Alternatives to the CAPM: Part 2: Proxy Models

The conventional models for risk and return in finance (CAPM, arbitrage pricing model and even multi-factor models) start by making assumptions about how investors behave and how markets work to derive models that measure risk and link those measures to expected returns. While these models have the advantage of a foundation in economic theory, they seem to fall short in explaining differences in returns across investments. The reasons for the failure of these models run the gamut: the assumptions made about markets are unrealistic (no transactions costs, perfect information) and investors don't behave rationally (and behavioral finance research provides ample evidence of this).

With proxy models, we essentially give up on building risk and return models from economic theory. Instead, we start with how investments are priced by markets and relate returns earned to observable variables. Rather than talk in abstractions, consider the work done by Fama and French in the early 1990s. Examining returns earned by individual stocks from 1962 to 1990, they concluded that CAPM betas did not explain much of the variation in these returns. They then took a different tack and  looking for company-specific variables that did a better job of explaining return differences and pinpointed two variables - the market capitalization of a firm and its price to book ratio (the ratio of market cap to accounting book value for equity). Specifically, they concluded that small market cap stocks earned much higher annual returns than large market cap stocks and that low price to book ratio stocks earned much higher annual returns than stocks that traded at high price to book ratios. Rather than view this as evidence of market inefficiency (which is what prior studies that had found the same phenomena had), they argued if these stocks earned higher returns over long time periods, they must be riskier than stocks that earned lower returns. In effect, market capitalization and price to book ratios were better proxies for risk, according to their reasoning, than betas. In fact, they regressed returns on stocks against the market capitalization of a company and its price to book ratio to arrive at the following regression for US stocks;
Expected Monthly Return = 1.77% - 0.11 (ln(Market Capitalization in millions) + 0.35 (ln (Book/Price))
In a pure proxy model, you could plug the market capitalization and book to market ratio for any company into this regression to get expected monthly returns.

In the two decades since the Fama-French paper brought proxy models to the fore, researchers have probed the data (which has become more detailed and voluminous over time) to find better and additional proxies for risk. Some of the proxies are highlighted below:
a. Earnings Momentum: Equity research analysts will find vindication in research that seems to indicate that companies that have reported stronger than expected earnings growth in the past earn higher returns than the rest of the market.
b. Price Momentum: Chartists will smile when they read this, but researchers have concluded that price momentum carries over into future periods. Thus, the expected returns will be higher for stocks that have outperformed markets in recent time periods and lower for stocks that have lagged.
c. Liquidity: In a nod to real world costs, there seems to be clear evidence that stocks that are less liquid (lower trading volume, higher bid-ask spreads) earn higher returns than more liquid stocks. In fact, I have a paper on liquidity, where I explore the estimation of a liquidity beta and liquidity risk premium to adjust expected returns for less liquid companies.

While the use of pure proxy models by practitioners is rare, they have adapted the findings for these models into their day-to-day use. IMany analysts have melded the CAPM with proxy models to create composite or melded models. For instance, many analysts who value small companies derive expected returns for these companies by adding a "small cap premium" to the CAPM expected return:
Expected return = Riskfree rate + Market Beta * Equity Risk Premium + Small Cap Premium
The threshold for small capitalization varies across time but is generally set at the bottom decile of publicly traded companies and the small cap premium itself is estimated by looking at the historical premium earned by small cap stocks over the market. (In my 2011 paper on equity risk premiums, I estimate that companies in the bottom market cap decile earned 4.82% more than the overall market between 1928 and 2010.) Thus, the expected return (cost of equity) for a small cap company, with a beta of 1.20 would be:
Expected return = 3.5% + 1.2 (5%) + 4.82% = 14.32%
(I have used a riskfree rate of 3.5% and a mature market premium of 5% in my estimation)
Using the Fama-French findings, the CAPM has been expanded to include market capitalization and price to book ratios as additional variables, with the expected return stated as:
Expected return = Riskfree rate + Market Beta * Equity Risk Premium + Size beta * Small cap risk premium + Book to Market beta * Book to Market premium
The size factor and the book to market betas are estimated by regressing a stock's returns against the size premium and book to market premiums over time; this is analogous to the way we get the market beta, by regressing stock returns against overall market returns.

While the use of proxy and melded models offers a way of adjusting expected returns to reflect market reality, there are three dangers in using these models.
a. Data mining: As the amount of data that we have on companies increases and becomes more accessible, it is inevitable that we will find more variables that are related to returns. It is also likely that most of these variables are not proxies for risk and that the correlation is a function of the time period that we look at. In effect, proxy models are statistical models and not economic models. Thus, there is no easy way to separate the variables that matter from those that do not.
b. Standard error: Since proxy models come from looking at historical data, they carry all of the burden of the noise in the data . Stock returns are extremely volatile over time, and any historical premia that we compute (for market capitalization or any other variable) are going to have significant standard errors. For instance, the small cap premium of 4.82% between 1928 and 2010 has a standard error of 2.02%; put simply, the true premium may be less than 1% or higher than 7%. The standard errors on the size and book to market betas in the three factor Fama-French model are so large that using them in practice creates almost as much noise as it adds in precision.
c. Pricing error or Risk proxy: For decades, value investors have argued that you should invest in stocks with low PE ratios that trade at low multiples of book value and have high dividend yields, pointing to the fact that you will earn higher returns by doing so. (In fact, a scan of Ben Graham's screens from security analysis for cheap companies unearths most of the proxies that you see in use today.)  Proxy models incorporate all of these variables into the expected return and thus render these assets to be fairly priced. Using the circular logic of these models, markets are always efficient because any inefficiency that exists is just another risk proxy that needs to get built into the model.

