“It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.”
Buffett’s meme quoted above is one of the toxiest in investing circles.
Because it’s not necessarily true.
It may be true in some situations, for a given portion of the market, for some individuals with a certain temperament, following a certain strategy…sometimes.
Buffett might as well have said:
“I have found, that in my own experience, and in my own unique circumstances, It has been better (how is “better” measured? Returns? Peace of mind?) to buy a wonderful company at a fair price than a fair company at a wonderful price, at least for the type of universe of stocks that I am restricted to.”
Buffett is fundamentally saying that Quality outweighs Valuation.
That is true if you are Buffett. If your capital is large that you are restricted to Mega caps; If you cannot trade in and out of positions easily and are stuck with your positions for many years, even decades; If you have access to free leverage (insurance float); If you can consistently find the best deals, often at prices not available on the free market;
If that is your situation – then yes, maybe it’s far better for you to buy wonderful companies at a fair price than fair companies at wonderful prices.
If you’re not Buffett, it will be far better that you look at the data and see what works.
The subject of this spotlight article is – Quality.
We will try to define it and see how it can be used to improve the returns of a value portfolio.
What is Quality?
Going back to the beginning, Graham and Dodd, in their book Security Analysis (1934), were well aware of the importance of Quality, and stated: “investment must always [consider] the price as well as the quality of the security.” Even when discussing net-nets, the cheapest stocks in the universe, Graham and Dodd had recommended to favor those companies which pass some basic quality tests.
The premise is – The better the Quality of the company, as measured by its profitability rates, growth, operational margins, cash flow management, and financial stability, so are the prospects of the company and its stock price.
But it wasn’t until 2013 that a smart-beta factor was devised for Quality by Clifford Asness of AQR, which he named:
QMJ = Quality-minus-Junk
A formal definition of the QMJ stock factor was needed in order to test the merits of Quality with rigorous academic standards. Otherwise, it would have stayed only in the realm of beliefs and notions.
Asness and team re-used the Fama and French methodology for devising stock factors and defined the QMJ portfolio as a portfolio of going long the highest-quality stocks and shorting the lower-quality stocks, hence the name Quality-minus-Junk.
Quality was defined as a combination of the following four parameters:
- Profitability. Profitability is the profits per unit of book value. All else equal, more profitable companies should command a higher stock price. We measure profits in several ways, including gross profits, margins, earnings, accruals, and cash flows, and focus on each stock’s average rank across these metrics.
- Growth. Investors should also pay a higher price for stocks with growing profits. We measure growth as the prior five-year growth in each of our profitability measures.
- Safety. Investors should also pay, all-else-equal, a higher price for a stock with a lower required return, that is, a safer stock. What should enter into required return is still a very contentious part of the literature. We do not attempt to resolve those issues here; rather, we take a simple, common-sense approach. We consider both return-based measures of safety (e.g., market beta and volatility) and fundamental-based measures of safety (e.g., stocks with low leverage, low volatility of profitability, and low credit risk).
- Payout. The payout ratio is the fraction of profits paid out to shareholders. This characteristic is determined by management and can be seen as a measure of shareholder friendliness. Management’s agency problems are diminished if free cash flows are reduced through higher dividends (Jensen (1986)). We also consider both net payout as well as issuance (dilution). Payout is an example of how each of these measures is about their marginal effect, all else being equal. Indeed, if a higher payout is associated with lower future profitability or growth, then this should not command a higher price, but a higher payout should be positive when we hold all other factors constant.
Not surprisingly, Asness and team indeed showed that Quality has merit, as they described in their academic paper Quality Minus Junk. Buying higher-quality stocks deliver higher returns than buying lower-quality stocks; all other factors being equal.
The latter part of the sentence is crucial, as we shall see later in this article.
