Evaluating Designer Models: Quality

PDF Version: Choosing Portfolio123 Designer Models Topic B3 – Quality

Quality is probably the least discussed least well-understood factor among members of the investing public. But in terms of how informative it can be for you, its way up there. Not only can it help you assess prospects for future return, it may also be the single best indicator of risk that is available.

If you want a quick shorthand definition of quality, think of Return on Assets, which is net income divided by assets. Eliminating the jargon, we could say it’s how much money a company earns relative to the money with which it is able to work, or what one earns relative to what one starts with. We’d say a business that can earn use $100 of capital to generate $10 in profits is better than a business that earns only $5 using the same $100 capital pool.

The Logical Foundation for Quality as an Investment Factor

Reference to earnings brings to mind the logical foundation of stock valuation, our old friend, the Dividend Discount Model (DDM). From that, we came to understand that the ideal price of a stock is equal to the present value of all dividends expected to be received in the future. We also know that DDM is not practically usable in the real world and that one of the proxies we use to point us in the direction of good DDM valuations is earnings (since dividends come from earnings).

Burt there are special features here that make Quality an especially valuable part of our stylistic framework.

Quality, ROA, does not measure earnings or earnings growth per se. It’s an indication of a company’s potential to generate earnings growth. Since investing is a future-oriented act, measuring potential is, actually, a very important thing. Potential is more persistent over time than the one-off picture of profitability presented by the Income Statement.

How Quality Does Its Thing

Table 1 demonstrates the link between ROA and earnings growth. It traces the earnings path of two companies, both of which pay no dividends. Company A has an ROA of 12%. Company B has an ROA of 16%.

Table 1 Illustration of Relationship between ROA and Growth

	Company A			Company B
ROA	12.0%			16.0%
Div. Payout	– –			– –
	Assets	Earnings	Dividends	Assets	Earnings	Dividends
Start	100.00	– –	– –	100.00	– –	– –
End Year 1	112.00	12.00	0.00	116.00	16.00	0.00
End Year 2	125.44	13.44	0.00	134.56	18.56	0.00
End Year 3	140.49	15.05	0.00	156.09	21.53	0.00
End Year 4	157.35	16.86	0.00	181.06	24.97	0.00
3 Year Growth Rate		12.0%	– –		16.0%	– –

 In Year 1, Company A earned 12% on its assets, which amounted to $12.00. All of that was added to the original capital base, which at the start of Year 2 is $112. In the second year, it earns 12% of 112, or $13.44—all of which is added to the base of capital available for next year. We follow this path on form one year to another.

Company B charts a parallel path but one that reflects a 16% ROA (each year it earns 16% on its capital, and all of that is added to the capital base.

All else being equal, Company B must grow more quickly than Company A because it in each year, it earns a higher percent relative to a capital base that expands more quickly through reinvestment of profits.

Table 2 varies the scenario. Now, each company pays out 20% of annual profits as dividends. That reduces the earnings growth rate in both cases because now, each company is expanding its capital base by less than the full amount of profit. Even so, all else being equal, Company B must still outgrow Company A.

Table 2 Another Illustration of Relationship between ROA and Growth

	Company A			Company B
ROA	12.0%			16.0%
Div. Payout	20.0%			20.0%
	Assets	Earnings	Dividends	Assets	Earnings	Dividends
Start	100.00	– –	– –	100.00	– –	– –
End Year 1	109.60	12.00	2.40	112.80	16.00	3.20
End Year 2	120.12	13.15	2.69	127.24	18.05	3.61
End Year 3	131.65	14.41	2.88	143.52	20.35	4.07
End Year 4	144.29	15.80	3.16	161.89	22.96	4.59
3 Year Growth Rate		9.60%	9.60%		12.78%	12.78%

 Each company has a growth rate that is less than its ROA. But that makes sense. Each company is now increasing its asset base each year by less than the full amount that is available. Notice, too, that for each company, all else being equal, dividends grow by an amount that matches the earnings growth rate and hence is higher for Company B (16% ROA) than Company B (12% ROA).

So what about the ubiquitous “all else” that must be equal in order for all this to make sense. That’s easy, at least easy to say: The ROA must remain steady. Company B, despite a day one 12% ROA, could be a better choice than Company B if the latter’s ROA is expected to deterioratefrom 16%. Could either of those scenarios ever materialize? Yes, absolutely. In the real world, the only constant is change. When strategists using ROA develop their strategies, it’s up to them to figure out how to use the available data to uncover clues relevant to ROA growth or deterioration. The degree of skill they bring to bear in this will contribute the real-world success or lack thereof of their strategies.

Is This What is Popularly Referred to as Fundamental Analysis?

Yes, it is.