I have never used the Fama-French model or added a small cap premium to a CAPM model in intrinsic valuation. If I believe that small cap stocks are riskier than large stocks, I have an obligation to think of fundamental or economic reasons why and build those into my risk and return model or into the parameters of the model. Adding a small cap premium strikes me as not only a sloppy (and high error) way of adjusting expected returns but an abdication of the mission in intrinsic valuation, which is to build up your numbers from fundamentals. I do think that it makes sense to adjust your expected returns for liquidity, and I think our capacity to do so is improving as we get access to more data on liquidity and better models for incorporating that data.

The series on alternatives to the CAPM
Alternatives to the CAPM: Part 1: Relative Risk Measures
Alternatives to the CAPM: Part 2: Proxy Models
Alternatives to the CAPM: Part 3: Connecting cost of equity to cost of debt
Alternatives to the CAPM: Part 4: Market-implied costs of equity
Alternatives to the CAPM: Part 5: Risk adjusting the cash flows
Alternatives to the CAPM: Wrapping up

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Alternatives to the CAPM: Part 1: Relative Risk Measures

The Capital Asset Pricing Model (CAPM) is almost fifty years old and it still evokes strong responses, especially from practitioners. In academia, the CAPM lives on primarily in the archives of old journals and most researchers have moved on to newer asset pricing models.  To practitioners, it represents everything that is wrong with financial theory, and beta is the cudgel that is used to beat up academics, no matter what the topic. I have never been shy about arguing the following:
a. The CAPM is a flawed model for risk and return among many flawed models.
b. The estimates of expected return that we get from the CAPM can be significantly improved if we use more information and remember basic statistics along the way. (I argue for using sector betas rather than a single regression beta.)
c. The expected returns we get from the CAPM (discount rates in valuation and corporate finance) are a small piece of overall corporate finance and valuation. In fact, removing the CAPM from my tool box will in no way paralyze me in my estimation of value.

Notwithstanding this, I understand the discomfort that people feel with the CAPM at several levels. First, by starting with the premise that risk is symmetric - the upside and downside are balanced - it already seems to concede the fight to beat the market. After all, a good investment should have more upside than downside; value investors in particular build their investment strategies around the ethos of minimizing downside risk while expanding upside potential. Second, the model's dependence upon past market prices to get a measure of risk (betas after all come from regressions) should make anyone wary: after all, markets are often volatile for no good fundamental reason. Third, the CAPM's focus on breaking down risk into diversifiable and undiversifiable risk, with only the latter being relevant for beta does not convince some, who believe that the distinction is meaningless or should not be made.

Consequently, both academics and practitioners have been on the lookout for better ways of measuring risk and estimating expected returns. In this post, which will be the first of a few, I want to look at alternatives to the CAPM that stay with its core set-up, where the risk of an investment is measured relative to the average risk investment and expected returns are derived accordingly:
E(Return) = Riskfree Rate + Beta of investment (Expected Risk Premium for all risky investments)
Note that in this set up, the riskfree rate and expected risk premium are the same for all investments in a market and that beta alone carries the burden of measuring risk. The fact that betas are scaled around one provides for a simple intuitive hook: an investment with a beta of 1.2 is 1.2 times more risky than the average investment in the market. I have extended papers on how best to estimate the riskfree rate and expected equity risk premium.

I. Multi Beta Models
Contrary to conventional wisdom, which views theorists as cult followers of beta, the criticism of the CAPM in academia has been around for as long as the model itself. While the initial critiques just argued that CAPM betas did not do very well in explaining past returns, we did see two alternatives emerge by the late 1970s.
- The Arbitrage Pricing Model, which stays true to conventional portfolio theory, but allows for multiple (though unidentified) sources of market risk, with betas estimated against each one.
- The Multifactor model, which uses historical data to relate stock returns to specific macro economic variables (the level of interest rates, the slope of the yield curve, growth rate in the GDP) and estimates betas for individual companies against these macro factors.
Both models represent extensions of the CAPM, with multiple betas replacing a single market beta, with risk premiums to go with each one.
Pluses: Do better than the CAPM in explaining past return differences across investments.
Minuses: For forward looking estimates (which is what we usually need in corporate finance and valuation), the improvement over the CAPM is debatable.
Bottom line: If you don't like the CAPM because of its complexity and its assumptions about markets, you will like multi beta models even less.

