Quantitative Methods – Page 22

The Rules, Part XXVII, and, Seeming Cheapness vs Margin of Safety

Posted on 29 December 201129 December 2011 by David Merkel

The market takes action against firms that carry positions bigger than their funding base can handle.? Temporarily, things may look good as the position is established, because the price rises as the position shifts from being a marginal part of the market to a structural part of the market.? After that happens, valuation-motivated sellers appear to offer more at those prices.? The price falls, leading to one of two actions: selling into a falling market (recognizing a true loss), or buying more at the “cheap” prices, exacerbating the illiquidity of the position.

When an asset management firm is growing, it has the wind at its back.? As assets flow in, they buy more of their favored ideas, pushing their prices up, sometimes above where the equilibrium prices should be.

As Ben Graham said, “In the short run, the market is a voting machine, but in the long run it is a weighing machine.”? The short-term proclivities of investors usually have no effect on the long run value of companies.? Rather, their productivity drives their long-term value.

There have been two issues with asset managers following a “value” discipline that have “flamed out” during the current crisis.? One, they attracted hot money from those who chase trends during the times where lending policies were easier, and the markets were booming.? And often, they invested in financials that looked cheap, but took too much credit risk.? Second, they invested in companies that were seemingly cheap, rather than those with a margin of safety.

My poster child this time is Fairholme Fund.? Now, I’ve never talked with Bruce Berkowitz; don’t know the guy at all.? Every time I read something by him or see a video with him, I think, “Bright guy.”? But when I look at what he owns, I often think, “Huh. These are the stocks you own if you are really bullish on financial conditions.”

Yesterday, I saw a statistic that said that his fund was 76% invested in financial stocks as of 8/31.? Now I believe in concentrated portfolios, and even concentrated by sector and industry, but this is way beyond my willingness to take risk.? From Fairholme’s 5/31/2011 semi-annual report to shareholders, here are the top 10 holdings and industries:

Aside from Sears, all of the top 10 holdings are financials.? And, of those financials that I have some knowledge of, they are all what I would call “complex financials.”

In general, unless you are a heavy hitter, I discourage investment in complex financials because it is hard to tell what you are getting.? Are the assets and liabilities properly stated?? Financial companies are just a gaggle of accruals, and the certainty of having the accounting right on an accrual entry decreases with:

Company size (the ability of management to make sure values are accurate or conservative declines with size)
Rapidity of the company’s growth
Length of the asset or liability
Uncertainty over when the asset will pay out, or when the liability will require cash
Uncertainty over how much the asset will pay out, or when how much cash the liability will require

It’s not just a question of whether the assets will eventually be “money good.”? It is also a question of whether the company will have adequate financing to hold those assets in all environments.? For financials, that’s a large part of “margin of safety,” and the main aspect of what failed for many financials in the last five years.

Another aspect of “margin of safety” for financials is whether you are truly “buying it cheap.”? All financial asset values are relative to the financing environment that they are in.? Imagine not only what the assets will be worth if things “normalize,” or conditions continue as at present, but also what they would be worth if liquidity dries up, a la mid-2002, or worse yet, late 2008.

Also remember that financials are regulated, and the regulators tend to react to crises, often making a marginal financial institution do something to clean up at exactly the wrong time, which puts in the bottom for some set of asset classes.? Now, I’m not blaming the regulators (or rating agencies) too much; no one forced the financial company to play near the cliff.? Occasionally, for the protection of the system as a whole, the regulator shoves a financial off the cliff.? (or, a rating agency downgrades them, creating a demand for liquidity because of lending agreements that accelerate on downgrades.)

Finally, think about management quality.? Do they try to grow rapidly?? That’s a danger sign.? There is always the tradeoff between quality, quantity, and price.? In a good environment, you can get 2 out of 3, and in a bad environment, 1 out of 3.? Managements that sacrifice asset quality for growth are not good long run investments, they may occasionally be interesting speculations at the beginning of a new boom phase.

Do they use odd accounting metrics to demonstrate performance?? How much do they explain away one-time events?? Are they raising leverage to boost ROE, or are they trying to improve operations?? Do they try to grow through scale acquisitions?

Are they willing to let bad results show or not?? Even with good financial companies there are disappointments.? With bad ones, the disappointments are papered over until they have to take a “big bath,” which temporarily sets the accounting conservative again.

The above is margin of safety for financials — not just seeming cheapness, but management quality and financing/accounting quality.? They often go together.

Fairholme’s annual report should come out somewhere around the end of January 2012.? What I am interested in seeing is how much of his shareholder base has left given his recent disappointments with AIG, Sears Holdings, Bank of America, Citigroup, Goldman Sachs, Morgan Stanley, Brookfield, and Regions Financial.? Even the others of his top 10 have not done well, and the fund as? a whole has suffered.? Mutual fund shareholders can be patient, but a mutual fund balance sheet is inherently weak for holding assets when underperformance is pronounced.

