When DuPont (DD) purchased a 23 percent stake in General Motors (GM) between 1917 and 1919, they tasked engineer F. Donaldson Brown with fixing the auto company’s messy finances in a bid to more accurately assess the value of the company.
In the process, Brown developed what would become known as the DuPont System. Many, including former GM Chairman Alfred Sloan, attribute much of GM’s ultimate success to Brown’s efforts. And Brown’s approach to valuation ultimately proved to have more life than the assets he was examining. While the Supreme Court ruled that DuPont’s stake in GM constituted a monopoly and forced them to sell it in 1957, by which time GM was the biggest car company in the country, Brown’s DuPont system proved the dominant method for valuing equities into the 1970s and remains fairly ubiquitous even today.
On a base level, it’s just a way of looking at a common fundamental metric: return on equity (ROE), a simple ratio achieved by dividing the company’s net income (NI) by its total shareholder equity (Eq). The DuPont method just breaks up that calculation into its three primary determinants.
However, this method’s simplicity is deceptive. It’s an approach that has proven valuable to analysts for almost a century, and few systems of financial analysis can boast of such long-standing relevance. In the end, Brown gave investors a great way to try and get the most bang for their buck, which seems appropriate coming from a company that started as a gunpowder mill in 1802.
Return on Equity (ROE)
The observation that companies getting a better ROE tended to have more value in the long run wasn’t exactly rocket science, even in the 1920s. The number is one that’s clearly indicative of strong fundamentals, and it has a high correlation with success for a company over time.
Shareholder equity is, in this case, the value of the company if it were to liquidate. That is, if you up and sold everything, what would it be worth? Net income, meanwhile, is just revenue less expenses (and there are, of course, a few different methods for subtracting expenses you can use, but this is one of the most basic).
What you get by comparing these two numbers in a simple ratio is how efficiently a company converts its resources (equity) into profits. A company that can use $10 million worth of resources to make $100 million in profits has something on a company that is making $50 million in profits with the same amount of underlying infrastructure.
Obviously, one can’t just compare ROEs in a vacuum. Industrial goods companies typically require a lot more equity to produce profits than, say, a software company. And a mega-cap company is likely going to have a very different ROE than a micro-cap company, even when they’re in the same industry.
However, once you start comparing ROEs between similarly sized companies in the same industry, the DuPont System starts to have a lot of value.
The DuPont System of Analysis
However, what Brown and DuPont noticed was that, while there was a strong correlation between ROE and a company’s ultimate value, it was hardly a perfect one. Plenty of companies would display a strong ROE and still fail to ultimately find the sort of success one would expect. And others would achieve success despite having a lower ROE than their competitors. There had to be more to this simple metric.
The DuPont system breaks up the ROE calculation into three pieces: profitability, asset turnover, and leverage. These three factors each proved to be very predictive in terms of a company’s future success and, when combined, reduced to a simple ROE calculation.
What’s more, the system was both statistically and economically significant. Statistically, Brown could examine historical economic data to show that there was a strong correlation between those three factors and long-term success. Economically, he could understand why those three factors contributed to a company’s success, and subsequently be confident the statistical correlation was not a coincidence.
Three Key Factors
Profitability was calculated by dividing net income by sales (S), giving you the company’s net margin. Essentially, how efficiently is the company converting money in into profits?
Asset turnover was calculated by dividing sales by total assets (A), where total assets is a combination of both equity (what the company owns) and liabilities (any debt the company might be carrying). This is another gauge of efficiency that compares how well the company is turning the resources at its disposal (total assets) into sales.
Finally, leverage is calculated by dividing total assets by equity. This essentially shows how much debt a company has in relation to its underlying value. If I have $50 in cash, and I borrow $100 from my friend Bill, I would have $150 in total assets (my cash plus Bill’s cash I’m using), $50 in equity (my cash), and leverage of 3 (total cash divided by my cash).
So, instead of a simple ROE calculation that looks like this:
ROE = NI/Eq
They started looking at it like this:
ROE=NI/S * S/A * A/Eq
Now, anyone who took high school algebra is probably looking at the above equation and thinking “What gives? That’s the same thing. The sales and assets cancel each other out and you’re left with the same net income over equity as the first one had. Isn’t this just a way to complicate a simple equation?”
To some degree, yes. But that’s sort of the point. See, what the DuPont System does is strip out individual pieces so that they can each be compared. Because while a strong ROE is ultimately good for a company, how it gets there is also important.
How Returns are Generated Matters
A company that’s borrowing heavily could generate very high leverage that, in turn, would allow it to display a much higher ROE number. However, another company that’s showing excellent margins might have a lower ROE than that first company but still represent a better investment. Not something one notices if you just do a simple ROE calculation.
What DuPont noticed by breaking the ROE calculation into three pieces like this was that, while profitability was always good, companies should ideally be at or near the industry average for asset turnover and leverage. In both cases, too high and too low tended to be a bad thing.
On some level, that might be confusing. Leverage, for instance, should be low, right? Less debt is good, isn’t it? The answer, surprisingly enough, is no. Too much debt is clearly bad, but using the resources at your disposal is good. And, if banks are willing to lend your company cash, you should take them up on it. It’s money that can be used to improve sales and/or profitability.
What’s more, the ability to borrow is, in and of itself, a sign of relative health. Banks and bond buyers won’t float your company a loan if they don’t think that you run a strong business, and the only other means for raising capital is through public offerings, which deplete the value of a stock. A company that can’t get a loan and keeps offering stock to pay for expansion will have low leverage, but they’re not a company you want to invest in. Likewise, a company with leverage much higher than its competitors is also probably a bad investment, only for more obvious reasons (it has too much debt).
Similarly, asset turnover isn’t about getting as high a ratio as possible. The right level of asset ratio is a sign of a healthy company, and getting that number close to industry average is a good sign. A high level of asset turnover could just be a sign that a company has too few of its assets for sale. And low asset turnover is usually a sign that the company isn’t efficiently turning assets into sales.
The Right Balance is Key
So, while having a better ROE than other similar companies is almost always a good thing from the perspective of an investor, understanding why that’s the case is still important. Getting there by being more profitable is almost always a really good sign. But getting there by having significantly higher leverage than your competition? Not so much. By examining these components independently, DuPont’s financial wizards got a more complete picture of each company.
And that’s ultimately the real selling point for the DuPont System: its dexterity as a tool for analysis. By taking a more complete picture, one can look deeper than just the underlying numbers. When these factors are less than ideal but trending in the right direction, it could mean a buying opportunity even though the initial analysis might say “stay away.”
Or, even if the analysis says the stock isn’t a strong buy, one might be able to find explanations in the marketplace, like a specific piece of news or a macro trend that’s about to change, for why one of the factors appears unfavorable, and buy anyway. Or you could feel that the separation from an industry average by a particular company is actually a positive because of the circumstances specific to that company and that industry.
The capacity to dig into the numbers for a more complete picture is what makes the DuPont system work. In fact, some analysts will add even more factors to the equation (which is easy to do, provided that they cancel out with the existing ones). While it’s a more complex picture, the basic methodology of breaking ROE into different components is the central concept that runs through it all.
Not a Perfect System, but One That’s Stood the Test of Time
However you cut it, the DuPont System can help an investor make better sense of those underlying numbers and make their own assessments. While far from perfect (repeat the mantra after me: “no system is ever perfect”), the longevity of the DuPont system as a method for analysis speaks to its value.
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