II. Market Price based Models
The CAPM beta can be written as follows:
CAPM Beta = Correlation between stock and market * Standard deviation in returns of stock/ Standard deviation in returns of market
The instability in this estimate comes from the correlation input, which can be volatile and change dramatically from period to period. One alternative suggested by some is to dispense with the correlation entirely and to estimate the relative risk of a stock by dividing its standard deviation by the average (or median) standard deviation across all stocks. For instance, the median annualized standard deviation across all US stocks between 2008 and 2010 was 57.01%. The relative standard deviation scores for two firms - Apple and 3M - can be computed using their annualized standard deviations over the same period: Apple's standard deviation was 42.66% and 3M's standard deviation was 25.17%.
Apple's relative standard deviation = 42.66%/ 57.01% = 0.75
3M's relative standard deviation = 25.17%/57.01% = 0.44
These take the place of the CAPM betas and get used with the riskfree rate and equity risk premium to get expected returns.
Pluses: Standard deviations are easier to compute and more stable than correlations (and betas)
Minuses: No real economic rationale behind the model. Treats all risk as equivalent, whether it can be diversified away or not.
Bottom line: For those who want relative risk measures that look closer to what they would intuitively expect, it is an alternative. For those who do not like market based measures, it is more of the same.

III. Accounting information based Models
For those who are inherently suspicious of any market based measure, there is always accounting information that can be used to come up with a measure of risk. In particular, firms that have low debt ratios, high dividends, stable and growing accounting earnings and large cash holdings should be less risky to equity investors than firms without these characteristics. While the intuition is impeccable, converting it into an expected return can be problematic, but here are some choices:
a. Pick one accounting ratio and create scaled risk measures around that ratio. Thus, the median book debt to capital ratio for US companies at the start of 2011 was 51%. The book debt to capital ratio for 3M at that time 30.91%, yielding a relative risk measure of 0.61 for the company. The perils of this approach should be clear when applied to Apple, since the firm has no debt outstanding, yielding a relative risk of zero (which is an absurd result).
b. Compute an accounting beta: Rather than estimate a beta from market prices, an accounting beta is estimated from accounting numbers. One simple approach is to relate changes in accounting earnings at a firm to accounting earnings for the entire market. Firms that have more stable earnings than the rest of the market or whose earnings movements have nothing to do with the rest of the market will have low accounting betas. An extended version of this approach would be to estimate the accounting beta as a function of multiple accounting variables including dividend payout ratios, debt ratios, cash balances and earnings stability for the entire market. Plugging in the values for an individual company into this regression will yield an accounting beta for the firm. While this approach looks promising, here are some cautionary notes: accounting numbers are smoothed out and can hide risk and are estimated at most four times a year (as opposed to market numbers which get minute by minute updates).
Pluses: The risk is related to a company's fundamentals, which seems more in keeping with an intrinsic valuation view of the world.
Minuses: Accounting numbers can be deceptive and the estimates can have significant errors associated with them.
Bottom line: If you truly do not trust market prices, use accounting data to construct your risk measures.

The reason for the CAPM's endurance as a model is simple. It provides a way of estimating the required returns and costs of equity for individual companies at low cost, by requiring only one input: a market beta. For those who like that aspect of the model, but don't like the baggage that comes with the model, relative standard deviations and accounting betas provide an alternative. For those who like the theoretical underpinnings of the model but do not like the poor estimates that it yields, the arbitrage and multifactor models should appeal. For those who contest the very basis of the approach, I will look at alternatives in the next few posts.

The series on alternatives to the CAPM
Alternatives to the CAPM: Part 1: Relative Risk Measures
Alternatives to the CAPM: Part 2: Proxy Models
Alternatives to the CAPM: Part 3: Connecting cost of equity to cost of debt
Alternatives to the CAPM: Part 4: Market-implied costs of equity
Alternatives to the CAPM: Part 5: Risk adjusting the cash flows
Alternatives to the CAPM: Wrapping up

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Margin of Safety: An alternative risk assessment tool?

I have lost count of the number of times I have been taken to task for not mentioning "margin of safety" in my valuation and investment books. In general, the critique is usually couched thus: "Instead of using beta or some other portfolio theory risk measure, why don't you look at the margin of safety?". While I see the intuitive value of paying heed to the "margin of safety",  I don't see the two as alternative measures of risk. In fact, I think that risk measures in valuation and margin of safety play very different roles in investing.

I know that "margin of safety" has a long history in value investing. While the term may have been in use prior to 1934, Graham and Dodd brought it into the value investing vernacular, when they used it in the first edition of "Security Analysis". Put simply, they argued that investors should buy stocks that trade at significant discounts on value and developed screens that would yield these stocks. In fact, many of Graham's screens in investment analysis (low PE, stocks that trade at a discount on net working capital) are attempts to put the margin of safety into practice.

In the years since, there have been value investors who have woven the margin on safety (MOS) into their valuation strategies. In fact, here is how I understand how a savvy value investor uses MOS. The first step in the process requires screening for companies that meets good company criteria: solid management, good product and sustainable competitive advantage; this is often done qualitatively but can be quantifiable. The second step in the process is the estimation of intrinsic value, but value investors are all over the map on how they do this: some use discounted cash flow, some use relative valuation and some look at book value. The third step in the process is to compare the price to the intrinsic value and that is where the MOS comes in: with a margin of safety of 40%, you would only buy an asset if its price was more than 40% below its intrinsic value.