(the above are estimates, I may have made some errors, but the data derives from their SEC filings)

Now, we eat dollar-weighted returns. Only the happy few that bought and held get time-weighted returns.? And, give Fairholme credit on two points (though I suspect it will look worse when the annual report comes out):

A 9.9% return from inception to 5/31/2011 is hot stuff, and,
A 6.0% dollar-weighted return is very good as well.? Only losing 3.9% to mutual fund shareholder behavior is not great, but I’ve seen worse.

This is the problem of buying the “hot fund.”? Once a fund becomes the “Ya gotta own this fund” fund, future returns on capital employed get worse because:

It gets harder to deploy increasingly large amounts of capital, and certainly not as well as in the past.
Management attention gets divided, because of the desire to start new funds, and the complexity of running a larger organization.
When relative underperformance does come, it is really hard to right the ship, because assets leave when you can least handle them doing so.? The manager has to think: “Which of my positions that I think are cheap will I liquidate, and what will happen to market prices when it is discovered that I, one of the major holders, is selling?”

That is a tough box to be in, and I sympathize with any manager that finds himself stuck there.? It can be a negative self-reinforcing cycle for some time.? My one bit of advice would be: focus on margin of safety.? If you do, eventually the withdrawals will moderate, and then you can work to rebuild.

Returns on Equity Amid the Financial Crisis, Response

Posted on 23 December 2011 by David Merkel

I appreciate constructive criticism.? I particularly appreciate comments at this blog, regarding my long article on how return on equity changed during the financial crisis.

The reviewer said,

In a world in which I didn?t have only 20 minutes to read, analyze and write about this paper, I?d like to think through his model choices. I would feel much more comfortable on this point if he accepted the Russ Roberts Science challenge and have a section discussing the process by which he arrived at the process by which he arrived at his conclusions.

Look, I have a policy.? I don’t do specification searches.? If I don’t get reasonable results in the first two tries, I abandon the project.? As it was in this case, I only did one pass through the data.? I was testing for the idea that state or national governmental policy might affect book or market value returns, after adjusting for market sector.

He later commented,

I?d have two comments:

1. What?s the point of decomposing them, then?

2. Can?t you just attribute ALL variance of corporates to ?historical accident?? Can there be no policy implications?

On point #2, I?d defend Merkel by saying that policy implications need a big enough sample that you can reasonably hold other factors constant. You?d need a dataset of every industry in every state over every conceivable macro-economic environment, then control for those other factors. Same applies for analyzing different countries.

The point of decomposing them is that you don’t know in advance what the result will be.? I only did one pass at the data (please ask academic economists what they do), in this case, it showed that after adjusting for sectors and general economics (time), the states one was in did not matter much, as those that did well did not move to seek lower tax environs.

The piece I did last year did not attribute everything to historical accident.? This year, I was surprised to find that few successful companies had not moved to lower tax/regulation jurisdictions.

I did not know what the decomposition would lead to — that was a major reason for doing it.? If there had been some indication that companies in the US sought lower tax or regulation states, I would have published that, but it was not so, in aggregate.? I does not matter that the result was ordinary.? Once I start the problem, if I come to any understandable result, consensus or non-consensus, I publish it.

Now in truth, I don’t think the paper was one of my best efforts.? I would like to have set error bounds, but I didn’t have access to good software.? I also would have liked to use a better database, like the CRSP database, but that was not available.? Given my lack of resources, it was the best I could do.? Anyway, anyone with more constructive criticisms, I welcome them.

Returns on Equity Amid the Financial Crisis, Redux

Posted on 20 December 2011 by David Merkel

To have a full version of my article, with the equations that explain my reasoning, Returns on Equity amid the Financial Crisis.?? Thanks to all who read it.

Returns on Equity Amid the Financial Crisis

Posted on 20 December 2011 by David Merkel

I wrote the following for the 2012 Baltimore Business Review.? When it is publicly available on the web, I will highlight it.? For now, I will offer you the unedited version of my paper that will be published there:

-==-=-=-=-=-=-=-=–==-=–=-=-=-==–==-=–=-=-==-=-=-=-=-=-=-=-=-=-=-=-=-

Returns on Equity amid the Financial Crisis

Abstract

From 2005-2010, the change in public company returns on book equity [ROE] was wrenching during the financial crisis.? The results were uneven by sectors, and even by geography, for stocks traded in US equity markets.? This paper looks at the differences, and attempts to explain why there was so much variation by sector and geography.? After that, the paper attempts to explain the correlation between changes in ROE and stock returns, by year, sector, and geography.

Introduction

Since 2005, equity markets have seen a boom, a bust, and a tepid recovery. Financial stocks seem to have had the worst of it, but is that really true?

This paper attempts to disaggregate the differing effects of geography (countries/US states), and economic sector over time to try to understand how the boom, bust and recovery have affected public companies.

Part 1 ? Return on Equity

Method

This study excluded stocks with market capitalizations under $100 million at the end of the study period.? It also excluded miscellaneous financial companies such as exchange-traded products, closed-end funds, and special-purpose acquisition companies, because they don?t have operating businesses.? That left 3,796 companies that trade on US exchanges available for the analysis.

