the long run i.e. if it sometimes takes three or even five years to work, most people won’t stick with it. After a year or two of performing worse than the market averages, most people look for a new strategy— usually one that has done well over the past few years.
The top 25 companies have generated exceptional returns but if this thesis of identifying the best combination of high return on capital and high earnings yield is true, then the next 25 companies i.e. (26-50) should have worse returns than the top 25 companies and the next 25 companies (51-75) should have worse returns than the prior top 25 companies (25-50).
Let us divide 500 companies into 20 equal groups based on their rankings. Group 1 would contain the top 25 companies, Group 2 would be the second-highest-ranked group of 25 companies, Group 3 would be the third-highest ranked group, and so on till Group 20.
Therefore, the Group 1 should generate the best return and the Group 20 should generate the worst return. Let go through the data.
It’s interesting. The magic formula doesn’t just work for only 25 stocks. The magic formula appears to work in order. The best-ranked stocks perform the best and as the ranking drops, so do the returns. There is a decreasing trend in the returns generated as we move down even though the trend is not linear. INR 100 invested in Group 1 will increase to 23,521 relative to 2,494 in Group 10 and just 831 in Group 20. Group 1, our best-ranked stocks, beats Group 20, our worst-ranked stocks, by nearly 23 percent a year.
The most common argument against this strategy will be that the magic formula may be picking companies that are so small that few people can buy them. Often, small companies have very low liquidity and even a small amount of demand for those shares can push share prices higher. If that’s the case, the formula may look great on paper, but in the real world, the fantastic results can’t be replicated. That’s why the companies chosen by the magic formula be pretty large and their shares must be liquid. Let us check the average and median market cap of the companies in the portfolio each year.
Give the companies of these sizes, most of the individual investors should be able to buy a reasonable number of shares without pushing prices higher.
But let’s see what happens when we raise the bar a little bit. It would certainly be nice if the magic formula worked for companies whether they were large or small.
Let’s look back and see what happened when we narrowed the group to just the largest 250 stocks. Even large institutional investors like mutual funds can buy these stocks.
Does it work for largecap stocks?
Over the last 18 years, owning a portfolio of 25 stocks that had the best combination of a high return on capital and a high earnings yield in the top 250 stocks would have returned approximately 23.7 percent CAGR. Investing at that rate for 18 years, 1 crore would have turned into well over 45.8 crores.
On the other hand during those same 18 years, Nifty 50 generated a return of about 12.7 percent CAGR. At that rate, 1 crore would have turned into approximately 8.6 crores.
It appears that even the largest investors can practically double the nifty’s compounded annual return simply by following the magic formula.
In the last 18 years, owning a portfolio of 25 stocks that had the best combination of a high return on capital and a high earnings yield in the top 250 stocks has underperformed Nifty 50 in 4 years.
Rolling Returns for portfolio built from the top 25 stocks:
3-Year rolling CAGR:
Over any 3 years between 2002 and 2020, the portfolio has unperformed Nifty 50 twice: 2010-2013 and 2017-2020.
5-Year rolling CAGR:
Over any five years between 2002 and 2020, the portfolio has never unperformed Nifty 50.
Let us check the average and median market cap of the companies.
Cyclical and Commodity companies:
India has several cyclical and commodity sectors and that is why they are constraints in using this methodology even though the strategy has worked well over the long run. In cyclical and commodity sectors, you have to sell the stocks when the companies are generating peak RoCE instead of buying them. So, a lot of these companies may be included in the top 25 stocks even though it is not the time to buy them.
Let me give a few examples, a lot of packaging companies generated RoCE in north of 35-40% in FY11 even though the average RoCE over the last cycle may be near 14-15% for the Industry. Similarly paper and electrode companies in FY18 and FY19 may be included, to state a few names.
To deal with this, we have included the companies in the universe if their last 7 years median RoCE is more than 15%. Let’s say we are applying the methodology in FY08, we will include the companies in the universe if their median RoCE is more than 15% between 2002 to 2008.
We have the data from 2002, so we have applied this methodology from 2008-2020. One drawback of applying this strategy is that if we don’t have data for the last 7 years then such companies will be excluded from the universe. For example, for companies like Polycab or HDFC AMC, the data may be not available for the past 7 years and hence excluded from the universe.
Over the last 12 years, owning a portfolio of 25 stocks after applying the RoCE filter would have returned approximately 17.9 percent CAGR. Investing at that rate for 12 years, 1 crore would have turned into well over 7.25 crores.