But for now, let’s examine Asness et al.’s results:
The table represents the monthly statistics of 10 portfolios between the years 1956-2012. Each portfolio consists of 10% of the stocks in the universe, sorted by Quality. Portfolio P10 contains stocks with the Highest Quality. Portfolio P9 contains stocks with lower Quality than in P10 but higher than in P8 through P1. And so on until P1, which is the portfolio with the lowest-quality stocks. Excess Returns represents the average monthly excess returns compared to a portfolio containing all the stocks in the universe.
It is evident that the higher the Quality, the better the returns are. This is true also on a risk-adjusted basis, as measured by the Sharpe ratio. Interestingly, the higher the Quality, the lower the Beta, which can serve here as an indication for the portfolios’ volatility.
Another important discovery that the folks at AQR have brought to the world is a better understanding of the relations between small size and Quality.
It is widely known that, on average, small caps perform better than large caps. This phenomenon also has a Smart Beta Factor:
SMB = Small-minus-Big.
But there has been a growing base of research, and also evidence of practitioners, that this rule doesn’t always work well.
Asness et al. describe it in their paper Size Matters, If You Control Your Junk:
“First, many papers find that the size effect is simply not very significant, producing only a small abnormal return and Sharpe ratio, with marginal statistical significance. Second, others have argued that the size effect has disappeared since the early 1980s when it was originally discovered and published (partly contributing to its overall weak effect). Dichev (1998), Chan, Karceski, and Lakonishok (2000), Horowitz, Loughran, and Savin (2000), Amihud (2002), and Van Dijk (2011) find that small firms do not outperform big firms during the 1980s and 1990s, rendering the small firm premium obsolete. Schwert (2003) suggests that the small-firm anomaly disappeared shortly after the initial publication of the papers that discovered it and coinciding with an explosion of small cap-based funds and indices. Gompers and Metrick (2001) argue that institutional investors’ demand for large stocks in the 1980s and 1990s increased the prices of large companies relative to small companies that accounts for a large part of the size premium’s disappearance over this period. More recently, Israel and Moskowitz (2013), McLean and Pontiff (2013), and Chordia, Subrahmanyam, and Tong (2014) examine the attenuation of a host of anomalies, including size, following original publication, declines in trading costs, and increases in active money management. Collectively, the results indicate a decrease in the returns to size, though the evidence of reduction is statistically weak.”
The researchers have discovered a potential reason why small-cap portfolios did not always outperform large-caps. The variance in Quality among small caps is huge, much larger than in large caps. Small caps, and especially micro-caps and nano-caps, range from pieces of useless junk to excellent, stable, profitable and growing companies. This wide mixture of companies messes up the results.
But if you control the quality factor, meaning that you compare small caps to large caps with constant Quality, small caps clearly outperform large caps.
The Problem With Quality
The reader may think now, Well, Quality has proven to be an important stock factor. Given all other parameters kept constant, higher-quality stocks earn better returns than lower-quality stocks. This proves that Buffett was correct in his meme, and I should favor high-quality companies.
Not so fast.
The key part in the claim above was “all other parameters kept constant.”
The problem with Quality is that it is very easy to spot, by even the most novice of investors. Growth, either in revenue or profits, is one of the first things which investors look for when reviewing a stock. Financial stability can also be easily calculated. So is the payout and profitability metrics. The relative ease of detecting Quality (even if quantitatively) results in bidding up the price of higher-quality stocks. The higher the price, the lower the opportunity for abnormal returns. If the stock price exceeds the fair economic value, investing in high-quality stocks may result in below-average returns.
It is safe to say that most investors, even the professional and experienced ones, do not possess any edge investing in high-quality stocks. Maybe Buffett and Munger can still do it well, but most of us can’t.
To be successful, Quality must always be coupled with Low Valuation.
How Valuation and Quality Interact
Value Investing strategies that have no component of Quality whatsoever do well. Such are the Deep Value strategies in which stocks are chosen based on valuation per-se (whether it is EV/EBIT, P/E, P/Sales, or a composite of valuation metrics). Look no further than the historical returns of the Deep Value strategies on our Quant Investing website. Additional examples of strategies lacking any element of Quality are Net-nets and Negative EV stocks.