Similar to what people in many analytic professions do in many contexts, we assess a whole by decomposing it into its essential parts based on the expectation that the parts are easier to analyze than the whole. Essentially, ROA is a combination of margin and turnover. And if we define the total capital base in terms of Equity (ROE) rather than total assets, than debt leverage also figures into the framework. For further information, you can Google the phrase “DuPont Framework.” It also encompasses the increasingly important field of earnings quality.

This Kind of Fundamental Analysis Starts With a Built-In Edge

All investment-oriented analysis is inherently difficult because we’re limited to using data from the known past to develop reasonable assumptions about the unknowable future.

But however hard that may be – and it really is hard – it’s a lot harder if the data we use tends to be unstable over time. The flip side is that however difficult the task may be, it can become a lot less so if we work with data that tends to be more stable over time, or in quant lingo, tends to be more “persistent.”

As investment-related data goes, ROA (and the variations like ROE that reflect differing definitions of the capital base) tends to be a lot more persistent than many other items. While past performance (of corporations as well as portfolios) can never determine future outcomes, when it comes to ROA, past performance does, at least suggest greater probabilities about future outcomes.

Like other metrics, ROA is not exactly stable. What’s more persistent is ROA trend. (If I remember calculus correctly, it would be more accurate to say the first and second derivatives of ROA, rather than ROA itself, is what’s persistent.) The general expectations are (1) that, on the whole, very high and very low ROAs will revert gradually toward normal levels (often reflecting industry norms) and (2) that high-return companies will tend to remain high-return companies and that low-return companies will remain low-return companies.

These tendencies are illustrated in Table 3, which shows the results of a study on companies sorted by 2005 Return on Assets. We focus on three cohorts:

1. A high cohort of companies whose 2005 ROAs were at least in the 90th (best) decile,
2. A middle cohort of companies whose 2005 ROAs were in the 45th through 55th percentile,
3. And a low cohort of companies whose 2005 ROAs were at most in the 10th (worst) decile.

The memberships of each cohort remained constant through 2015 (although there was shrinkage over time due to bankruptcies, mergers, and so forth).

The table shows the median ROA each year for each cohort.

Table 3 ROA Persistence  

	Returns on Assets (%)*
Cohort	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
High	17.32	15.31	13.76	12.43	11.23	7.09	10.60	9.83	9.84	8.99	8.51
Mid	3.43	3.48	3.71	3.08	3.08	1.67	3.15	3.35	3.36	3.07	3.27
Low	-35.92	-13.63	-9.53	-3.97	-3.97	-6.02	-1.65	0.04	0.07	-1.37	-0.92

 *Median Values. As of January 1^stof each year

As expected, we see a tendency for extremes to correct and trend toward more normal levels. Beyond that, the persistence is noteworthy. Consider, for example, 2005’s high and low cohorts. While in each case the magnitude of returns became less extreme over time, both cohorts retained their relative positions throughout the entire ten-year span. The middle cohort likewise retained its relative position and exhibited remarkable stability over time, as we might expect given the absence of extremes from which it would have had to revert. Interestingly, even in the off year, 2010, the cohorts retained their relative positions.

ROA as a Strategic Tool, Not a Silver Bullet

Would that life would be so simple as to allow us to simply sort all companies of the basis of ROA, invest in the top few, and be done with it. The problem we have is that the world changes, and by more so than the sample medians shown in Table 3 imply. On the whole ROAs trend as shown. But individual situations vary considerably.

So we don’t look for high ROAs per se. We look for good ROAs that have the potential to remain good or improve over time. And that’s what gets us into the other aspects of fundamental analysis; i.e., the DuPont framework mentioned above. High ROA is easy to identify. The hard work is in teasing out ROA stability, improvement or deterioration. Taken together, all this comes under the stylistic label Quality.

Quality as a Risk Indicator

This would be a good time to introduce the topic of fundamental risk.

Although it’s not perceived this way now, the worst thing done by quants (even the most highly respected of quants) has been to convince so many that we should measure risk in terms of data based on historic stock returns. Their handiwork leads us to exalt Beta, Standard Deviation, Skewness, Value at Risk, the Sharpe Ratio, the Sortino Ratio, and others.

None of those measures tells us anything about risk. They are merely statistical report cards that tell us what happened in specific past time intervals.

Imagine Stock A with a Beta of 0.75, Stock B with a Beta of 1.65, and Stock C with a Beta of -0.50. There are Nobel Prize winners and Nobel Prize aspirants who will tell you that C is a must-own; its negative Beta indicates it moves contrary to the market, thus making it invaluable as a holding that will control risk in a diversified portfolio. A is also pretty good, being 50% less volatile than the market. Meanwhile, B looks a bit dicey being, as Beta shows, 65% more volatility than the market.