The term returned to center stage a few years ago, when Seth Klarman, a value investing legend, wrote a book using the term as the title, published in 1991. In the book, though, Seth summarizes the margin of safety as "buying assets at a significant discount to underlying business value, and giving preference to tangible assets over intangibles".  Seth is a brilliant thinker (I love the letters he writes to investors..)  and the book has original and interesting ways of looking at risk. I learned a great deal about the ethos of value investing but it did not alter the fundamental ways in which I approached estimating intrinsic value, only the ways in which I used that value.

The basic idea behind MOS is an unexceptional one. In fact, would any investor (growth, value or a technical analyst) disagree with the notion that you would like buy an asset at a significant discount on estimated value? Even the most daring growth investor would buy into the notion, though she may disagree about what to incorporate into intrinsic value. To integrate MOS into the investment process, we need to recognize its place in the process and its limitations.

1. Stage of the investment process: Note that the MOS is used by investors at the very last stage of the investment process, once you have screened for good companies and estimated intrinsic value. Thinking about MOS while screening for companies or estimating intrinsic value is a distraction, not a help.
Proposition 1: MOS comes into play at the end of the investment process, not at the beginning.

2. MOS is only as good as your estimate of intrinsic value: This should go without saying but the MOS is heavily dependent on getting good and unbiased estimates of the intrinsic value. Put a different way, if you consistently over estimate intrinsic value by 100% ore greater, having a 40% margin for error will not protect you against bad investment choices.

That is perhaps the reason why I have never understood why MOS is offered as an alternative to the standard risk and return measures used in intrinsic valuation (beta or betas). Beta is not an investment choice tool but an input (and not even the key one) into a discounted cash flow model. In other words, there is no reason why I cannot use beta to estimate intrinsic value and then use MOS to determine whether I buy the investment. If you don't like beta as your measure of risk, I completely understand, but how does using MOS provide an alternative? You still need to come up with a different way of incorporating risk into your analysis and estimating intrinsic value. (Perhaps, you would like me to use the risk free rate as my discount rate in discounted cash flow valuation and use MOS as my risk adjustment measure... That's an interesting choice and worth talking about ... I know that Buffett claims to do something similar, but he discounts only the cash flows that he believes he can count on, making his cash flows risk adjusted cash flows.)

I know.. I know... There are those who argue that you don't need to do discounted cash flow valuation to estimate intrinsic value and that there are alternatives. True, but they come with their own baggage. One is to use relative valuation: assume that the multiple (PE or EV/EBITDA) at which the sector is trading at can be used to estimate the intrinsic value for your company. The upside of this approach is that it is simple and does not require an explicit risk adjustment. The downside is that you make implicit assumptions about risk and growth when you use a sector average multiple... The other is to use book value, in stated or modified form, as the intrinsic value. Not a bad way of doing things, if you trust accountants to get these numbers right...
Proposition 2: MOS does not substitute for risk assessment and intrinsic valuation, but augments them.

3. Need a measure of error in intrinsic value estimate: If you are going to use a MOS, it cannot be a constant. Intuitively, you would expect it to vary across investments and across time. Why? The reason we build in margins for error is because we are uncertain about our own estimates of intrinsic value, but that uncertainty is not the same for all stocks. Thus, I would feel perfectly comfortable buying stock in Con Ed, a regulated utility where I feel secure about my estimates of cash flows, growth and risk, with a 20% margin of safety, whereas I would need a 40% margin of safety, before buying Google or Apple, where I face more uncertainty. In a similar vein, I would have demanded a much larger margin of safety in November 2008, when macro economic uncertainty was substantial, than today, for the same stock.

While this may seem completely subjective, it does not have to be so. If we can bring probabilistic approaches (simulations, scenario analysis) to play in intrinsic valuation, we can not only estimate intrinsic value but also the standard error in the estimates.
Proposition 3: The MOS cannot and should not be a fixed number, but should be reflective of the uncertainty in the assessment of intrinsic value.

4. There is a cost to having a larger margin of safety: Adding MOS to the investment process adds a constraint and every constraint creates a cost. What, you may wonder, is the cost of investing only in stocks that have a margin on safety of 40% or higher? Borrowing from statistics, there are two types of errors in investing: type 1 errors, where you invest in over valued stocks thinking that they are cheap and type 2 errors, where you don't invest in under valued stocks because of concerns that they might be over valued. Adding MOS to the screening process and increasing the MOS reduces your chance of type 1 errors but increases the possibility of type 2 errors. For individual investors or small portfolio managers, the cost of type 2 errors may be small because there are so many listed stocks and they have relatively little money to invest. However, as fund size increases, the costs of type 2 errors will also go up. I know quite of few larger mutual fund managers, who claim to be value investors , who cannot find enough stocks that meet their MOS criteria and hold larger and larger amounts of the fund in cash.

It gets worse, when a MOS is overlaid on top of a conservative estimate of intrinsic value. While the investments that make it through both tests may be great, there may be very few or no investments that meet these criteria. I would love to find a company with growing earnings, no debt, trading for less than the cash balance on the balance sheet. I would also like to play shortstop for the Yankees and slam dunk a basketball and I have no chance of doing any of those and I would waste my time and resources trying to do so.
Proposition 4: Being too conservative can be damaging to your long term investment prospects.