The premise of Zero-Quality strategies is two-fold. One, struggling companies are under-rated. Pessimism and avoidance lead their stock prices to be cheaper than their fair economic value. Often times, those companies can be turned around, sold, or liquidated for more than they are selling for. Secondly, the probability of reverting to the mean (that is, to performing on par with the industry mean) is statistically higher than the probability of continue failing. When a company is struggling, and its stock price is depressed, there are huge forces acting to right the ship, from its current management and stockholders to active investors and potential buyers. Ample diversification will protect the buyer from stock-specific risk, while a group of such stock is likely to perform better than the market averages.
Besides Deep Value, Net-nets, and Negative EV, other Value strategies all have a component of Quality built into them. The differences are in their implementation.
The Magic Formula averages between Valuation and Quality. Both factors have a similar weight. Stocks are sorted and ranked for Quality (Based on Return on Capital). Then, independently, they are sorted and ranked for Valuation (based on EV/EBIT). The ranks for Valuation and Quality are then averaged, and the stocks are sorted again based on the average rank. The Magic Formula works well, and delivers market-beating returns, but…ranking and sorting solely by Valuation performs even better. Interested readers are referred to the website to learn more.
As it turns out, the Quality component only hurts the Magic Formula.
And why is that? Probably because Quality is easily spotted, and priced (often over-priced) into the price of the assets. It’s the same problem of Quality described in the previous section.
Quantitative Value combines Valuation and Quality in a different fashion. Stocks are first filtered based on Valuation. Therefore, Valuation gets clear precedence. In our implementation, only the cheapest 25% of stocks are kept, and the 75% not-cheapest stocks are discarded. Only then, we bring Quality back into the picture and rank and sort the stocks based on Quality factors. The results, needless to say, are far better than the Magic Formula and also better than the Deep Value strategies. Our conclusion is that as long as Valuation is our first priority seasoning, a strategy with a bit of Quality factor – improves performance.
Even Graham Defensive Investor strategy, which we showcase on our website, combines Valuation and Quality and in its own unique way. Stocks are filtered based on strict Valuation and Quality criteria. On the Valuation side, our implementation requires that the P/E is lower than 15, or that the P/E * P/B is lower than 30. Thus, the strategy avoids pricey stocks, regardless of the outcome will be that there are 60 stocks at a given time or 0. On the Quality side, our implementation requires stocks to present profitability and positive dividends for at least five years straight. The dividend acts as a proxy for Quality – a company that has proceeds to distribute year over year is probably a stable one. There are additional rules for financial stability. Graham’s strategy makes above-market returns with low risk.
Quality: Before and After
Since data is worth much more than a thousand words (or three thousand or so in our case), let’s examine our top strategies, before and after adding to them a Quality component.
We shall start with presenting the backtesting results of our beloved Quantitative Value (QV) strategy, sorted only by Valuation (VC2 ranking system), without any Quality criteria:
The returns jump more than 400 basis points per year and the Sharpe improvs from 0.60 to 1.26.
Recall our discussion on Asness above, and how he and his AQR researchers have found that The Quality factor is ultra important when investing in microcaps.
Here is QV performance on a universe of only microcaps, from roughly $50M to $300M market cap:
And now with Quality (sorted based on a Quality rank, as above):
Returns jumped more than 7% per year, and the Sharpe ratio has more than doubled.
One of the most known ways to assess the Quality of stocks is by calculating the Piotroski’s F-score.