It’s possible that all of those conclusions may be accurate. But it is equally possible that all of those conclusions may be dangerously wrong. Beta sheds no light one way or the other.

• C may be a frighteningly volatile stock because its earnings are completely unpredictable, and so, too, are shifts in market perceptions about its future; hence volatile earnings and volatile PE (caused by erratic sentiment). You’d think C would have a very high Beta. But what happens if the timing of very bad news that causes the stock to plummet just so happens to be coincident with some good economic news that causes the market to rally? Because the stock moved opposite the market, a beta that might have been 2.50-3.00 winds up at -0.50, not because the company or the stock are less risky but based on the fortuitous of the timing of news.
• B jumped way ahead of the market as it rallied on good news that transforms the firm into something better and more consistent than has ever been the case. Business risk has been sharply reduced. That will reflect in future earnings. And as the market digests this, the PE will stabilize too. In the future, the beta will likely come in at around 0.50. But that’s not what we’re seeing. The 1.65 Beta is reflecting some upside market volatility as the company’s better business profile worked its way into PE and EPS.
• As to A, oh who cares! You’ve already seen how Betas for B and C, although correctly calculated from a mathematical standpoint can give us messages that are 180-degrees opposite form the messages we should be getting.

 

How might we evaluate the risk of A based on what we know now? It’s OK to equate risk with volatility (so long as we’re not so fanatic that we forget upside volatility is a good thing). What we really care about is future volatility, not historic volatility. Predicting future stock prices is a treacherous task. But perhaps we can instead try to get a handle on future company business performance and EPS. We learned that high and stable ROAs will likely translate to better earnings, and although we can’t be sure about PEs, investors have done crazier things than assume that good steady earnings will translate to good PEs.

So why do we waste our time measuring risk using data (historical share prices) that has no forward-looking relevance. Why not measure it based on fundamentals that have the logical capability of influencing stock prices in the future. On that basis, I suggest to you that the Designer Models Quality ranks are the single best measure of risk available to you today. What makes Quality (ROA and its variations and components) important is that while we can never assume past performance dictates the future, this set of metrics has a higher probability of being persistent than do most others. That makes it incredibly valuable.

The Designer Models Quality Rank

The Quality Rank we use in Designer Models is a Portfolio123 ranking system created on this basis from factors widely assumed in the financial community to be relevant.

• One set of factors relates directly to ROA. While we don’t use the classic definition of ROA, we use several variants (Return on Investment, wherein capital is based on equity plus long-term liabilities; gross profits to asset; and free cash flow to assets)
• Another set of factors measures stability of key financial streams, sales and operating profit
• Another set of factors measures debt leverage (greater use of debt entails greater fixed costs for interest and, hence, more volatility on the bottom line)
• We use the well-known Piotroski F-Score factors (see Reference in Help section for more details) that were designed to show which presumably risky stocks (low price/book) where least likely to falter as value traps
• Finally, we use the Beneish M-Score (see Reference in Help section for more details) a measure of earnings quality. Contrary to what many would have you suppose, investors don’t necessarily care about the ethics of accounting per se. What they do care about is persistence and gimmicky accounting offends because it makes earnings less persistent. (But it’s still nice to pretend it’s a moral thing!)

We Don’t Have To Be Preachy About Quality

Don’t get the idea from this that it’s bad to choose Designer Models models with low Quality scores. Quality is one end of a scale, the opposite end being “Junk,” not in a pejorative sense of the word but in terms of jargon.

You can make a lot of money on the junk side of the scale and many do just that all over the markets. What’s important is that you make knowledgeable decisions about what you want to do with this Style.

Using Quality as a Tool to Evaluate and Choose Models

Perhaps the single biggest difference between Quality and Junk is in the area of fundamental risk; persistence, stability, etc. If you are bullish on the market, you want volatility, instability. High Quality scores may suppress your returns. And considering how the market has been during the period covered by Portfolio123’s database, when we’ve had many more good periods than bad, strategy designers who sought to present very high simulated returns probably wound up cutting down on Quality (whether they intended to so, or whether it just so happened to have turned out that way as they used empirical methods to guide them to factors most associated with very high past returns). That’s fine as long as markets continue to rise. As noted, in good times, you want to maximize volatility.

If, however, you are cautious about the market, high simulated results coupled with relatively low Quality style scores may be something to avoid.

However you feel about the markets, whatever your tolerance for risk, whichever way you want to go, the most crucial thing is to make sure the models to which you subscribe have Quality style ranks consistent with your outlook. In this regard, the Quality scores should trump all other risk metrics published by Designer Models or by strategy designers in their descriptions.

Next Up . . .

Momentum! That’s the style many don’t realize is legit. And it’s the style many are actually pursuing even if they don’t realize it, or to a much greater extent than they realize.