So, let's call a truce. Rather than making intrinsic valuation techniques (such as DCF) the enemy and portraying portfolio theory as the black science, value investors who want to use MOS should consider incorporating useful information from both to refine MOS as an investment technique. After all, we have a shared objective. We want to generate better returns on our investments than the proverbial monkey with a dartboard... or the Vanguard 500 Index fund...

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Breach of Trust: Bank Valuation after the banking crisis

Until the banking crisis of 2008, investors had made a Faustian bargain, when it came to valuing and investing in banks. Banks were opaque in their public disclosures and investors often had little information on either the risk of the securities held or the default probabilities of loan portfolios. However, investors were willing to accept this opacity and view banks as "safe" investments for two reasons:

1. Banks were regulated in their risk taking: In effect, we were assuming that bank regulators would bring enough scrutiny to the process to prevent banks from taking "rash" risks. (We also assumed that the regulatory authorities had access to far more information that we did and would act accordingly.)
2. Assets (and equity capital) were marked to market: The notion of marking to market was adopted much more quickly in financial service firms than at other sectors. Our distrust of accounting notwithstanding, we assumed that the book values for banks actually were good reflections of market value.

How did this faith in the regulatory overlay get reflected in valuation/investing?

• In intrinsic valuation, banks remained the last holdout for the use of the dividend discount model. Unlike other companies, where our distrust in managers paying out what they could afford to had led us to move on to free cash flows, we retained the faith that bank managers, constrained by the need to meet regulatory capital constraints on one hand and "dividend seeking" investors on the other, would pay out what they could afford to in dividends. (In effect, banks that paid too much in dividends would be punished by the regulators and those that paid too little in dividends would be punished by investors.) 
• In relative valuation, the book value of equity in a bank was given more weight than in other sectors, because it was marked to market and subject to regulatory capital rules. Thus, price to book ratios (with returns on equity as companion variables) were widely used in analysis: a bank with a low price to book ratio and a high return on equity was viewed as a bargain. Worse still, risk averse investors were asked to buy the highest dividend yield banks and assured that these yields were secure.

So, what's changed? First, our faith in both bankers and regulators has been shaken, perhaps to a point of no return. We can no longer assume that having regulatory rules on risk taking will result in sensible risk taking at individual banks. There can be, as there are in other sectors, very risky banks, risky banks, safe banks and very safe banks, as a consequence. Second, the erratic and often ill-thought out dividend policies adopted by banks since the crisis indicates that bank managers, at many banks, use dividends as a blunt weapon. How else can you explain banks with precarious capital ratios that continue to pay and increase dividends, while raising fresh capital in preferred stock at the same time? In fact, it is a sign of the times that the Fed  stepped in to stop a major money center bank from paying dividends, as it did with Bank of America a couple of weeks ago.

So, what do we do now? In intrinsic valuation, we have two choices.
1. One is to use a modified version of the dividend discount model, where we estimate future dividends based upon expected growth and the return on equity that we foresee for a bank, rather than the actual dividends in the last period. Thus, if a bank is expected to grow at 8% and has a return on equity of 10%, it an afford to pay out only 20% of its earnings as dividends:
Payout ratio = 1 - Expected growth rate/ Return on equity
Thus, we can bring in both the quality of a bank's investments and expected changes in regulatory capital rules into the valuation. Increases in regulatory capital requirements will reduce the return on equity and by extension, the capacity to pay dividends.
2. The other and more complicated route requires knowledge of regulatory capital requirements and involves the following steps. You first estimate the growth in the asset base of the bank (growth in loans, for instance). You then follow up by estimating how much regulatory capital will be required to sustain the asset base - that will depend upon the risk in the asset base and the regulatory capital ratio that the bank wants to maintain. (Note that this ratio will not necessarily be at the regulatory minimum since conservative banks will maintain a buffer.) Changes in regulatory capital from period to period than take on the role that capital expenditures do in a more conventional firm and can be used to compute free cash flows to equity:
FCFE for a bank = Net Income - Change in Regulatory capital required for future growth
These FCFE are potential dividends and can be discounted to arrive at fair value. In fact the cost of equity for a bank can then be tied to its regulatory capital buffer: banks that build in a bigger buffer will be safer and have a lower cost of equity whereas banks that are more aggressive in both their asset holdings and regulatory capital policies will have higher costs of equity.

In relative valuation, I think that the use of price to book ratios, in conjunction with return on equity, still makes sense, but risk now has to be treated as a third dimension. The risk itself can be measured using a variety of measures: regulatory capital ratios (higher ratios are safer), losses on bad loans (higher is riskier) or holdings of toxic securities (higher is riskier). A bargain bank will then be one that trades at a low price to book ratio, has a high return on equity and is well capitalized. I expand on both notions in this paper that I wrote a couple of years ago on valuing banks (which subsequently became a chapter in one of my books):
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1798578

I think that there are broader policy implications.

1. More transparency in financial statements: Since banks have broken their side of the bargain with investors, we need to respond by removing the opacity from the financial statements of banks. Banks should be forced to provide far more detail about the riskiness of their security holdings and the default risk in the loans that they make. Much more information needs to be provided about regulatory capital requirements and the policies that banks adopt on regulatory capital should be more transparent.
2. Regulatory capital has to be common equity: Banks that are under capitalized should be required to issue common stock, and face up to their fears of dilution. We need to scrap the notion that preferred stock (a tax-inefficient mismash) or convoluted hybrids (such as these) will be treated as equity, since it exposes us to game playing and worse.