On the other hand during those same 12 years, Nifty 50 generated a return of about 7.1 percent CAGR. At that rate, 1 crore would have turned into approximately 2.27 crores. Nifty 500 generated a return of about 7.3 percent CAGR. At that rate, 1 crore would have turned into approximately 2.34 crores. Nifty Midcap 100 generated a return of about 7.7 percent CAGR. At that rate, 1 crore would have turned into approximately 2.42 crores. Nifty Smallcap 100 generated a return of about 1.6 percent CAGR. At that rate, 1 crore would have turned into approximately 1.2 crores.
In tough times like 2009, 2012, 2018, 2019 i.e. when the standard portfolio has not beaten the benchmark, the portfolio with RoCE filter has performed better than standard portfolio even though the overall CAGR of standard portfolio is better by some margin from 2008 to 2020.
To be on the conservative side and improve the biases or technical difficulties that could have been there while buying and selling the stocks on 30 June. If we incorporate slippage costs and reduce return by 5% each year, then also it will be a respectable CAGR near 30%.
Considering cash on the balance sheet while calculating RoCE:
One difference that can be made to Greenblatt’s approach is that while he adjusts for cash and looks only at returns on invested capital, we can look at returns on total capital including cash (by including cash income in the numerator). This penalizes firms that hoard cash as high cash balances lower their RoCE.
More about Gotham Funds:
After returning from the hedge fund industry in 2009, and bringing his strategy to wealthy mutual fund investors in 2012, Greenblatt is having trouble beating benchmarks. None of his funds have outperformed the S&P 500 Index total return since their inception.
Gotham’s stellar performance starting in the mid-1980s came from Greenblatt maintenance of a concentrated portfolio. But being a mutual fund managing more than $5.5 Billion in a diversified portfolio of hundreds of stocks, it is difficult to beat the benchmark.
Some thoughts on taxation:
For individual stocks in which we are showing a loss from our initial purchase price, we will want to sell a few days before our one-year holding period is up. For those stocks with gain, we will want to sell a day or two after the one year is up. In that way, all of our gains will receive the advantages of the lower tax rate afforded to long-term capital gains and all of our losses will receive short-term tax treatment (a deduction against other sources of income that otherwise could have been taxable). Over time, this minor adjustment can add significantly to our after-tax investment returns.
There aren’t many investors who have consistently generated 30-40% returns every year over decades – and even fewer who go on to offer investment advice and their secret sauce to retail investors. Joel Greenblatt is one of those rare investors who shared his “magic formula” and provided investors with his winning strategy. Joel Greenblatt himself admits that this magic formula won’t always work in all types of market conditions but we should sticks to it for a long period.
As we mentioned earlier, this is a combination of factor-based investing and value investing. Value investing works when the macro environment is conducive. In challenging times, investors flock to safety even if it is expensive and so valuations become secondary.
One way to use this formula is to build a portfolio based on a complete data-based investment approach. Another way is to put together a list of top 50 or 100 stocks as ranked by the magic formula. And then do your due diligence and create a portfolio of 15-20 companies. In investing, there is no hard-fast rule, it is up to you to decide which method you are comfortable with. Many mutual fund houses & researchers use this magic formula to filter stocks. For them, the magic formula is the form of idea generation.
But all that means is that the magic formula has worked in the past. How do we know the magic formula will continue to work in the future?
To answer this I will quote Joel Greenblatt himself, “Most people won’t believe it. Or, believing it, won’t have the patience to follow its advice. That’s good, because the more people who know about a good thing, the more expensive that thing ordinarily becomes. However, using the magic formula for your investments going forward will guarantee results similar to the stellar performance of the past. I can’t know that.”
“The hard part is making sure that you understand why the magic formula makes sense. The hard part is continuing to believe that the magic formula still makes sense even when friends, experts, the news media, and Mr. Market indicate otherwise. Lastly, the hard part is just getting started.”
Our study is not biased because the database used in the study included companies that later went bankrupt. Hence, survivorship bias is taken care of.
The formula will take significantly less time and effort than doing the “work” yourself, and will provide better results than most of the people. The strategy is not 100% effective. But it surpasses the average quantitative or fundamentals-based stock-picking strategy. As an investor, I would consider setting aside 10-15% of my portfolio for this strategy to start with. I am hopeful that magic is not only in the air and this strategy will succeed in the future.
Note: The backtested results are hypothetical and do not represent actual results. An investor applying this strategy would incur transaction costs and taxes on short-term gains which are not reflected in the backtest results. Also, the backtests do not include dividends or their reinvestment.