Why I cannot stand George Soros!

Let me start with a confession. There are some people I will pay not to listen to, and one of them is George Soros. First, let's dispense with the myth that this guy is a great investor. I don't know Buffett, but if I did, I would tell you that Soros is no Buffett. George Soros is a speculator who got lucky at two levels. The first was timing.. betting against the British pound in the early 1990s was perfect. The second was that he has made his big score betting against central banks that refused to face the facts.

There are many investors who mistake luck for skill and I would not blame Soros for doing the same, if it were his only fault. There are two things about the man that I find distasteful:

1. Moral high ground: I find it hard to listen to lectures on morality and ethics from Mr. Soros, A speculator who made his money on a few big bets should not be telling the rest of the world what constitutes good or moral behavior and why hard work should be rewarded.

2. False expertise: The Financial Times has been publishing a series of articles by Soros on how banking can be fixed in developed markets. A few years ago, Soros also told us what was wrong with the derivatives markets and why options and futures should be restricted, regulated or banned because they could be misused. Unfortunately, the man knows little about either. But, he made a lot of money on derivatives, you say... True! But we don't consider a guy who hits the jackpot on a slot machine in a casino to be an expert on probabilities, do we?

My point is a larger one. We assume that people who have been successful in investing know a great deal more about investing than we do. We buy their books, we listen to them on television and radio and worst of all, we entrust our savings to them at substantial cost. While this may be true in a few cases, it is not true in most. Most successful investors and traders are successful because they are lucky and not because of their intellectual prowess or investing smarts... it is better to be lucky than smart. A few of these investors (like Soros) let success get to their heads and start believing their own hype. They should be ignored!

Buffett: Man or Myth

Warren Buffett is now more myth than man. The investors who claim to follow Buffetology, read the Berkshire Hathaway annual report and actually make the trip to Omaha (the value investing Woodstock) numbers in the tens of thousands, if not millions. I think that we do a disservice to Buffett, when we put him on a pedestal and treat every word he says as gospel. At the risk of sounding like a curmudgeon, here is my take on Mr. Buffett.

Let's start with the obvious. Warren Buffett has been an incredibly successful investor and his string of successess cannot be explained by luck. He has been able to make money with diverse investment strategies, but with a core philosophy that has remained unchanged over the last four decades. That core philosophy: you buy a business, not a stock. Hence, his competitive advantage has been assessing the value of an underlying business, especially in chaotic times, and buying stock in that business, when the price is down.

Buffett has never been an activist investor, i.e., he has seldom taken positions in poorly managed companies and tried to change the management. In fact, one of his criteria for investing in a company is that he admires the management. He has also never been comfortable straying from his niche, which is mature businesses: I cannot think of a single, big investment that he has made in a technology or young company.

I like to read Buffett's comments on the markets. Unlike many market strategists at investment banks, who cloak their recommendations in buzz words and hedge them until they are meaningless, Buffett is to the point and says what he means. His emphasis on corporate governance puts most institutional investors to shame.

My only real issue with Buffett is that he sometimes lets his "Aw, shucks! I am just a regular investor" persona cover up two things that he does that contribute to his success. One is his careful focus on cash flows; he might not use the terminology of valuation but Buffett has been using free cash flows to measure investments for as long as he has been investing. The second is that he is now an insider in many of the companies that he invests in; he has an advantage over you or I, when taking the same investments.

One final point about Buffett. The mythology is that Buffett does not adjust for risk, when he invests. I have even heard people say that he settles for the riskfree rate. Right? Buffett may not use betas or risk-adjusted discount rates but he certainly factors risk into the analysis by making conservative estimates of the cash flows. In effect, he reduces the expected cash flows of riskier businesses, i.e., uses certainty equivalents.

Be like Buffett, if that is what you want to do. But don't view this as a license to ignore risk and to just buy companies with good management (no matter what the price). You are almost certainly not going to make money that way.

Fama-French and the Proxy Wars

A turning point in the debate on risk and return occurred in the early 1990s, when Gene Fama and Ken French wrote one of the most-quoted and influential papers on the topic. In it, they began with a simple premise. If our objective in risk and return models is to come up with expected returns on investments, should we not judge the quality of these models by looking at how well they explained actual returns over very long time periods? They began by looking at the CAPM: in it, all return differences across investments should be explained by differences in betas. Looking at actual stock returns from 1962 to 1990, Fama and French found that betas cannot explain a very large portion of the differences in returns across stocks.

This finding was not knew and reflected what other researchers had concluded in earlier papers. Fama and French, however, decided to turn the problem on its head. Rather than build an alternative risk and return model, which is what others had tried to do with the arbitrage pricing and multi factor models, with all their baggage, they decided to start with the data on returns and work backwards. In other words, they went looking for other company characteristics that would do a better job of explaining differences in returns across stocks, than betas did. Their search led them to two variables - the market capitalization of the company and it's price to book ratio- that together explained a large portion of the differences in returns. Small market cap companies and low price to book value companies consistently earned higher returns than large market cap companies and high price to book value companies. Rather than view this as an inefficiency (which other papers had in the past), Fama and French considered these variables as proxies (stand ins) for risk. In effect, they concluded that small companies must be riskier than large companies and low price to book companies must be riskier than high price to book companies.