Given the tendency for businesses in states and countries to be concentrated in one or two sectors, a minimum was imposed for states and countries to be analyzed individually.? Countries with fewer than four companies trading on US exchanges were placed in the ?other? country category, and states with fewer than four companies trading on US exchanges were placed in the ?other? state category.

Over the years 2005-2010, data regarding book equity, net income, market capitalization, market price, share count, and total returns were gathered, and aggregated by geography (Country if non-US, state if US), sector, and year.

Using Ordinary Least Squares Regression, the following relationship was estimated:

Where:

?is the set of dummy variables for geography.
?is the set of dummy variables for sectors.
?is the set of dummy variables for the years 2005-2010.
?is the contribution to return on equity due to geography.
?is the contribution to return on equity due to sector.
?is the contribution to return on equity due to year.
?is the net income for a given geographic area, sector, and year.
?is the book equity for a given geographic area, sector, at the prior year end.
?is the error term for a given geographic area, sector, and year.

The reasons for using this sort of equation is twofold: first, by using dollar figures rather than earnings per share and book value per share, large companies are given their proper weight versus smaller companies.? Second, it allows for the effects of ROE changes by geography, sector and year to be separated.

In an analysis where there are multiple groups of dummy variables, at most one set of dummy variables can be complete if there is no intercept term, and no set can be complete if there is an intercept term.? If not, the regression will fail.? The choice of what to omit is arbitrary, and does not affect the relative relationships within a set of dummy variables.? For the purposes of this paper the sector dummy variables were left complete, and the coefficients on the first geographic area (Argentina) and the first year (2005) were set to zero.

Results

The R-squared of the regression was 55.7%, which has a prob-value of greater than 99.9%.

Here are the results of contribution to ROE by country:

18.1%	Mexico
16.9%	Chile
15.4%	Other Nations
15.1%	Brazil
14.1%	Australia
13.4%	Spain
13.2%	India
10.6%	Bermuda
10.6%	Hong Kong
7.3%	Greece
7.1%	Russia
6.5%	Taiwan
6.3%	Netherlands
6.3%	Italy
6.3%	Switzerland
6.1%	China
5.9%	Norway
5.8%	Canada
5.1%	Sweden
5.1%	Germany
4.1%	France
3.7%	United Kingdom
2.8%	United States
1.9%	Singapore
1.9%	Israel
1.0%	Cayman Islands
0.6%	Japan
0.1%	South Korea
0.0%	Argentina
-0.2%	Puerto Rico
-1.4%	Finland
-3.1%	Ireland
-3.2%	Luxembourg
-6.3%	South Africa

The United States is included for comparison purposes as the weighted average of the contribution to ROE by states.? There was not a separate variable for the US in the analysis.

As Latin America moved toward freer markets, with growing middle classes, their contributions to ROE were relatively high.? In general, resource rich nations tended to have higher contributions to ROE.

Mexico?s contribution to ROE was led by communication companies Telmex, America Movil, and Grupo Televisa and consumer-oriented companies like Coca-cola Femsa, FEMSA, and Wal-Mart de Mexico.? A growing middle class pushed up demand for these companies.

Chile?s contribution to ROE was led by the utilities Enersis and Empresa Nacional de Electricidad, the banks Banco Santander Chile and Banco de Chile, and chemical company Sociedad Quimica y Minera de Chile.? A growing economy boosted demand for electrical power, their banks didn?t make the mistakes made by most of the rest of the developed world, and Sociedad Quimica y Minera was in the ?sweet spot? for the chemicals it produced, particularly fertilizers, and lithium which goes into rechargeable batteries.

Brazil?s contribution to ROE was led by the energy giant Petrobras, the diversified mining company Vale, and the banks Banco Santander (Brasil), Itau Unibanco Holding, and Banco Bradesco.? Global demand for crude oil, iron ore, and other resources boosted the contributions to ROE with Petrobras and Vale.? Brazil?s banks also didn?t make the mistakes made by most of the rest of the developed world.

On the negative side, contributions to ROE in Finland were held down by Nokia, where they fell behind consumer trends with cell phones and other portable wireless devices.? Ireland was held back by banking sector, which lent too much on Irish residential property, amid other errors.? Luxembourg had ArcelorMittal, which slumped with the global steel industry as prices for coking coal and iron ore rose.? South Africa had the worst contribution to ROE as a country because of the heavy weight their economy has in basic materials.? Basic materials was a strong sector, but South Africa was concentrated in one the weakest ROE industries in that sector, gold mining.