I am not ready to give up on investing in banks. In fact, I am sure that some banks are great bargains and the payoff to finding these, in this time of greater uncertainty, is higher than ever before. But I will be more careful in my assessments of banks and not take numbers for given, just because they have been rubber stamped by regulators and appraised by accountants. That is more a promise to myself than to you!

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Catastrophe and consequences for value

The airwaves have been inundated with news about natural disasters in Japan and their aftermath. Without minimizing the human impact - the thousands who have lost their lives and belongings - and the dangers of a nuclear meltdown, I want to focus on the impact of catastrophes, natural or man-made, on markets and asset values. While each disaster is different, here are some common themes that emerge after the disaster:

a. Our definition of "long time periods" is woefully inadequate: After the quake, which measured 8.9 on the Richter scale and ranked as one of the five strongest in recorded history, it was noted that nothing of this magnitude had been seen in Japan over the last 300 years. Since much of the regulation (of construction and nuclear power plants) had been structured based upon past history, they proved inadequate for the quake. As I look at how much of what we do in corporate finance and valuation is based upon time periods of 80-100 years (if we are lucky) and 10-20 years (if we are not), I wonder how much we are missing as a consequence of our dependence on the past.
 b. Experts are always "surprised" and are exceptionally good at ex-post rationalization: I am not that knowledgeable about earthquakes, but as I watched earthquake experts on the news in the days following the quake, I was struck by how much they reminded me of financial experts after the banking crisis in 2008 in their messages. First, for the most part, they admitted to be surprised by both the magnitude and the location of the quake (just as banking experts were surprised by the magnitude of and players in the sub-prime crisis). Second, they waxed eloquent about how uncertain they were about  long term consequences.... which leaves me wondering why we call them experts in the first place.
c. The doomsayers will have their day in the sun:  In the aftermath of every crisis, there will be people who emerge from the woodwork to say "I told you so". They will be feted as celebrities and treated as oracles, at least for a while. My response is less positive. After all, I have walked by the crazy preacher in Times Square almost every weekday, for close to 25 years, and he has warned me every single time that I have passed him that the end of the world was coming... He did sound prescient on September 12, 2001, but he was bound to, sooner or later. That is the reaction I have to those who preach doom and gloom all the time. They will be right at times but I will not attribute that success to wisdom but to accident....
d. Managing catastrophic risk exposure is much more difficult than managing continuous risk exposure: As companies and investors with Japanese risk exposure struggled with the aftermath of the disaster, I was reminded again of how much more difficult it is to manage and deal with discontinuous risk than continuous risk, especially if that risk occurs infrequently and has large economic consequences. In fact, this is the reason that I argued that companies that think that operating in authoritarian, stable regimes is less risky than operating in democratic chaos are mistaken. It is also the reason why managing exchange rate risk in a floating rate currency is much easier than managing that risk in a fixed rate currency.

I am not a deep thinker and am more interested in the prosaic than in the profound,  but I would like to address two questions that I have been asked in the last two weeks:

i. Are the markets reacting appropriately to the news?
While my instincts, based upon everything I know about behavioral finance, would lead me to say that markets  overreact to crises, I am not convinced by the analysis that I have read that make this argument with the Japanese tsunami. While much of the commentary has noted that the market value lost (in the Nikkei) has been disproportionally large, relative to the cost of of the damage, the definition of cost (as damage to existing assets) seems crimped.

As I see it, there are three levels of cost from any catastrophe:
a. Damage to existing assets: This is measured, either in terms of book value (or what was originally spent to build or acquire these assets) or replacement cost (to replace the damaged assets).
b. Loss of earnings power: The true value lost in a catastrophe is not the original cost, replacement cost or book value of the assets destroyed but the present value of cash flows lost in future periods as a result of the loss. Thus, when a factory with a book value or replacement cost of $50 million collapses, the value lost is the present value of the expected cash flows that would have been generated by the factory. If the firm was generating returns that exceeded its cost of capital, the value from the foregone cash flows will exceed $ 50 million.
c. Psychic damage: Catastrophes create psychic damage by reminding investors not only of their own mortality but of the fragility of the assumptions that they make to justify value. After all, in discounted cash flow valuations, we assume that cash flows  continue in perpetuity for most companies and that big chunks of value (especially for growth companies) come from expectations of excess returns from investments that firms will make in the future. To the extent that catastrophes shake this faith that investors have in the future, they can create significant damage to the value of growth assets.


The change in market value after a catastrophe will reflect these costs to varying degrees.

• For mature businesses that generate little in terms of excess returns, the loss in value will approximate just the damage to existing assets (since the present value of cash flows should be close or equal to the book value). 
• For mature businesses that generate returns on their investments that exceed the cost of capital, the value loss will be higher than the replacement cost or book value of existing assets and be more reflective of the lost cash flows. 
• For growth firms, the loss in value can be extensive (as expectations of future growth get downgraded) even though they may suffer the least losses to existing assets.