While the logic of the Fama-French approach is impeccable, it has one significant weakness. Since it is data driven, any proxy, no matter how outlandish, that explains differences in returns could be used in the model. At the risk of pushing this argument to absurd limits, if companies with fatter CEOs have higher returns than companies with thinner CEOs, the weight of the CEO should be used as a risk proxy. Not surprisingly, as the data we look at gets richer and deeper, other variables have been added to the list of good risk proxies - return momentum, earnings revisions, insider buying etc.

Models that test the CAPM against proxy models by looking at how much of past returns are explained by each are almost certainly going to find the CAPM wanting. After all, the proxies were not picked at random but because they had been correlated with returns in the past.

I think that the question of whether to stick with the CAPM or go with a proxy model depends in large part on what you are using the model for. If your job is performance evaluation, say of mutual funds, I think that proxy models make sense, because you are looking at the past and examining whether individual mutual funds beat the market. If you are trying to forecast expected returns in the future, which is what you are doing when estimating cost of equity in corporate finance or valuation, I prefer to stick with the CAPM, with bottom up betas and adjustments for financial and operating leverage.

Can betas be negative? (and other well used interview questions)

Here is a favorite question among corporate finance interviewers: " Can betas be negative? And if so, what exactly do they tell us?" The reason negative betas pose a conundrum to many finance students is that they seem to go against intuition. After all, if a beta of one is average risk and a beta of zero is riskless, how can an investment have negative risk?

Here is the answer. Yes, betas can be negative. To see how and why, consider what betas measure: the risk added by an investment to a well diversified portfoli0. By that definition, any investment that when added to a portfolio, makes the overall risk of the portfolio go down, has a negative beta. A more intuitive way of thinking about this is that a negative beta investment represents insurance against some macro economic risk that affects the rest of your portfolio adversely. A standard example that is offered for a negative beta investment is gold, which acts as a hedge against higher inflation (which devastates financial investments such as stocks and bonds). It is also true that puts on stocks and selling forward contracts against indices will have negative betas.

What are the consequences of a negative beta? The expected return on that investment will be less than the riskfree rate; the nominal returns on gold over the last 40 years have been 2% less,on average annually, than the riskfree rate. However, that makes complete sense if you think of it as buying insurance. You are paying for the insurance by settling for a very low or even negative return.

Are there actual investments out there that have negative betas? I know that there are stocks with negative regression betas, but those are the mostly the result of something strange happening during the period of the regression - an extended lawsuit or acquisition battle throwing off the correlation with the market- rather the true betas. In fact, in my fifteen years of updating betas by sector, I have still not found a sector with a negative beta. Furthermore, even assets that, in theory, could have negative betas (gold, for instance) seem to have positive betas when securitized (gold shares, gold ETF). There seems to be something about the securitization process that makes real assets behave more like financial assets.

Alternatives to Regression Betas

In my prior post, I explain why I am averse to regression betas - the high standard errors of the estimates, the backward-looking nature of the estimates and the loss of intuitive feel for risk. In this post, I would like to look at alternatives that are offered to regression betas, with an eye towards making better estimates:

1. Relative standard deviations: The beta is a function of the standard deviation of the stock, the standard deviation of the market and the correlation of the stock with the market:

Beta = (Correlation between stock and market) (Std dev of stock)/Std dev of mkt

The primary culprit behind the high standard error of betas is the shifting correlation number. Hence, there are some who suggest using an alternative measure of relative risk:

Relative Std dev = (Std dev of stock)/Average std dev across all stocks

Note that the denominator has to the average standard deviation across all stocks, not the standard deviation for the market, since you want the relative risk number to average out to one across all stocks (and it will not if you use the standard deviation of the market).
Pros: This number, like beta, should average out to one across stocks and should have lower standard error; even in a period like the last quarter, the standard deviations rose across the board and the relative standard deviation was fairly stable.
Cons: You are looking at total risk (not just market risk), which may not be appropriate for a diversified investor. You are also still backward looking and dependent on stock prices.

2. Option based approaches: A few years ago, there was a hue an a cry, arising from a paper published in the Harvard Business Review, which claimed to have come up with a forward looking estimate for risk. In the approach, the implied standard deviation of stocks is backed out of traded options issued by the company and compared to the standard deviation of bonds issued by the same company.
Cost of equity = Cost of debt *(Imp std dev of equity/ Std dev of bonds)
Pros: Forward looking, becauase you use option prices to back out standard deviation.
Cons: Works only for companies that have traded options and bonds... and it mixes up total risk and market risk. Does not strike me as a general approach that will work with most companies.

3. Accounting betas: Rather than regressing stock returns against market returns, we could regress changes in accounting earnings at a company against changes in accounting earnings for the entire market, and the slope would be the accounting beta.
Pros: Not dependent upon stock prices and can be estimated even for private businesses. For those who do not trust markets, accounting earnings offer a more stable alternative (assuming that you trust accountants).
Cons: Accounting earnings are often smoothed out and lag true earnings. Furthermore, the number of observations in your regression is restricted. With quarterly statements, you will have 20 observations over 5 years and the resulting standard error will be huge.