Here are the results of contribution to ROE by US state:

18.6%	Washington
16.9%	Arkansas
13.0%	District of Columbia
11.3%	Minnesota
10.0%	Connecticut
10.0%	Oregon
8.9%	Rhode Island
8.2%	New Jersey
7.8%	Kentucky
6.7%	Nebraska
6.6%	Indiana
6.2%	California
6.1%	Georgia
5.5%	Wisconsin
5.4%	Missouri
5.1%	Iowa
5.0%	Texas
4.4%	Tennessee
3.2%	Illinois
3.1%	Florida
2.9%	Maryland
2.8%	US Average
2.5%	North Carolina
1.2%	New York
1.2%	Pennsylvania
1.1%	South Carolina
0.8%	Other
0.6%	Ohio
-0.4%	Utah
-0.5%	Nevada
-1.3%	Louisiana
-2.3%	Arizona
-3.6%	Colorado
-4.6%	Massachusetts
-5.6%	Alabama
-7.9%	Oklahoma
-10.3%	Virginia
-31.9%	Kansas
-83.6%	Michigan

To some degree, historical accidents help explain why some states have high contributions to returns on equity, and others low contributions.? Washington State has Microsoft, Amazon, and Costco, all of which started out there.? Michigan has General Motors, Ford, and Chrysler; the automobile industry has long been a big part of the state economy.

The contribution to ROE of Arkansas can be entirely attributed to Wal-Mart.? Washington, DC can largely be attributed to Danaher, though Fannie Mae pulled the contribution to ROE down considerably as it failed in 2008.

The results of Kansas are dominated by Sprint Nextel, which has been a weak competitor in wireless telephony, though YRC Worldwide also had some impact on the low contribution to ROE as it was too acquisitive heading into a major recession.? Virginia has many strong companies, but Freddie Mac pulled the contribution to ROE down with it failure in 2008.

Companies don?t move often, so attributing the differing contributions to ROE to state policies is unlikely.? In the extreme cases listed above, all of the companies listed had been headquartered in their respective states for a long time, and most had been started there.

Here are the results of contribution to ROE by sector:

25.91%	Consumer Non-Cyclical
23.31%	Basic Materials
20.20%	Energy
18.10%	Health Care
14.59%	Utilities
14.24%	Capital Goods
14.07%	Technology
10.56%	Services
10.20%	Consumer Cyclical
9.52%	Financial
4.72%	Transportation
-5.58%	Conglomerates

The end of the first decade of the new millennium was characterized by strong development around the world, with many nations clamoring for resources and non-cyclical consumer goods, which why the contribution to ROE by sector was led by Consumer Non-Cyclicals, Basic Materials, and Energy.

Conglomerates are the smallest sector, at 0.3% of total book equity, so it is difficult to draw conclusions about why it had the lowest contribution to ROE.? That said, it is difficult to manage disparate enterprises for organic operating returns.? Increases in energy costs hurt transportation ROEs, which unlike utilities, have a harder time passing the price increases through.

Financial stocks saw their contribution to ROE drop because of the financial crisis.? The contribution to ROE includes two great years 2005-2006, two horrible years 2007-2008, and two years of recovery.? The contributions to ROE in the financial sector in 2007-2008 more than erased the gains made earlier in the decade.

Contribution to ROE for Consumer Cyclicals were damaged by bad results in the Automobile industry and slumping demand as the economy went into a recession in 2008, and had a rather weak recovery in 2009-2010.

Here are the results of contribution to ROE by year:

0.00%	2005
2.04%	2006
-1.28%	2007
-18.37%	2008
-8.06%	2009
-3.72%	2010

Contribution to return on equity rose 2% over 2005 levels in 2006.? In 2007, as the stock market reached new highs and began to fall in the fourth quarter of 2007, partially because the contribution to ROE fell below 2005 and 2006 levels.

In 2008, as the financial crisis arrived, the contribution to ROE plummeted.? Much of the effect was concentrated in financial stocks, but the contribution to ROE for the market as a whole fell 17%.? In 2009 and 2010, as the recovery from the crisis progressed contribution to ROE rose each year, but still remained below the contribution to ROE that existed during the boom years 2005-2007.

Part 2 ? Total Returns

Method

The same stocks as in the first section, and the same methods were used to estimate the following relationship, using Ordinary Least Squares:

Where:

?is the set of dummy variables for geography.
?is the set of dummy variables for sectors.
?is the set of dummy variables for the years 2005-2010.
?is the contribution to total return due to geography.
?is the contribution to total return due to sector.
?is the contribution to total return due to year.
?is the dollar value of gains or losses for a given geographic area, sector, and year.
?is the market capitalization for a given geographic area, sector, at the prior year end.
?is the error term for a given geographic area, sector, and year.

The dollar value of gains or losses is calculated by the change in market capitalization, plus dividends, less the proceeds of shares issued, plus the cost of shares bought back.

Results

The R-squared of the regression was 76.7%, which has a prob-value of greater than 99.9%.

Here are the results of contribution to total return by country:

216.77%	Israel
24.53%	Chile
17.34%	Singapore
12.44%	Other Nations
11.99%	China
11.34%	Australia
10.37%	Hong Kong
8.32%	Mexico
7.62%	Bermuda
7.15%	Brazil
4.14%	Netherlands
3.41%	Germany
3.24%	Greece
2.32%	Spain
1.93%	Norway
1.72%	Italy
1.62%	United Kingdom
1.61%	Cayman Islands
1.30%	US Average
1.24%	Taiwan
1.08%	India
0.86%	France
0.76%	Switzerland
0.74%	Puerto Rico
0.13%	Finland
0.00%	Argentina
-1.44%	Russia
-3.46%	South Korea
-4.16%	Canada
-4.32%	Japan
-4.44%	Ireland
-6.19%	South Africa
-8.72%	Sweden
-17.49%	Luxembourg

The United States is included for comparison purposes as the weighted average of the contribution to ROE by states.? There was not a separate variable for the US in the analysis.

