Well That's Flattering

On June 15th I posted my quick idea on IPC Holdings (IPCR) in which I listed a number of reasons that the broken deal with Max Capital provided an opportunity to buy into the stock at a very attractive valuation. I guess Warren Buffett must have read my report.

Via Bloomberg:

"Warren Buffett’s Berkshire Hathaway Inc. offered $1.7 billion in cash to buy Bermuda-based reinsurer IPC Holdings Ltd. earlier this month, said two people with knowledge of the bid.

Berkshire is the “Party M” named in a regulatory filing today as making a July 1 bid, according to the people, who asked not to be identified because the offer wasn’t public. The bid was rejected in favor of a lower stock-and-cash offer from Validus Holdings Ltd. because Party M demanded IPC not pay a third-quarter dividend, the filing said. Party M is called “a global insurance and reinsurance company” in the document."

Now, I know the stock is not up very much since I recommended it. However, I think Buffett's interest in IPCR indicates that I was dead on regarding the quality of the company. Plus, if the Oracle is looking at a company you know it has to be valued attractively. My hope is that this is just one of many times in my career in which my value investing acumen is validated by the interest of a guru.

Why You Should Pay No Attention to Consensus Estimates

While perusing the news headlines on Seeking Alpha this morning I learned that Fairchild Semiconductor (FCS) reported second quarter earnings per share of -$.03, soundly beating the consensus estimate of -$.11. Hooray! The company only lost $.03 a share in the worst economic downturn in 80 years. This is surely something to celebrate, as evidenced by the fact that the stock was indicated up in pre market trading. Now, I have no idea whether the stock will finish or up down on the day. I also don’t know if only losing $.03 a share in this economic environment is actually indicative of shrewd management and effective cost containment. It could be that FCS did an admirable job in Q2 navigating its way through a very tough market. I am certainly not an expert on the semiconductor industry and am not picking on FCS. I’m just using Fairchild’s Q2 results as an example of what I see as the absurdity of putting any weight behind how a company performed relative to consensus forecasts.

Being a contrarian investor, whenever I hear that the “consensus” believes something I tend to shudder. In fact, I often take universal agreement as a contrary indicator, meaning that I tend to feel that whatever it is that the herd is predicting will either not play out or, even worse, the exact opposite will result. During earnings season investors are inundated with consensus forecasts for earnings and the market is unfortunately prone to celebrating companies who trump analysts’ estimates but to scorning those who fall short of expectations. Given that market participants often react very strongly to announcements of quarterly revenue, margin, and earnings per share figures, doesn’t it make sense to ask simple questions regarding the historical accuracy of analyst estimates? In other words, if the market as a whole puts so much emphasis on the prognostications of analysts, isn’t it important to know if the numbers that companies are being compared against are even meaningful? What good is using a benchmark to evaluate the quality of a company if that benchmark is completely skewed?

In his 1998 book, “Contrarian Investment Strategies: The Next Generation” fund manager David Dreman tries to answer the very questions posed above. (After a brief search I could not locate any more recent data but I suspect that nothing has changed in the last 11 years, especially given the fact that increased market volatility has only made forecasting more difficult.) For any of you who rely on consensus estimates and forward earnings multiples in order to make investment decisions, the results of Dreman’s study with Michael Berry of James Madison University should be eye opening.

The study compared brokerage analyst quarterly earnings forecasts between 1973 and 1996. In total, the duo surveyed over 94,000 estimates and in order to make sure that they were not relying on too few analysts, required that at least four analysts’ estimates were included for each company. Furthermore, more than 1500 companies were included to ensure that companies with a wide spectrum of market caps were included. According to Dreman:

“The results are startling—analysts’ estimates were sharply and consistently off the mark, even though they were made less than three months before the end of the quarter for which actual earnings were reported. The average error for the sample was a whopping 44% annually.” (Emphasis mine)

Dreman goes on to point out that despite the explosion of readily available information on companies since the 1970s, analysts’ ability to forecast did not get any better. In fact, in the last eight years of the study the average error was an incredible 50%. The researchers also attempted to eliminate the effect on the averages of large errors:

“[We] eliminated all companies that reported earnings in the + or – 10-cent range to prevent large percentage errors from this group distorting the study…Even using this ultra conservative method, the average forecast error was still 23%...more than quadruple the size that market pros believe could set off a major price reaction.”

As referenced above, Berry and Dreman use 5% as a benchmark for the maximum amount a company could beat or miss by without setting off a major market reaction. In other words, they assumed that for a company expected to earn $1 per share, the company could earn anywhere between $.95 and $1.05 (approximately) without making the stock price move dramatically. In addition, the team used the +/- 5% range as a barometer of analyst accuracy. For an analyst to be deemed prescient, he or she would have to be able to predict the quarterly earnings within a 5% range. Whether you think that measuring stick was fair or not, the percentage of analysts who met that criteria was pathetically low. Specifically, only 29.4% of analysts were able to forecast within +/- 5%. Even more troubling, only 46.8% were able to do so within a +/- 10% range and only 58.3% fell within a +/- 15% window. Taking that company that is supposed to earn $1 again, the evidence shows that, on average, less than 60% of analysts would be accurate within $.15 cents in either direction. When you consider how much a stock can move if the company makes $.85 or $1.15 as opposed to $1, the average analyst error is not insignificant.