The score is calculated based on nine criteria divided into three groups (based on Wikipedia):
- Return on Assets (1 point if it is positive in the current year, 0 otherwise);
- Operating Cash Flow (1 point if it is positive in the current year, 0 otherwise);
- Change in Return of Assets (ROA) (1 point if ROA is higher in the current year compared to the previous one, 0 otherwise);
- Accruals (1 point if Operating Cash Flow/Total Assets is higher than ROA in the current year, 0 otherwise);
Leverage, Liquidity, and Source of Funds
- Change in Leverage (long-term) ratio (1 point if the ratio is lower this year compared to the previous one, 0 otherwise);
- Change in Current ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
- Change in the number of shares (1 point if no new shares were issued during the last year);
- Change in Gross Margin (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
- Change in Asset Turnover ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
- Some adjustments that were done in the calculation of the required financial ratios are discussed in the original paper.
The higher – the better.
Wes Gray and Tobias Carlisle suggested improvements, in their seminal book Quantitative Value. They named it the FS_Score.
Here are its components:
- FS_ROA = ‘1’ if the ROA is positive for the past year, ‘0’ if otherwise
- FS_FCFTA = ‘1’ if the Free Cash Flow divided by total assets for the past year is positive, ‘0’ if otherwise
- FS_ACCRUAL = ‘1’ if the Free Cash Flow minus Net Earnings in the past year is positive, ‘0’ if otherwise
- FS_LEVER = ‘1’ if Debt/Assets ratio has decreased during the past year, ‘0’ if otherwise
- FS_LIQUID = ‘1’ if the current ratio has improved in the past year, ‘0’ if otherwise
- FS_NEQISS = ‘1’ if the equity repurchases exceeded equity issuance in the past year, ‘0’ if otherwise
- F_dROA = ‘1’ if the ROA in the last year is greater than in the past year, ‘0’ if otherwise
- F_dFCFTA = ‘1’ if the Free Cash Flow divided by Total Asset has increased in the past year, ‘0’ if otherwise
- FS_dMargin = ‘1’ if the Gross Margin rate increased during the past year, ‘0’ if otherwise
- FS_dTURN = ‘1’ if Asset Turnover has increased during the past year, ‘0’ if otherwise.
We wanted to examine the effect of the FS_Score on a group of prominent stocks. We started with the S&P 500 universe, and picked 30 stocks, sorted by the Value Composite 2 ranking system (VC2), and rebalanced every year. We tested a period of 19 years, from June 30th, 1999, to June 30th, 2018.
|FS_Score||No. of stocks in the universe||Total Return (19 years)||Annual Return||Sharpe||Std. Dev.||Max DD|
|>=9||21||Not Calculated||Not Calculated||Not Calculated||Not Calculated||Not Calculated|
|=10||7||Not Calculated||Not Calculated||Not Calculated||Not Calculated||Not Calculated|
To our surprise, the FS_Score helped us only marginally. It did reduce volatility and max drawdowns as expected, and thus improved the Sharpe Ratio, but only by a small amount. We believe that the reason is that the S&P 500 constituents already inherently possess a large degree of Quality and that the Quality is fairly priced, hence only minimal gain can be achieved by selecting Quality among S&P 500 stocks.
Here are the stocks with a perfect FS_Score=10 score on the S&P 500:
And similarly, the stocks with a perfect FS_Score=10 score on the Russell 3000:
Quality is an important attribute in investing. All other parameters are equal, Quality should be sought after. Nevertheless, the evidence is clear:
Low Valuation takes precedence.
First and foremost, the evidence shows that investing in stocks with a low valuation delivers abnormal returns, on average, and over time.
Only then should Quality be considered. In some strategies, such as Quantitative Value and Graham’s Defensive strategy, and even Net-nets and Negative EV – higher-quality stocks perform better than their low-quality counterparts.
In other strategies, such as The Magic Formula, Quality impairs results.
And one more thing. Don’t take any meme or claim on face value, even if it comes from mighty Buffett.
Always ask what the assumptions behind the meme or claim are.
For us, individual investors, with a small capital and a long investing horizon, without any boss or client, examining our performance on a quarter-over-quarter basis:
“It’s far better to buy a fair company at a wonderful price than a wonderful company at a fair price.”
— Tal Davidson.