Looking at the countries at the top and the bottom, Israel benefitted from Teva Pharmaceutical, Check Point Software Technologies, and a scad of little technology companies that soared in value.? Singapore was led by Avago Technologies which has been seeing strong growth in demand for their analog semiconductor devices.

Chile, as mentioned above, contribution to total return was led by the utilities Enersis and Empresa Nacional de Electricidad, the banks Banco Santander Chile and Banco de Chile, and chemical company Sociedad Quimica y Minera de Chile.? In addition, Lan Airlines grew their net income by 150% over the whole of the study period, as a growing middle class flew more often.

Ireland, Luxembourg and South Africa were low on the contribution to ROE by countries.? Ireland?s contribution to total returns was held back by its banking sector, as mentioned previously.? The same applies to Luxembourg with ArcelorMittal.? And again, South Africa had a low contribution to total returns as a country because of the heavy weight their economy has in basic materials.? Basic materials was a strong sector, but South Africa was concentrated in one the weakest industries for total returns in that sector, gold mining.

Sweden had three large companies Ericcson (Telecommunications Equipment), Volvo (Automobiles) and Swedbank (Banking) that underperformed.? Volvo and Swedbank were in weak industries given the financial crisis, while Ericcson underperformed versus competitors in its industry.

Note that the order of the lists of contribution to ROE and contribution to total return across are similar.? The correlation of the two sets of coefficients is 1.8% — statistically indistinguishable from zero, but the rank correlation of the two sets is 62.7%, which is significantly greater than zero with 95% certainty.? The high coefficient on Israel?s contribution to total returns throws the ordinary correlation coefficient off; without Israel, the correlation would be 64.5%.

Thus it seems that contribution to ROE and contribution to total return are related across countries.

Here are the results of contribution to total return by US state:

19.12%	Oregon
15.18%	Kentucky
13.85%	Iowa
13.28%	Michigan
12.77%	Nebraska
12.53%	Arizona
11.52%	Rhode Island
9.35%	Colorado
9.24%	Texas
8.10%	Alabama
7.18%	Louisiana
7.02%	Oklahoma
6.26%	Illinois
5.58%	California
5.01%	New Jersey
4.58%	Massachusetts
3.49%	Missouri
2.62%	Maryland
2.21%	South Carolina
2.17%	Minnesota
1.56%	Utah
1.40%	Washington
1.30%	US Average
-0.02%	Wisconsin
-0.49%	Connecticut
-1.11%	New York
-1.39%	Arkansas
-2.02%	Indiana
-3.13%	Pennsylvania
-4.49%	Florida
-5.21%	Ohio
-7.04%	Tennessee
-7.76%	North Carolina
-8.19%	Kansas
-8.42%	Nevada
-12.06%	Georgia
-19.45%	Other
-21.02%	Virginia
-33.73%	District of Columbia

Oregon?s contribution to total return was high because of Nike and Precision Castparts.? Both have been based in Oregon since their founding.? The same can be said of Yum! Brands, Humana, and Brown Forman in Kentucky.? Yum Brands began with Pepsi?s purchase of Kentucky Fried Chicken, which was founded by Colonel Sanders out of home in Corbin, Kentucky in 1930.? Brown Forman was started in Kentucky in 1870 by George Garvin Brown.

Terra Nitrogen, LP was an Iowa firm from its founding until its parent company was acquired by CF industries in mid-2010.? It is counted as an Iowa firm for this study, but is now based in Illinois.

DC and Virginia have the lowest contributions to total returns because of Fannie Mae and Freddie Mac, respectively.? Georgia had a low contribution to total returns, largely due to SunTrust Banks, which holds the dubious distinction of receiving four installments of bailout cash.? Nevada had a low contribution to total returns because of their high exposure to the casino/gaming industry, which did poorly during and after the financial crisis.

All of these companies are historical accidents.? They were based in their states since their founding.

The state lists on contribution to ROE and contribution to total return across are not similar.? The correlation of the two sets of coefficients is -10.68% — statistically indistinguishable from zero.? The rank correlation of the two sets is 26.68%, which is also not significantly greater than zero with 95% certainty.

It seems there is no relationship at the state level between contribution to ROE and contribution to total return.

