Tag Archives: business
Why PE Ratios Are Not A Good Measure Of Value
Summary PE ratios are commonly used as a metric to determine “value”. However, PE ratios are unreliable for a number of reasons and earnings actually have no correlation with valuations. Return on invested capital is a better measure of value and has significant correlation with valuation. We’ve pointed out the flaws in the price to earnings (PE) ratio many times before. Chief among these flaws is the fact that the accounting earnings used in the ratio are unreliable for many reasons: Accounting rules can change, shifting reported earnings without any real change in the underlying business. The large number of accounting loopholes makes it easy for executives to mislead investors. PE ratios overlook assets and liabilities that have a material impact on valuation. It should come as no surprise that empirical research shows accounting earnings have almost no impact on long-term valuations. No Correlation Between Earnings And Value If accounting earnings actually drove valuations, then companies with high EPS growth should command higher multiples, and companies with low or negative EPS growth should have lower PE multiples. As Figure 1 shows, this correlation is nearly nonexistent. Figure 1: EPS Growth Has Almost No Impact On Valuation (click to enlarge) Sources: New Constructs, LLC and company filings. The r-squared value of 0.0006 in Figure 1 shows that EPS growth over the past five years explains less than one tenth of one percent of the difference in price between stocks in the S&P 500. Stocks can see their PE multiples expand and contract in a manner that has almost nothing to do with changes in EPS, which makes looking at these metrics a poor indicator of valuation or future returns. The Market Cares More About ROIC Many other studies have found the same lack of correlation between earnings growth and stock price. Instead, we find that valuations tend to be driven largely by return on invested capital ( ROIC ). Figure 2 shows that ROIC is highly correlated with Enterprise Value/Invested Capital (a cleaner version of price to book). Figure 2: ROIC Is The Primary Driver Of Stock Price (click to enlarge) Sources: New Constructs, LLC and company filings. ROIC explains nearly two thirds of the difference in valuations between various companies. That means companies that can improve their ROIC are more likely to grow their stock price in the market. Short Term Vs. Long Term Drivers “But wait!” you might be saying. “I know accounting earnings have an impact on valuations. I’ve seen stock prices rise and fall dramatically based on a company’s quarterly earnings report.” This is true. It’s clear that headline numbers can have an immediate and sometimes dramatic influence on stock prices. The key word in that sentence is “immediate”. A big increase in EPS might drive short-term gains in stock prices, but it won’t create long-term value. To understand the cause of this divergence, you have to understand the different types of investors in the market. Brian Bushee from the Wharton School of Business wrote an excellent paper back in 2005 that highlighted the behavioral differences among institutional investors. His research found that: 61% of institutional investors are “Quasi-Indexers”. They hold many small stakes with low turnover, so they have little impact on market valuations. 31% of institutional investors are “Transients”. They have small stakes but a high turnover, so their high volume of trading can impact valuations in the short term. 8% of institutional investors are “Dedicated”. They take large stakes and hold them for a very long time. These are the investors that drive long-term valuations. A big earnings beat might cause a lot of “Transient” investors to buy that stock, pushing up the price, but most of these investors will sell their stakes not long after, pushing the price back down. They can create spikes, but their impact on the long-term performance of the stock is next to nothing. Instead, it’s that small percentage of “Dedicated” investors that are responsible for the majority of long-term performance. These are highly sophisticated individuals that take a long time evaluating stocks before taking large positions that they hold through bouts of volatility. Why You Have To Look At The Balance Sheet And Cost Of Capital The central flaw of the PE ratio holds true for many of the other common ratios such as: Enterprise Value/EBITDA Price to Earnings Growth (PEG) Price to Operating Cash Flow Price to Sales All of these ratios ignore the cost of the capital that the company uses to drive profits. To understand why cost of capital is so important, imagine this hypothetical scenario: you have an infinitely wealthy investor who is willing to offer you an unlimited source of equity capital. You take the money from this investor and put it in a low-yielding savings account. The more money you take from this investor, the more your interest payments, or “earnings”, will grow, but you’re not actually creating any value. In fact, by earning such a low return on that money compared to what they could earn elsewhere, you’ve actually destroyed value. The use of these flawed metrics perpetuates the irrelevant distinction between growth and value investing . Earnings growth without an ROIC above the weighted average cost of capital ( WACC ) destroys value, and value without growth limits upside. While ROIC is, by far, the most important driver of value, it is not the only factor. One must also consider revenue growth and duration of profit growth, i.e. growth appreciation period ( GAP ). These three drivers comprise everything that defines the profitability and, therefore value, of a company. PE and PEG are driven by these drivers, not the other way around. The same concept applies to companies that grow EPS by deploying capital at suboptimal rates of return. As we discussed in ” The High-Low Fallacy “, an acquisition can be accretive to earnings but destructive to shareholder value. Recent Danger Zone pick Expedia (NASDAQ: EXPE ) has managed significant EPS growth through $3.2 billion in acquisitions, but these acquisitions have actually hurt the long-term interests of shareholders by earning an ROIC that falls short of WACC. For that reason, investors need to be looking at ROIC rather than EPS, and they need to recognize that a PE multiple tells you next to nothing about the actual value of a stock. Disclosure: David Trainer and Sam McBride receive no compensation to write about any specific stock, sector, style, or theme.
