What drives stocks to be the number-one stock in the S&P 500 in terms of one-year returns is surprises.
An Interview with Martin Fridson
In your book, you criticize sell-side analysts’ recommendations for being overly weighted toward buys, with relatively few sell recommendations issued. These issues notwithstanding, is there any way individual investors can use analyst reports to make investing decisions?
The flaws are as you described. I think that the earnings estimates, price targets and recommendations can be useful inputs to stock selection for the greater part of your portfolio, but they are not good for picking the number-one-performing stock.
If you are trying to create a diversified portfolio of stocks that offers good long-run potential, you may want to consider earnings estimates and price targets as basic inputs as long as you can identify analysts who have demonstrated a consistent ability to make profitable recommendations. The most valuable input from Wall Street analysts may not be the earnings estimates or the price targets, but rather the qualitative insights analysts can provide on companies’ strategies, competitive environments and management quality.
Because they regularly talk to these companies, the analysts get to know them very well.
In terms of earnings estimates, you found greater levels of dispersion—the difference between the highest and lowest analyst estimates—associated with bigger stock price moves compared to when analysts mostly agree about the earnings outlook for a company, correct?
Yes, the dispersion among analysts’ earnings estimates turned out to be helpful. That’s why we introduced the Fridson-Lee statistic, which is calculated as the difference between the highest earnings estimate and the lowest earnings estimate divided by the lowest estimate.
The number-one S&P 500 index stocks often have high ratios—in some cases greater than 100%. I think the underlying reasoning and cause for the phenomenon is that shares of companies whose earnings potential is viewed uniformly by all the covering analysts won’t go up a lot if their earnings come in pretty much as expected. If there’s a wide difference of opinion about expected earnings, then whatever a company’s earnings are reported at, it will be a surprise to some analysts and, by extension, to some investors. On the positive side, there’s more potential for the earnings reported by these kinds of companies to be seen as a beat.
We should point out that higher dispersion means a greater possibility of a surprise occurring, but the surprises can be either positive or negative.
This raises the larger point that the stocks with the potential to be number one also have the potential to be way down the list and to have a very bad year. I don’t recommend seeking the best-performing stock in the S&P 500 for a large portion of your portfolio. It’s a relatively low-risk, low-cost way of channeling that speculative urge that many of us have. The kind of stocks that I write about in the book could go either way. Sometimes, when those stocks are not right near the top, they’re well down in the rankings and have some negative surprises.
For the purpose of identifying which S&P 500 stock is going to outperform the most this calendar year, an investor would have had to look at 2023 earnings estimates at the end of December 2022, along with the dispersion at that time.
Yes, we use the estimates available on December 31 for the next 12 months. Now, of course, you could use any 12-month period. For writing the book, we looked at earnings estimates as of December 31 for the subsequent 12 months.
You also wrote about bond ratings. Moody’s makes its ratings available with free registration on its website. Some individual investors might have access to S&P or Fitch ratings. How can individual investors use bond credit ratings?
I’ll answer the question from two standpoints. If you’re looking to include corporate bonds, municipal bonds or bond funds along with stocks in a balanced portfolio, then the credit ratings are a useful reference point for matching the bonds to your risk tolerance. A key dividing line is between investment grade—ratings of AAA to BBB—and speculative grade—basically, BB to CCC. In general, the lower the rating, the higher the yield, but also the greater the price swings you can expect in response to changes in economic conditions. So, you should determine what type of rating you feel comfortable with.
As far as identifying the number-one stock in the S&P 500 is concerned, realize the top stocks have not historically been AAA-rated companies like Apple Inc. (AAPL) or Johnson & Johnson (JNJ). One reason for that is AAA-rated companies are bigger on average than BB-rated companies. I point this out because the top-performing stocks have generally been smaller than the median- sized company in the S&P 500, as measured by market capitalization.
One other thing to consider is that lower credit ratings generally indicate a higher degree of leverage—that is, reliance on debt financing. Having a lot of debt on the balance sheet magnifies the impact of a positive development that causes a company’s cash flow to rise and the stock price to go up.
For a stock with below-investment-grade credit ratings, is there a minimum rating below which investors should view the company as being too risky to invest in?
Once you get down to the CCC rating—and there are CC and C ratings too, which identify companies that are already in trouble—you have a significantly higher risk of default within a 12-month period than you do with companies rated BB or B–. Investing in such companies is going out on a limb in terms of risk because not only could the company suffer an earnings decline but it could also go into default in a fairly short period of time. The likelihood of this occurring goes up when you get into a recession.