Here are the results of contribution to total return by Sector:

34.22%	Basic Materials
33.86%	Consumer Non-Cyclical
33.13%	Conglomerates
30.87%	Transportation
27.49%	Utilities
24.38%	Technology
23.69%	Consumer Cyclical
22.88%	Services
21.94%	Energy
19.80%	Health Care
19.51%	Capital Goods
15.49%	Financial

The lists between contribution to ROE and contribution to total return by sector are different.? The correlation coefficient between them is -0.50%, which is virtually zero.? But excluding the two smallest sectors, Conglomerates and Transportation, which have noisy data with only 2% of the total market capitalization, the correlation would be 71.51%, which would be statistically different from zero with 95% probability.? Thus it seems that contribution to ROE and contribution to total return are related across sectors.

The low contributors to total return by sector are led by Financials and Capital Goods, both of which did poorly in the recent crisis and the aftermath.? Basic Materials and Consumer Non-Cyclicals led the high contributors to total return by sector, as a growing global middle class created demand for commodities and staple consumer goods.

Here are the results of contribution to total return by year:

0.00%	2005
-5.35%	2006
-11.15%	2007
-67.18%	2008
5.51%	2009
-12.47%	2010

The contributions to ROE and contributions to total return by year are very similar, though the contribution to total return is far more volatile.? Also, total return anticipates changes in ROE, exacerbating the fall in 2007 and 2008, and anticipating tougher market conditions in 2011 in the results of 2010.

Without adjustment for leading effects, the correlation of the two series is 80.83%, which is different from zero with greater than 95% probability.? Thus it seems that contribution to ROE and contribution to total return are related across years.

In a regression of the two series, where ROE contribution by year is the independent variable, and total return contribution by year is the dependent variable, the beta of the regression was 2.86, with a 94% prob-value? for the coefficient and the regression as a whole.

That total returns should be levered 2.86 times to changes in ROE should surprise no one.? Markets anticipate, and change disproportionately, because they can?t tell whether changes are temporary or permanent, and so a multiple near 3 splits the difference.

Avenues for Further Study and Conclusion

The researcher did not use the CRSP database, because he had no easy access to it.? This study could be done over far more years and with greater precision.

The markets during 2005-2010 rewarded companies the served the growing global middle class, and aided the growth of the developing world.? It punished financial companies, and cyclical companies that did not have significant markets in the developing world.

In general, US state policies did not directly affect the financial results.? The best and worst companies by state were generally long term residents of the state in question.? Historical accidents dominate over companies that choose to move to other jurisdictions.

In general, contributions to ROE and total returns are related, but contributions to total returns lead contributions to ROE.? Markets anticipate changes in future profits.

Disclosure: David Merkel and clients of Aleph Investments own shares of Wal-Mart and Petrobras, as of the date this was originally written.

The Gold Medal Gold Model

Posted on 13 December 201113 December 2011 by David Merkel

Eddy Elfenbein is a clever guy; he put together a model of gold prices that fits the data very well.? Tonight, I will share my own variation on the model, and try to give an intuitive explanation of why it works.

Ask yourself this: where does investor put his money if he wants to stay safe?? Most people are savers not investors, so ideally they would want to put their money on deposit and earn a real return with the ability to access their money at any time.? Then there is the alternative asset, gold.? Gold is a hedge against inflation, but it throws off no interest.? But at some level of real return, savers begin to conclude that they aren’t earning all that much, so they may as well hold gold.? Vice versa when real rates rise.

One more thing: gold doesn’t benefit from productivity increases, as stocks do.? Rapidly increasing productivity makes gold less attractive than stocks.

Eddy’s model boils down to this (in my implementation):

Percentage change in gold price = Multiplier * Percentage change in (Deflator Index / Real return Index)

where the Real Return index compounds three month T-bill yields less inflation via the 12-month CPI-U in arrears.

Here is how well the mode works, since 1970:

The first model attempts to minimize absolute dollar price differences between actual and model.? The second attempts to minimize the ratio between actual and model prices.? Both have R-squareds over 90%.

The deflator return is constant in percentage terms.? For the two models it is around 2.3%/yr, which is not far from productivity gains.

As for the multiplier, it is near six.? The multiplier is like a duration figure with bonds.? What this means is that the percentage change in real interest rates, three-month T-bills less CPI-U inflation, is projected to persist for six years.? Six years is a reasonable figure, because monetary policy changes slowly, but not glacially.

Now, at present levels of real interest rates, with T-bill yields near zero, and the CPI above 3%, it implies a gold price rising at 3% per month.? If inflation stays where it is and the Fed holds good on its promises, that means a gold price in the $3000s in mid-2013.

Do I believe this?? Partially.? I own lots of oil stocks, but nothing in metals at all.

Eddy’s model helps to clarify the value of gold.? It is a store of value, as its price anticipates the degradation and strengthening of the dollar, because changes in real rates will persist on average for six years.

Industry Ranks December 2011

Posted on 10 December 201111 December 2011 by David Merkel

I?m working on my quarterly reshaping ? where I choose new companies to enter my portfolio.? The first part of this is industry analysis.

My main industry model is illustrated in the graphic.? Green industries are cold.? Red industries are hot.? If you like to play momentum, look at the red zone, and ask the question, ?Where are trends under-discounted??? Price momentum tends to persist, but look for areas where it might be even better in the near term.