If you are a contrarian or value investor, who believes that the psychic damage is transitory, there is an investment strategy that emerges from the rubble. It is not to invest in the entire market (all Japanese stocks) or in companies that have dropped the most in price (because some  may be mature companies like Tokyo Electric Power that have suffered significant loss of earning power), but to pick those companies where the price drop is more the result of the psychic reaction than the economic costs. (Multinationals like Honda, Toyota and Fuji that are Japanese in origin but have both their revenues and operations spread over the world would be a good place to start looking.) The risk, of course, is that the psychic damage is long term and not easily reversed.

ii. How do you incorporate the risk that catastrophes can occur in the future into valuation models?
If we define catastrophes as low-probability, high-impact events that affect most companies in an economy, there are three ways in which we can incorporate those events into value:
a. Adjust cash flows for an expected insurance cost: The simplest mechanism for building in the potential for catastrophes is to estimate the cost of insuring against catastrophes and building that cost into the expected cash flows. This, in turn, will lower the cash flows and value of every asset. It may be difficult to do for two reasons. The first is that some catastrophes may be uninsurable and getting an estimate of the insurance cost is not easy. The second is that even if there are insurers willing to provide coverage, a large enough catastrophe may render them incapable of backing up their promises (by making them insolvent). Note also that insurance covers only the first of the three levels of costs - damage to existing assets - and provides little protection against the other two levels - loss of expected cash flows and loss in growth asset value.
b. Use a higher risk premium: When buying risky assets, investors attach a risk premium to their required returns- an equity risk premium in the equity market and default spreads in the bond market. Since catastrophes affect entire markets, one way in which investors can build their likelihood (and consequent damage) into value is by charging higher risk premiums. As a consequence, the potential for catastrophe will have a much larger effect on risky, high growth firms than on safer,  mature companies. (The higher risk premium will push up costs of capital for all firms, but growth firms will be more affected since they get more of their value from cash flows way into the future.) To me, this seems to be the most viable option, especially when faced with risks that occur rarely, have large effects and are difficult to quantify in cash flow terms. I had an extended post on this a few months ago.
c. Allow for a higher probability of truncation risk: As I noted earlier, we value companies assuming cash flows in perpetuity (or at least for very long time periods), and catastrophes can put firms at risk of default or distress. When valuing companies (especially those with significant debt or other obligations), we should not only be more cautious about long term assumptions but also explicitly build into value, the likelihood that the firm will not survive.
 

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A tide in the affairs of men...

In my last post, I noted how difficult it is to separate luck from skill in  both investment and corporate finance.  While I remain leery of stock picking success stories (and believe me when I say I hear dozens each week), I continue to admire successful businesses of all stripes, from the bagel shop in my town that manages to sell out every day to Facebook in the social media world.

It is not that luck does not play a role in business success. In fact, most successful individuals and businesses can point to a stroke of good luck that got them started.  Microsoft was lucky that IBM allowed it to write the code that made the first personal computers work and Apple was lucky that music companies were too bullheaded to deviate from their traditional sales model of bundling a dozen songs on an album and forcing people to buy the entire package. It is what great companies do with that initial lucky break that set them apart: when they get lucky, they take that success and build on it. Most other businesses, however, view good luck as a windfall, report higher earnings for the year, but have little to show for it in the long term.

In fact, this was the reason I wrote my book on strategic risk taking. If the essence of risk taking is that you are going to be right some of the time and wrong the rest of the time, here is what I see separating good risk takers from bad ones. When good risk taking organizations get lucky and see upside from risk taking, they find ways to build on that upside. When they are confronted with unpleasant surprises, they manage to minimize their losses and move on. In option terminology, successful risk takers create their own call options to augment upside risk and put options to minimize downside risk. Of course, I am not the first to recognize this. Here is one of my favorite quotes from Shakespeare:
There is a tide in the affairs of men.
Which, taken at the flood, leads on to fortune;
Omitted, all the voyage of their life
Is bound in shallows and in miseries.
On such a full sea are we now afloat,
And we must take the current when it serves,
Or lose our ventures.
Brutus had a splendid grasp of risk taking (though I don't quite know where to put the stabbing of Julius Caesar in the risk taking scale).

Put in less lofty terms, each of us will be blessed with good luck in our investment and business endeavors at some point in time. What we do with that luck will determine whether it leaves a lasting mark or not. In the same vein, each of us will also be unlucky at some point in time and how prepared we are for that contingency will determine whether it will bring us down or just dent us.

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Luck versus skill: How can you tell?

A hedge fund manager doubles her investors' money over the course of a year.. A company's stock increases four fold over the course of six months.... these are not unusual news stories but they give rise to one of those enduring questions in finance: Was it luck or skill? The answer of course is critical. If it was "luck", we should not be giving the hedge fund manager 2% of our wealth and 20% of the profits. If it was skill, the company's managers deserve not just a huge thank you but commensurate financial rewards.

As always in finance, there are two extreme outlooks. At one end, there are those who view any superior performance as evidence of skill and extended superior performance as almost super natural. At the other end, there are those who who contend that it is all "luck" and that portfolio managers have any "discernible skill". As an illustration, Fama and French have a damning article on active portfolio management, where they note that all of the excess returns in practice can be explained by randomness:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1356021
In their assessment, all "superior performance" in portfolio management  can be attributed to luck. Here is a more recent paper by Andrew Mauboussin and Sam Arbesman that argues that there is some evidence of differential skill:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1664031
Needless to say, this is an issue where researchers have disagreed and continue to do so.