4. Bottom-up Betas: In this approach, we start with the businesses that a firm operates in, estimate the betas of these businesses (by looking at the average regression betas of publicly traded firms in each of the businesses) and clean up for differences in financial leverage.
Pros: The average across many regression betas will be more precise than any individual company's regression beta. It can be computed based upon the current or even a future business mix of a company and for private businesses.
Cons: You do need to find publicly traded companies that operate predominantly or only in each individual business and you the average regression beta does reflect the past. For instance, the average beta across all banks over the last 5 years may understate the true beta for banks for the future.

I am a firm believer in the last approach. Since there are lots of mechanical details that can trip you up, I do have a link on my site where I examine these:
http://www.stern.nyu.edu/~adamodar/New_Home_Page/TenQs/TenQsBottomupBetas.htm
More in my next post!

The problem with regression betas

I must confess that I find the practices used to estimate betas to be both sloppy and counter intuitive. The standard approach, offered in every finance text book, is to regress returns on the stock against returns on a market index, with the slope yielding the beta. I have five problems with this approach:

1. Statistically, the slope coefficient in a simple regression comes with a standard error. The beta estimate for a typical US company has a standard error that is about 0.20. One way to read this number, is when you are told that the beta for a company (from a regression) is 1.10, the true beta could be anywhere from 0.70 to 1.50 (plus or minus two standard errors).

2. Outside the US, regression betas often look much more precise but only because the indices used tend to be narrow local indices (DAX, Bovespa, Sensex). As anyone who has toyed with the parameters of the beta regression (2 vs 5 years, daily vs weekly, different market indices) knows, you can arrive at very different betas for the same company, based on your choices. None of them is the right beta, and they may all be coming from the same distribution, but it is a wide one.

3. By definition, a regression beta has to be backward looking, since you need past returns. To the extent that companies change their business mixes  (by expanding, divesting and acquiring businesses) or their financial leverage (debt ratios) over time, the regression beta may not be a good measure of the beta for the future.

4. If the only way you can estimate betas is with a regression, you will be stymied right from the start, if you are analyzing the division of a company, a private business or a company that went public recently.

5. By making beta a statistical number, we are missing the fundamentals that drive beta. Every company has an intrinsic or true beta that comes from choices it has made and understanding how these choices can cause your beta to change is central to a better beta estimate.

So, I would take any beta reported for a company by a service or an analyst with a grain of salt. It probably came from a regression and should not define your thinking about the firm's risk. In my next two posts, I will offer my analysis of the determinants of betas and an alternative to regression betas.

What betas can... and cannot do...

There is no concept that is more abused, and more misinterpreted, than beta in corporate finance. I have heard betas blamed for everything from global warming to the market collapse. "Warren Buffet does not use beta", the refrain goes, "so why should I?"

I think the biggest mistake that people make is to wrap betas up with the assumptions of the capital asset pricing model (CAPM). Does the CAPM make unrealistic assumptions about no transactions costs and no private information to get to its final conclusion (that all exposure to market risk can be captured in a beta, which should then be the sole determinant of the differences in expected returns)? Absolutely. However, just because you don't like the CAPM's rigidity does not mean that you throw the baby out with the bathwater and abandon beta as a measure of risk, or worse, use no measure of risk at all.

I think of betas as measures of relative risk, with the risk defined as exposure to macro economic variables (interest rates, overall economic growth, inflation). Thus, a stock with a beta of 1.2 is 1.2 times more exposed to macro economic risk than the average stock in the market. That proposition stands, whether one buys into the CAPM or not. Seen from that perspective, here are the things betas cannot do:
1. Explain changes in returns for the entire market. The betas for all stocks cannot go up at the same time, since they have to average out to one. (This is in response to those who have argued that the recent drop in equity prices can be explained by an increase in betas across the board.)
2. Capture emerging market risk. If we regress the returns on emerging market stock against emerging market indices, which is the standard practice for most estimation services, the average beta for Indonesian stocks will be one, as will the average beta of Swiss stocks. If we run regressions against a global index, the results are often unpredictable, with betas for emerging market companies often dropping simply because they are such as a small part of the index.
3. Capture firm specific risk: Betas cannot incorporate risks that affect only a firm or a few firms, even if these risks are huge. Thus, a tobacco company's beta cannot reflect litigation risk and a biotech firm's beta will not capture the uncertainty inherent in the FDA approval process.

Here is what betas can do. They can capture shifts in risk across the market. If a sector gets riskier, its beta should go up, but there has be another sector whose risk has to go down to compensate. Thus, banks will have higher betas today than they did a year ago but technology firms may have seen their betas decline, as they have held up fairly well in this downturn. They provide simple, intuitive and surprisingly effective snapshots of how risky an investment is, relative to the rest of the market, especially if you are an investor with multiple investments in your portfolio.

I do have to mention in closing that running a regression of stock returns against a market index is both a terrible way of estimating betas and of thinking about betas. However, this post is already way too long for me to suggest alternatives to regression betas. That will come in the next post.