If you are a value player, look at the green zone, and ask where trends are over-discounted.? Yes, things are bad, but are they all that bad?? Perhaps the is room for mean reversion.

My candidates from both categories are in the column labeled ?Dig through.?

If you use any of this, choose what you use off of your own trading style.? If you trade frequently, stay in the red zone.? Trading infrequently, play in the green zone ? don?t look for momentum, look for mean reversion.

Whatever you do, be consistent in your methods regarding momentum/mean-reversion, and only change methods if your current method is working well.

Huh?? Why change if things are working well?? I?m not saying to change if things are working well.? I?m saying don?t change if things are working badly.? Price momentum and mean-reversion are cyclical, and we tend to make changes at the worst possible moments, just before the pattern changes.? Maximum pain drives changes for most people, which is why average investors don?t make much money.

Maximum pleasure when things are going right leaves investors fat, dumb, and happy ? no one thinks of changing then.? This is why a disciplined approach that forces changes on a portfolio is useful, as I do 3-4 times a year.? It forces me to be bloodless and sell stocks with less potential for those with more potential over the next 1-5 years.

I like some technology names here, some energy some healthcare-related names, P&C Insurance and to a lesser extent Reinsurance, particularly those that are strongly capitalized.? I?m not concerned about the healthcare bill; necessary services will be delivered, and healthcare companies will get paid.

A word on banks and REITs: the credit cycle has not been repealed, and there are still issues unresolved from the last cycle ? I am not interested there even at present levels.? The modest unwind currently happening in the credit markets, if it expands, would imply significant issues for banks and their ?regulators.?

I?m looking for undervalued and stable industries.? I?m not saying that there is always a bull market out there, and I will find it for you.? But there are places that are relatively better, and I have done relatively well in finding them.

At present, I am trying to be defensive.? I don?t have a lot of faith in the market as a whole, so I am biased toward the green zone, looking for mean-reversion, rather than momentum persisting.? The red zone is pretty cyclical at present.? I will be very happy hanging out in dull stocks for a while.

P&C Insurers Look Cheap

After the heavy disaster year of 2011, P&C insurers and reinsurers look cheap.? Many trade below tangible book, and at single-digit P/Es, which has always been a strong area for me, if the companies are well-capitalized, which they are.

I already own a spread of well-run, inexpensive P&C insurers & reinsurers.? Would I increase the overweight here?? Yes, I might, because I view the group as absolutely cheap; it could make me money even in a down market.? Now, I would do my series of analyses such that I would be happy with the reserving and the investing policies of each insurer, but after that, I would be willing to add to my holdings.

Do your own due diligence on this, because I am often wrong.? One more note, I am still not tempted by banks or real estate related stocks.? I am beginning to wonder when the right time to buy them as a sector is.? As for that, I am open to advice.

Book Review: What Works on Wall Street (4th Edition)

Posted on 8 December 20118 December 2011 by David Merkel

I previously reviewed the First Edition.? Now it is time for the Fourth Edition.? Rather than do a teardown, I think it would be more useful if I explained how the book can best be used.? Here goes:

There has been a lot of research done on stock returns, the results of which have encouraged investment in:

Cheap stocks relative to book value, earnings, sales, EBITDA, FCF, etc.
Stocks with strong price momentum.
Stocks with strong earnings quality.

And such is true of this book.? And so, I encourage investors to focus on earnings quality, cheapness, and maybe, momentum, which hasn’t done so well of late.? (Probably too many following it.)

Now, the wrong way to use the book is to look at the highest returning strategy of the past, and follow it.? Since they test so many strategies, the one at the top is an accident of the historical period it covered.? Far better to be more humble and use a strategy that borrows from many successful strategies.? In doing that, there is less chance of amplifying the noise of the past.

Quibbles

The danger of this book is data-mining.? The deeper you dig to find what would have worked best in the past, the more you mirror the idiosyncrasies of the past, which does not then reveal the long-term principles that generally work, over intermediate-term periods.

Far better to stick with “pretty good” methods that never reach the top, but usually work.? Don’t be concern about hitting home runs, as much as getting on base regularly.? I say this because it works well for me and my clients.

Who would benefit from this book: Most investors would benefit from this book, if they are careful not to grab for the “brass ring” and imitate the strategy that has worked best in the past.? If you want to, you can buy it here: What Works on Wall Street.

Full disclosure: The publisher asked if I wanted the book.? I said ?yes? and he sent it to me.

If you enter Amazon through my site, and you buy anything, I get a small commission.? This is my main source of blog revenue.? I prefer this to a ?tip jar? because I want you to get something you want, rather than merely giving me a tip.? Book reviews take time, particularly with the reading, which most book reviewers don?t do in full, and I typically do. (When I don?t, I mention that I scanned the book.? Also, I never use the data that the PR flacks send out.)

Most people buying at Amazon do not enter via a referring website.? Thus Amazon builds an extra 1-3% into the prices to all buyers to compensate for the commissions given to the minority that come through referring sites.? Whether you buy at Amazon directly or enter via my site, your prices don?t change.