What about during recessions? 47.4% average error. Expansions? 44.9% average error. But what about if the study is broken down by industry? Using 62 separate subgroupings between 1973 and 1996 the average error was 50%. For example, the worst errors occurred in metals and mining (71%) and oil (73%). Although, I guess investors can take some solace in the fact that errors for tobacco (4%), food (25%) and communication (25%) stocks were far less than the average.

Now, I am well aware that accurately and consistently predicting quarterly earnings is very difficult and borders on impossible. I certainly feel for my sell-side brethren who are constantly under pressure to see into a very murky future and make recommendations based on a cloudy crystal ball. In general, I see that there are five major factors (there could be more) that contribute to this proven inability to forecast in a precise manner (in no particular order):

1. Reliance on management guidance: Investors can’t forget that management teams have all the incentive in the world to under-promise and over-deliver. No CEO wants to explain why his company was unable to meet or beat the guidance it provided for analysts and investors. Accordingly, executives would rather err on the side of caution by low balling earnings estimates or providing a very wide range, neither of which help analysts when it comes to precision. Also, no matter how much an analyst knows about a company there are always numerous moving pieces that only insiders can understand and assess. The result of this is that many times there are things going on with customers or production that analysts cannot possibly be expected to account for.
2. Earnings manipulation: Although you almost never hear of the SEC investigating claims of earnings smoothing or manipulation, it would be very naïve to believe that this does not happen on a regular basis. Whether it is companies like GE consistently beating estimates by a single penny, manufacturing firms recognizing revenue aggressively at the end of the quarter or struggling companies taking large write downs all in one quarter (commonly known as a big bath), there is plenty of evidence that earnings manipulation is a relatively common occurrence. While some of it is probably benign, practices like those listed above make it much harder for analysts to anticipate what quarterly earnings are going to be.
3. Use of complicated models: Many sell-side analysts maintain robust Excel spreadsheets with earnings and margins extrapolated over five, ten or even twenty year periods. But, if analysts are not able to reliably predict earnings on a quarterly basis, how in the world could they do so years into the future? A model is only as good as the underlying data and the more skewed that data is the less useful the results of the model.
4. Dynamic economy: We are in unprecedented times when it comes to the volatility within the global economy. Company management teams have very little visibility regarding demand tomorrow, let alone next quarter or next year. Accordingly, analysts are currently completely in the dark and my guess is that their forecasts will reflect that. However, even in more benign times supply and demand are fluctuating constantly and a company’s prospects can literally turn on a dime. Unless an analyst has unusual access to industry participants and is literally in the trenches with the companies he or she covers, it is very unlikely that dynamic conditions will be captured in earnings forecasts.
5. Quarterly noise: The truth is that three months is not a very long time. For companies that plan their budgeting for a full year, small deviations from the budget or unexpected timing of revenues can have outsized effects on quarterly earnings. In addition, things like capital expenditures, hirings, firings, and the timing of share buybacks or debt issuance can skew individual quarterly results dramatically. This is why prudent managers don’t worry about managing quarterly numbers and focus on the long term. But, what this means is that analysts will always be unable to account for what is nothing more than noise in their quarterly predictions.

In conclusion, the above analysis is not really an indictment of the analyst community. I understand very well why it is so difficult to accurately forecast quarterly earnings and do not hold it against the analysts for having an extremely poor historical track record. Believe me; I could not do any better myself. However, this is explicitly a criticism of a market and system that relies on analysts’ estimates to make investment decisions even though the data unequivocally shows that the forecasts are wholly unreliably and inaccurate. The idea that anyone believes that a stock should go up or down based on the deviation from earnings expectations is absolutely ludicrous. Instead, investors would be wise to come up with their own assessment of a company’s value that does not rely on forward earnings or a company's ability to print whatever number it thinks the Street wants to see.

(Picture courtesy of ebooks.metronet.lib.mi.us)

Beware of the Dreaded Anchoring Bias

Now that financial Armageddon is apparently off the table and markets have begun to stabilize a bit, is it time for investors to get back to good old fashioned stock picking? In other words, going forward will bottom up analysis of individual securities again be more important than the top down, macroeconomic view? Of course any definitive answer to those questions would be nothing more than speculation. However, if is it the case that company specific fundamentals are more likely to drive share prices now that the volatility has subsided, investors would be smart to revisit a couple of biases that could lead to poor returns. Accordingly, over the next few posts I will examine some of these biases and how they often play out among stock market participants.

The first of these is anchoring bias. In a famous paper from 1974, behavioral scientists Tversky and Kahneman describe this bias in the following manner:

In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer. The initial value, or starting point, may be suggested by the formulation of the problem, or it may be the result of a partial computation. In either case, adjustments are typically insufficient That is, different starting points yield different estimates, which are biased toward the initial values. We call this phenomenon anchoring.