Catalysts were another common trait among the best-performing stocks in the S&P 500. You give several examples in your book. Are there any common traits an individual investor should look for when seeking out a stock with the potential to make a big price move? Put another way, how do investors judge whether a development could be a catalyst to really move the stock price upward versus something that’s just news or more of a gradual change?
There are a couple kinds of catalysts that are worth keeping an eye out for. One is a company that is in an industry dominated by a proverbial 800-pound gorilla. This can actually be a positive factor. If the dominant firm falters—perhaps it’s unable to meet its production schedule—then a new revenue opportunity for one of the smaller firms in the same industry suddenly and very unexpectedly opens up. The size of that opportunity can be quite substantial relative to the smaller firm’s revenues. A huge revenue increase can really boost the stock tremendously.
Another type of catalyst arises in technologically dynamic industries such as semiconductors. These businesses are characterized by extremely short product cycles. So, a company that has dominated one segment of its market for the past few years can suddenly get displaced by a competitor that has developed a slightly better mousetrap. The payoff on gaining that edge can be huge.
One final type of catalyst that I would point to is in the energy industry, because commodity prices there are subject to sharp spikes. There can be political developments—something like the Ukraine-Russia war, for example—dramatically affecting either natural gas or oil or both. If a company is highly leveraged to commodity prices, a jump in energy prices can cause it to shoot up to the very top of the S&P 500.
Two other traits of past top-performing S&P 500 stocks were below-median size and above-median volatility. There’s a documented size effect. But your suggestion for seeking out higher levels of volatility seems to contradict the low volatility factor, which favors below-median levels of volatility. Is this a case where speculative investors should seek high volatility and long-term investors may want to instead view low volatility as a potential consideration?
Yes, that hits it right on the head. A stock that never experiences a big price drop is the kind that lets you sleep well at night. It may have excellent long-term potential for appreciation. So, it fits into that core part of the portfolio, but price stability cuts both ways. This kind of stock won’t go down a lot, but it’s also unlikely to double or more in the space of a year, which is what the number one stocks typically do. So, it’s kind of like the difference between being on the merry-go-round and being on the roller coaster: You should decide when you go to the amusement park which kind of ride you like the best.
Speaking of being aggressive versus more conservative, I’ve met individual investors over the years who follow a core-and-satellite approach. The majority of their portfolio is traditionally allocated to index funds and a small part of the portfolio is managed very aggressively. Any suggestions on how much of a portfolio should be speculative for those who want to follow such approaches?
In the book, I take a very conservative approach on that. To get the satisfaction of being right and the accompanying bragging rights, you don’t need to use more than 1% or 2% of your portfolio for speculation. Now, this is about making a play on one specific stock that you’re hoping to be the number-one stock in the S&P 500.
I think Jeremy Siegel has thrown out a figure more like 15% using terminology conceptually similar to core and satellite. Putting 15% into one stock that could potentially be the best performer in the S&P 500 would be overdoing it. But if you’re talking about buying good long-term value stocks that you expect to build wealth over a multi-year period, then an allocation of 10% or 15% would not be irresponsible.
We’ve been talking about what traits are associated with potentially big price gains, but the list of traits that aren’t associated with big price moves might surprise some investors. At the top of that list is strong revenue growth.
There are several factors that get a lot of attention but just don’t show a correlation with the top five stocks in the S&P 500. One of those characteristics is, as you say, high revenue growth. We measured it over the previous five years because if a company is generating that kind of growth, it shows the company is successful in its business.
Analysts predicting a high percentage price gain was not really associated with top performance. A high level of short interest is something you hear a lot about because, technically, it sets a stock up for a big move. A high level of insider buying is viewed as a very positive factor. If the people who really have the best knowledge of the company are buying the stock very aggressively, that must mean that they expect a big move to occur. One other factor that probably doesn’t get as much attention but logically might be considered a positive factor is a positive outlook issued by the credit rating agencies.
I think the reason these characteristics didn’t identify the top five stocks in advance is precisely because they were known in advance. So, to the extent that they were favorable characteristics, they were already reflected in the prices of the stocks.
What drove stocks to be the number-one stock in the S&P 500 in terms of one-year returns was surprises: things that were not known; in some cases, things that could not possibly have been known. One dramatic example and this is a stock that wasn’t in the S&P 500 at the time is Zoom Video Communications Inc. (ZM). The stock quadrupled in price in 2020. Zoom was perfectly positioned to take advantage of the totally unforeseen shift to working from home due to the coronavirus, whose global spread didn’t start to become apparent until early 2020. The pandemic created far more demand for virtual meetings than had ever existed before and beyond what the company could have predicted or would have been responsible for predicting from a business planning standpoint.