Ignore Sharp Moves

Posted on 29 November 2011 by David Merkel

In general, my experience is that sharp moves up or down over a day or a few days proceed from investors that are reacting, not thinking.? Those moves tend to get erased by future market action.

Slower, intermediate-term moves tend to persist, and those moves don’t get the same media attention, because they aren’t dramatic.? Bear market rallies are sharp, so are Bull market panics.

That’s why I don’t make too much out of days like yesterday.? A news-driven market is over-reactive.? Days where there is no no significant news gives us a feel for how the large players are adjusting their exposure; the same is true of looking at the trend over 6-12 months, where the effect of short-term news gets washed out.

So be wary, skeptical, etc., and keep some cash on hand to buy up future bargains.

Weighing Beats Voting

Posted on 19 October 2011 by David Merkel

Correlations are high.? Risk-on, risk-off drives the market as market players trade ETPs and baskets rather than individual stocks.? Market players worry about policy, and whether it will be inflationary (bullish) or deflationary (bearish).

What an ugly time to be a value investor, and a long-term industry rotator.? The time cycle has shrunk to tiny proportions relative to the likely life of the assets being traded.

But I take heart that it will not always be this way.? As Ben Graham said, “In the short run, the market is a voting machine but in the long run it is a weighing machine.”

Eventually, for industries where the companies are worth a lot more than the current price, there will be buyouts.? For industries where companies are worth less, there may be IPOs.

I believe that correlations will reduce from here.? It may not be dramatic, but they will fall.? Whenever there is a dominant paradigm for asset pricing, there are assets that get mispriced.

My expectation is that there are many companies earning money while trading at a discount to adjusted book that will be bought out by others.

On High Correlations

Posted on 31 August 2011 by David Merkel

There have been a lot of articles written recently about a high average correlation level in the stock market.? I want to take a stab at explaining what it means and implies.

A few notes before I start.? First, remember that cash doesn?t enter or leave the market when we buy or sell.? Cash enters the market when new stocks, bonds, etc., get issued in exchange for cash.? Cash exits the market when stocks, bonds, etc., get retired in exchange for cash through buyouts, maturities, etc.? Second, when we buy or sell, the price changes based on whether buyers are sellers are more motivated to buy/sell the asset and sell/buy cash.? In the short run, even the amount of cash doesn?t change, aside from what the brokers and market-makers scrape off.

Note that this applies to ETFs as well.? Even as they grow, they suck in more of the stocks/bonds that they index, but after fees (more scrape) they are just shells, holding vehicles for assets.

Third, there are two reasons why assets can be highly correlated.? The first reason is that the business performance is geared to the same driver, for example, the expansion of credit.? The second reason is that the current and future ownership has similar motives for each asset, and trade each similarly.? The first is Ben Graham?s weighing machine, while the second is the voting machine.? The second reason is more relevant for what we are experiencing today.

Fourth, remember that correlation is not the same as beta.? Stock A always moves half as much as stock B.? The correlation is 1, but the beta versus B is 0.5.? Just because correlations are high does not mean every stock is moving the same amount.? It does mean that they are almost all moving in the same direction at mostly consistent relative amplitudes.

The preliminaries are done.? The most important aspect of my preliminaries is that we are likely dealing with Ben Graham?s voting machine as the causative factor for the high valuations.

Okay, now think of stocks and other assets as dependent on the time horizons of their investors.??? If the time horizons of investors are predominantly long, correlations on assets should be low in the short-run, because investors don?t make decisions to trade off of short-term macro factors.? But when a large part of the investor base is skittish and is always running to or from the latest bit/byte/bite of data ? that leads to high correlations.

ETFs aren?t necessary for high correlations, but they seem to help the process by creating easy ways for people to implement decisions that are a simple idea.?? ?I want financials, I don?t want energy, buy the long bond, sell gold.?

Thus high short-term correlations indicate a momentum mindset in the investor base.? Momentum investors are the ?weak hands? of ownership.? They don?t have much of a balance sheet, and so their decisions are quick and correlated with short term price action.? The strong hands have balance sheets, or are long-term minded, and can ?buy and hold? or ?sell and sit on cash.?? That takes a lot of fortitude, particularly in the present environment.

In an era of high correlations, I have two things to say:

1)????? When the voting machine is running hot, pay more attention to the weighing machine ? the fundamental values that drive long-run investing.? Pretend you are Seth Klarman, Warren Buffett, or if you can?t imagine that, pretend you are me, and aim for the best over the next three years.

2)????? In general, markets are near short-term peaks when the level of momentum investing is high.?? Volatility tends to be high as well.? Volatility is inversely proportional to time horizon. (I.e., the longer you aim for in investing, the less you care about short-term volatility.)

Thus my conclusion is this: now is a time to pay attention to fundamental values.? Ignore the noise and protect your capital.? I know this sounds too simple, but when correlations get too high, act against the direction of the market.

PS — still don’t have power back from Irene.? Pray for us.? I get my work done at a backup site.