Tversky and Kahneman observed this behavior in a number of experiments conducted in the early 1970s. In the most well-known of these studies, the researchers asked participants to estimate the percentage of African countries in the United Nations. The results indicated that people anchored their answer to completely arbitrary numbers presented by the researchers. For example, the median estimate of people who were given 10% as a starting point was 25% and the median estimate of people who were given 45% as a starting point was 65%. Specifically, people became anchored to the percentage suggested to them by the question even though that number had nothing to do with the actual percentage of African countries in the UN. Having no knowledge of the exact percentage, people subconsciously took their cues from the numbers presented in the questioning despite the fact that those numbers were randomly generated.

Now, how does this bias manifest itself in the investing world? I think the main way in which investors can fall prey to this pitfall is by paying too much attention to the past prices of securities. The two most prevalent numbers that people seem to anchor to are the 52 week high and 52 week low for a stock. Setting aside technical analysis, in my young career I have observed a marked tendency for people to assume that a stock has potential to get back to its 52 week high but not breach its 52 week low. I think this is a reflection of the eternal optimism that exists in the market. On some level even short sellers believe that the market’s trajectory over the long run is more likely to be up than down. The problem with this thought process is that it assumes that those numbers are an indication of value and are not just random outcomes based on the whims of the market. In the end the value of a stock should be based on its earnings potential, a value that at certain times may have absolutely nothing to do with the current stock price. A quick look at the ride the Nikkei Stock Exchange has had over the past 20 years provides a sobering reminder that previous highs may never be reached again and stocks can stay at low nominal values for a protracted period.

Let’s take a current example to show how anchoring bias could really trip up an investor. This may seem like an extreme example but I think it illustrates very clearly the danger of becoming anchored to past prices. According to Google Finance, the 52 week high for Citigroup (C) was $23.50, reached in October of last year. From the current price of $2.78 Citi would have to appreciate by more than 840% to reach that 52 week high again. Could it get back there? Sure, but I think there are a number of things that will prevent that from happening for a long time (and maybe never). This is because the dynamics of companies are always changing and material events can occur that make previous prices completely irrelevant. In Citi’s case, the price in October 2008 obviously did not properly value the assets and future earnings power of the bank. In effect, the toll that the number of toxic assets on Citi’s balance sheet was going to have on earnings and solvency was not at all priced in at that point. Currently I think there are very few investors who would argue that the 52 week high reflected the company’s actual fundamentals or that Citi’s current earning power is anywhere near what is was during the boom period of 2005-2007 in which Citi averaged $2.90 in EPS. Therefore, that previous high may provide just as much information about the current value as the spin of a wheel provided about the percentage of African countries in the UN.

Furthermore, adding to Citi’s problems is the fact that in the future it is likely that the company’s return on equity (ROE) will be much lower. Just about every week in his market commentary, fund manager John Hussman includes a caveat regarding profit margins. For example, from his June 15^th piece:

Stocks are modestly overvalued here, except on metrics that assume a permanent recovery to 2007's record profit margins (which were about 50% above the historical norm).

The implication is that companies were using leverage to increase profit margins and as the world de-levers, companies like Citi are not likely to be able to achieve the same margins going forward. This of course is compounded by the fact that Citi’s majority shareholder is now the US government. It is logical to assume that Citi will be under constant pressure to reduce leverage, limit risky but potential profitable behavior, and forgo some earnings to serve the nation’s credit needs. Finally, the fact that Citi’s share count has increased so much over the past year makes it much more difficult for the price to appreciate in a rapid manner. Just based on supply and demand there now has to be tremendous demand to move the needle on the stock price. Recently, many investors have not cared about dilution as numerous companies have raced to raise new equity. However, it is my belief that in the long run supply and demand fundamentals rule the day and that a company whose share count has increased as much as Citi’s has will have a harder time getting back to previous highs.

On the flip side of the anchoring bias regarding the 52 week high is the tendency to believe that a 52 week low represents some kind of durable bottom for a stock or index. These days the most common comment I hear has to do with the devilishly low 666 figure on the S&P 500 being the ultimate bottom of this bear market. I can offer no definitive wisdom about whether or not the fundamentals in the economy and financial markets will justify the index staying above its 52 week low. But, setting aside technical analysts and chart gurus, my concern is that some bottom up investors will ascribe some omniscient value to this figure and act on it. I personally don’t believe that the daily price of a stock or the value of an index consistently tells investors anything more than what Mr. Market is thinking on that day. All analysis of whether a price is too high, too low or just right has to be based on fundamentals. If people believe that at 666 the market is significantly undervalued based on the earnings potential of the companies in the index, then I have no problem with using a breach of that level as a buy signal. But to act as though the index should not go lower than 666 because that low was anything but a random number (like those that were presented to the research subjects of Tversky and Kahneman) is a recipe for disaster and substandard returns.

In conclusion, I would welcome the return of a time when the macroeconomic outlook did not completely dominate the direction of the market and individual stocks. While I am not sure we are there yet, it is still important for investors who are fundamentally oriented to remember that paying too much attention to past prices levels, earnings figures or profit margins without considering valuation is an example of anchoring bias that could skew a rational assessment of a stock or index.

(Picture courtesy of brotherpeacemaker.wordpress.com/2008/11/01/)