Is there anything I should have asked you that I haven’t?
The one thing that perhaps is worth highlighting is that almost 50 years ago the Financial Analysts Journal published Joel Stern’s paper “Earnings per Share Don’t Count” (July–August 1974). Stern showed how companies would make acquisitions that reduced their average return on investment even though their earnings per share increased. This occurred because the acquisitions were done by paying cash for cash instead of by issuing new shares. The smart investors who really set the prices of the stocks weren’t about to pay a higher price for a stock where the return on income was going down.
It’s amazing to me that half a century has gone by and there really hasn’t been a change in the ecosystem as to how recommendations get generated on Wall Street. It’s understandable when you look into it: It’s in the interest of the companies to keep it this way because they can control the situation up to a point by issuing earnings guidance. So, it’s very hard to break out of this. But I find it fascinating that, although there are independent research organizations producing research that focuses on free cash flow, the inertia of focusing on earnings per share and the relative lack of innovation in the way Wall Street research is done continue to exist.
Relevant Information & Terms
Earnings Estimate Dispersion
Each quarter, companies’ reported earnings are measured against the forecasts made by analysts (earnings estimates). The individual earnings estimates made by analysts for a given company are aggregated into a consensus. This consensus estimate serves as the benchmark used to determine whether a company beat or missed expectations.
The consensus estimate is the average of all forecasts made by analysts. Because it is a simple average, it does not tell you anything about the extent to which analysts agree or disagree on how much the company will earn.
This is why some investors also pay attention to the dispersion of earnings estimates. Dispersion measures the extent to which analysts’ forecasts are similar or are far apart. The greater the level of dispersion (analysts are less in agreement), the greater the likelihood of an earnings surprise being announced (either positive or negative).
In “The Little Book of Picking Top Stocks,” Martin Fridson introduces the Fridson-Lee statistic. This measure of dispersion is calculated as the highest estimate minus the lowest estimate, divided by the lowest estimate. Looking at this measure, he found that high levels of dispersion were common among the S&P 500 number-one stocks. “In 90% of the cases we examined, the Fridson-Lee statistic just prior to [stock price] takeoff was 18% or higher, with some stocks clocking in at nearly 200%,” wrote Fridson.
An Example of Low Dispersion
WEC Energy Group Inc. (WEC) is a Wisconsin-based utility. The consensus among the 14 analysts providing earnings estimates calls for the company to earn $4.61 per share this year.
» Highest earnings estimate: $4.62 per share
» Lowest earnings estimate: $4.59 per share
= ($4.62 – $4.59) ÷ $4.59
= $0.03 ÷ $4.59
= 0.7% dispersion
An Example of High Dispersion
Las Vegas Sands Corp. (LVS) operates casinos and resorts. The consensus among the 18 covering analysts providing earnings forecasts calls for the company to earn $1.43 per share this year.
» Highest earnings estimate: $2.14 per share
» Lowest earnings estimate: $0.54 per share
= ($2.14 – $0.54) ÷ $0.54
= $1.60 ÷ $0.54
= 296.3% dispersion
Factors Not Shown to Be Predictive of Potentially Big Price Gains
There are several factors associated with favorable stocks that aren’t useful for identifying the best-performing S&P 500 index stocks over the next 12 months. Here are some of those factors.
Higher-Than-Average Revenue Growth: While a positive trait, there is not a discernable pattern between a high level of past revenue growth and big price moves.
High Short Interest: Despite the occasional news headlines about a “short squeeze” leading to a jump in a stock’s price, short interest wasn’t found to be an identifiable factor of top-performing S&P 500 stocks.
Insider Buying: Purchases by those who know the most about the company are priced into the stock and therefore aren’t helpful for identifying next year’s top-performing stock.
Investment-Grade Bond Ratings: Seeking non-investment grade companies works better because higher debt levels magnify the impact of a positive development leading to an increase in a company’s cash flow.
Low Volatility: Stocks that trade within a narrow range may help you sleep at night but aren’t as likely to make big price moves as smaller stocks.
Rating Agency Outlooks: Neither positive nor negative outlooks issued by credit rating agencies were found to have any consistent, predictive pattern in terms of predicting top performers.