For Better Valuations, Avoid These Five Behavioral Mistakes
Benjamin Graham, in his seminal text on value investing, The Intelligent Investor, observed, “The investor’s chief problem — and even his worst enemy — is likely to be himself.” Graham was speaking of the importance of emotional discipline when it comes to investing, and it was this intersection of psychology and economics that subsequently became the focus of much research and formed the basis of behavioral finance.
In 1979, with the publication of “Prospect Theory: An Analysis of Decision under Risk,” Daniel Kahneman and Amos Tversky were among the first to consider how cognitive biases lead investors to act irrationally. Today, the influence of these three investing pioneers is clearly evident in the work of investment strategist Michael Mauboussin.
Mauboussin recently spoke at the CFA Institute Equity Research and Valuation Conference, where he posited that investors could generate more accurate valuations and improve their investment decision making by avoiding the following five behavioral pitfalls.
- Failing to incorporate base rates: When making a forecast, analysts often take what’s referred to as an “inside view.” That is, they over-rely on their own personal experience and intuition, and neglect the “outside view,” or base rate, that considers a larger reference class or sample size. In Thinking, Fast and Slow, Kahneman wrote, “People who have information about an individual case rarely feel the need to know the statistics of the class to which the case belongs.” In practice, the best valuations consider both the inside and outside views. Mauboussin pointed to Elon Musk’s bold forecast in 2015 that Tesla’s market value would reach $700 billion — equal to Apple’s at the time — over the next decade, in part by growing revenues by 50% per year. This was a classic example of an inside view. If Musk had instead taken an outside view and incorporated a known base rate, he would have considered the fact that no similar-sized public companies in US history went on to grow revenues at such a rate. In fact, Mauboussin found only six firms that managed to grow their sales between 30% and 35% over such a stretch. While it’s possible Tesla could defy the odds and deliver on Musk’s projection, investors would be wise to recognize it as salesmanship and discount it accordingly. In short, incorporating base rates in forecasts serves as an effective reality check and leads to more accurate valuations. The Base Rate Book, co-authored by Mauboussin, has more details.
- Ignoring reversion to the mean: The concept of reversion to the mean — that an outcome that is far from the average will be followed by an outcome that is closer to the average — is poorly understood by investors and is well illustrated by their propensity to buy high and sell low. In The Success Equation, Mauboussin explains that when outcomes from period to period aren’t perfectly correlated, they’ll revert to the mean, and the rate of reversion is a function of the relative contribution of luck to the outcomes. High correlations generally indicate that skill plays a significant role in outcomes. In such cases, reversion to the mean is relatively slow, allowing for more accurate forecasts. Conversely, low correlations typically mean that luck plays a greater role in determining outcomes, and reversion to the mean will be relatively quick, resulting in less accurate forecasts. When correlations are low — like with the stock market’s year-to-year performance — investors need to rely heavily on the mean, or outside view, in making a forecast. In sum, investors can sharpen their forecasts by carefully considering where the activity falls on the luck-skill continuum and incorporating reversion to the mean in their decision making.
- Being overconfident: Overconfidence manifests itself in a variety of ways when it comes to investing. It includes overestimation, or a belief that we can do things better than we actually can; overplacement, when we’re convinced that we’re better than average; and overprecision, when we think we understand things better than we really do. Mauboussin developed an online test to measure overconfidence that over 11,000 people have taken. The test is composed of a series of true or false questions that also ask participants to estimate a probability of correctness for each answer. Thus, it is a test of calibration that answers the question, “How much do you know what you know?” Perhaps not surprisingly, the test participants as a group were found to be overconfident. The average confidence level was 70% and on average 60% of the questions were answered correctly. Interestingly, the results showed a gender bias: Men displayed more overconfidence than women. Being well-calibrated is important in investing because it shows up in our conviction levels and impacts portfolio construction.
- Over-relying on multiples: Mauboussin noted that investors are often quick to dismiss the discounted cash-flow (DCF) model because of the number of assumptions required, and yet they embrace a multiple approach that is equally reliant on assumptions. The P/E multiple, in particular, is widely used yet poorly understood by analysts, and as a result, is often misapplied, according to Mauboussin. “Multiples are simply shorthand for the valuation process,” he said. “You can’t get valuation right without understanding the economics of the business.” P/E multiples are impacted by a host of factors, including interest rates, the business cycle, inflation expectations, returns on capital, and growth prospects, all of which can make historical comparisons problematic. The investment community also tends to favor earnings growth over all else, including returns on capital. That’s a mistake. Mauboussin noted that growth is great as long as a firm is earning above its cost of capital. But growth is wealth-destroying when a firm fails to earn its cost of capital. Fast-growing companies and investors also need to beware of the “grim reaper” of P/E multiples: Over time, a firm’s P/E multiple will drift towards the commodity multiple.
- Making faulty comparisons: Analysts are quick to say, “Look how cheap this stock is relative to my comps!” The problem with this approach, according to Mauboussin, is that chosen peer groups are often cherry-picked to help justify a recommendation or unnecessarily constrained by industry. Rather than comparing companies based solely on their industry classification, analysts should instead find companies with similar returns on capital and growth prospects. And because all cases in this reference class won’t be equally informative, investors should assign more weight to those that are most similar. So-called similarity-based forecasting has been found to result in more accurate forecasts and improved investment decision-making.
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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
Image courtesy of Paul McCaffrey