A (Partial) Solution For Narrative Risk: Probit Modeling
The search for objective analysis in the cause of making informed investment decisions is the Holy Grail of finance. Unfortunately, narrative risk continually threatens to derail us on our crucial quest for perspective. Everyone loves a good story, and it’s no different when it comes to finance and economics. The problem: there’s an excess of interesting narratives that too often are bereft of useful information. Genuine insight that’s earned by way of a clear-eyed review of the numbers, in other words, is the exception in a world that’s overflowing with story lines that appeal to emotion rather than intellect. Unfortunately, we’re bombarded with distraction. The self-proclaimed seer on TV who spins a good yarn about what’s really driving prices or the business cycle can draw a crowd by dispensing entertaining narratives about gloom and glory. Bigger is always better from a media vantage, even if the facts don’t easily fit the narrative. Meanwhile, a sober reading of the numbers is a yawn if you’re trying to maximize eyeballs pinned to the tube. Making reasonable decisions and producing compelling media content, in short, are often at cross purposes. What’s the solution? There are many paths to quantitative enlightenment, including the powerful analytics that arise from probit and logit regressions. There are no silver bullets here, but the ability to translate raw data into specific probability estimates for a particular condition offers a valuable resource for cutting through the noise. The flexibility and power that probit and logit models bring to the table suggest that running the numbers through these filters should be on everyone’s short list of analytical tools. As a simple review of how these models work, let’s run through the basics using R code as the quantitative lingua franca, although you could easily do the same in Python or even Excel. But first a warning: the illustration below is a toy example and not a very useful one as presented in terms of making real-world decisions. But the general outline is valid and so the process will offer a flavor of how to deploy this modeling framework. ( Here’s the R code to replicate the data discussed below.) Let’s say that you think that the VIX index, which tracks the implied volatility of the US stock market (S&P 500), offers useful information for monitoring and measuring equity risk. After eyeballing a chart of the two indexes (as shown below) you decide that a VIX reading above 20 is a warning sign for the market. But how threatening is a 20-plus reading? One way to quantify the danger is by analyzing the S&P 500 in context with the VIX by way of a probit model. The first step is creating a data set of binary signals that reflect your definition of “risk.” The sky’s the limit for customizing this definition, but in the interests of keeping this short review from becoming a consulting project let’s use one simple metric: rolling one-year return for the S&P 500. The research agenda is estimating the probability of a negative one-year return based on the current VIX reading. (Yes, this is a bit naïve not to mention superfluous, but it’s good enough to profile probit modeling.) With our research map in hand, it’s a simple matter of getting the data in shape. The first step is creating a set of binary signals to indicate the state of the market that we’re trying to model. Remember, a probit model is designed to estimate probabilities for one of two states, which is considerably easier and therefore more practical in the real world vs. trying to model a spectrum of conditions. In keeping with our simplistic example. any negative one-year return for the S&P is labeled as “1” and a positive return as “0”. The next step is instructing the probit model to estimate the probability that the S&P is in negative territory by way of analyzing the historical relationship between the VIX and the signal data as defined above. Right about now you’re probably complaining that we already know the state of S&P one-year return by looking at real-time market data without referring to the VIX. Agreed, and so creating a probit model to tell us what’s already obvious is a redundant exercise. True, at least in this case, but the point of all this is to outline a basic probit procedure. Keep in mind that a genuine effort in this corner would probably focus on modeling a state that’s unclear in real time, such as the start of a recession or some other aspect of market risk that’s not readily available. As for our toy example with the S&P, here’s the result of the probit model estimates for the probability that the S&P’s trailing return is below zero. Visual inspection suggests there’s some meat on this bone. The rising probability that eventually reached 100% in late-2008, for instance, tells us that there’s a relatively robust relationship between the S&P and the VIX. Well, of course there is! We already knew that. The probit model is simply quantifying the relationship per our specifications. The question is how or if such a model should be adjusted. Is modeling trailing 6-month return preferable to 1-year performance? Should we raise or lower the 20-plus VIX trigger? What about adding in additional variables – the 10-year Treasury yield, for instance. There’s a wide array of possibilities here, which is a benefit and a curse. A benefit because probit modeling (and its close cousin logit modeling) can be customized in an endless variety to extract estimates of a particular state from raw data. But that’s a curse if you’re unsure of how to proceed. In other words, doing preliminary research to map out a reasonable strategy is essential before you dive into the numbers. But with a bit of advance planning, deploying a probit model can offer deep insight into market and macro analysis. There are no guarantees, of course-probit models can lead us astray in some cases, particularly when we’re sloppy with assumptions about relevant variables. But compared with listening to someone’s interpretations of what the latest market moves suggest, probit modeling offers objective context without the baggage of behavioral biases. It’s not a complete solution to narrative risk, but it’s a good start.