You may disagree with the broadness of the Fama/French conclusions (and I do), but they do point out how difficult it to differentiate lucky winners from skillful winners. To understand why, it is best to look at an arena where the differentiation between luck and skill is easier: sports. Even those who don't like Sachin Tendulkar, Lionel Messi, Tiger Woods or Kobe Bryant have to admit that they have skills the rest of us don't possess and that their success cannot be attributed to luck.  So, why is it so easy to separate skill from luck in sports and not so in finance? Separating luck from skill is easiest when:

a. Success is clearly defined: In basketball, you either make a basket or you do not. In cricket, you are out or you are not. In golf, you make par or you do not. In soccer, you score a goal or you do not.  An "almost a basket" or "almost par" can be a chatting point with a friend but does not count.

b. It is difficult to have a successful outcome with just luck: I will make a confession. I cannot shoot par on a golf course, make a three pointer in basketball or score a goal in soccer, even with luck.  I am awed when I see people do these things, since I know it requires skills that I do not have.

c. Number of trials: Professional sports players get hundreds of chances to show their wares, and luck very quickly drops to the wayside. You may make one three-pointer in the gym, with sheer luck, but if you were asked to shoot a few hundred three pointers, your limitations would be clear to all. There is no way that luck can explain the hundreds of sub-par rounds that Tiger Woods had (when he was a golfer and not a celebrity), the runs that Sachin scored for India, the points (and championships) for Kobe and the goals that Messi has scored for Argentina (and Barcelona) over time.

Looking at finance through these lens, it is easy to see why it is so difficult to separate luck from skill:

a. Success is not clearly defined: Is a portfolio manager who makes money for his investors a success? What about one who beats the S&P 500 each year? Is a company that delivers returns that outstrip the rest of the sector a success a "good" company? The very fact that we have to think about our answers to these questions tells you something about "success" in finance. To be successful, you have to beat your benchmark, after controlling for risk. However, since risk is a subjective measure, it is entirely possible for a portfolio manager to be classified as a success by one evaluator and not by another. With hedge funds and private equity managers, it becomes even more so, since the net risk exposure is often tough to measure.


b. It is easier being successful with just luck in finance:  I would not bet my house that my portfolio selections will deliver higher returns in the next year than those of my neighbor, who picks stocks based on astrological signs and has the financial sense of a dodo, or of my 11-year old son, who has never looked at the Wall Street Journal. As I note in my valuation class, there is no justice in the investing world. You can do everything right (collect the data, analyze it carefully, make reasoned judgments) and go bankrupt... and you can be absolutely cavalier in your investment judgments and make millions.

c. Too few trials: Can you be lucky once? Sure! How about 4 times in a row? Yes.. How about 15 years in a row? Not as easy, but with hundreds of people trying, a few will.... One problem that we face in portfolio management and corporate finance is that we get to observe outcomes too infrequently, making it difficult to separate luck from skill.

I don't mean to leave you in limbo. After all, most of us want to separate luck from skill in finance. So, here are the things that I would look for in a "skillful" portfolio manager or a CEO:

a. Consistency: As an investor, I don't want to just see that you beat the market, on average, but that you beat it consistently for an extended period. I am more likely to attribute your success to skill, if you beat the market by 2-3% each year for 15 years than if you beat the market by an average of 2-3%, with more variability and poor years intermixed, over that period.

b. Transparency: I tend to mistrust success, when that success is based on portfolio managers self-appraising the values of the properties and investments in their portfolios. A hedge fund may claim it made a 30% return last year, but if that return was based on appraised values for non-traded assets, did it really make 30%? If your success is based on skill and not luck, you should have as open a process as possible for measuring returns and risk and allow investors to observe that process.

c. Awareness: If you beat the market, you are pulling off a difficult feat, since there are literally millions of investors attempting to to do the same thing. If it is not luck that is causing the superior performance, you have to be able to point to something that you are bringing to the table that others are not - better information, better analytical tools, a longer time horizon or a very different tax status. If you don't know why you are beating the market, rest assured that you will not be beating the market for very long..... In my experience, the most skillful investors tend to not only be the most self aware (of their strengths and limitations) but also have no qualms about letting you know what their investment philosophy is. (Note that you can be secretive about investment strategies but you give away little by sharing an investment philosophy).

d. Humility: This is my subjective input to the process. In my years in the market, I have discovered that it is the lucky investors (with no skill) who are most hot headed and arrogant about their skills, and that skillful investors recognize how much luck can affect their final returns.

Here is my bottom line for a skillful portfolio manager or CEO: I am looking for a person who has been able to deliver performance that beats the competition consistently over many years, can tell you why he or she can pull this off and is willing to concede that luck could explain the whole phenomena....

Update: A couple of you have drawn my attention to Mike Mauboussin's excellent and extended discussion of the topic.
www.lmcm.com/pdf/UntanglingSkillandLuck.pdf
Mike is one of my favorite thinkers in finance - he is always original and manages to think across disciplines - and I don't know how I missed this piece but he says what I was trying to say much better than I ever could, and in much more depth. Do read it!

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