The Aleph Blog » Quantitative Methods

## Archive for the ‘Quantitative Methods’ Category

### The Rules, Part LVIII

Friday, March 7th, 2014

Can contingent claims theory for bond defaults be done on a cash flow/liquidity basis?  KMV-type models seem to fail on severely distressed bonds that have time to breathe and repair.

We’re getting close to the end of this series, and I am scraping the bottom of the barrel.  As with most aspects of life, the best things get done first.  After that diminishing marginal returns kick in.

Here’s the issue.  It’s possible to model credit risk as a put option that the bondholders have sold to the stockholders.  As such, equity implied volatility helps inform us as to how likely default will be.  But implied volatilities are only available for at most two years out, because they don’t commonly trade options longer than that.

Here’s the scenario that I posit: there is a company in lousy shape that looks like a certain bankruptcy candidate, except that there are no significant events requiring liquidity for 3-5 years.  In a case like this, the exercise date of the option to default is so far out, that the company can probably find ways to avoid bankruptcy, but the math may make it look unavoidable.  Remember, the equity has the option to default, but they also control the company until they do default.  Being the equity is valuable, because you control the assets.

Bankruptcy means choking on cash flows out that can’t be made.  Ordinarily, that happens because of interest payments that can’t be made, rather than repayment of principal.  If interest payments can be made, typically principal payments can be refinanced, unless credit gets tight.

The raw math of the contingent claims models do not take account of the clever distressed company manager who finds a way to avoid bankruptcy, driving deals to avoid it.  The more time he has, the more clever he can be.

This is a reason why I distrust simple mathematical models in investing.  The world is more complex than the math will admit.  So be careful applying math to markets.  Think through what the assumptions and models mean, because they may not reflect how people actually work.

### On Finding Neglected Companies

Wednesday, March 5th, 2014

While at RealMoney, I wrote a short series on data-mining.  Copies of the articles are here: (one, two). I enjoyed writing them, and the most pleasant surprise was the favorable email from readers and fellow columnists. As a follow up, on April 13th, 2005, I wrote an article on analyst coverage — and neglect. Today, I am writing the same article but as of today, with even more detail, and comparisons to prior analyses.

As it was, in my Finacorp years, I wrote a similar piece to this but it has been lost; I can’t find a copy of it, and Finacorp is in the ash-heap of financial firms. (Big heap, that.)

For a variety of reasons, sell-side analysts do not cover companies and sectors evenly. For one, they have biases that are related to how the sell-side analyst’s employer makes money. It is my contention that companies with less analyst coverage than would be expected offer an opportunity to profit for investors who are willing to sit down and analyze these lesser-analyzed companies and sectors.

I am a quantitative analyst, but I try to be intellectually honest about my models and not demand more from them than they can deliver. That’s why I have relatively few useful models, maybe a dozen or so, when there are hundreds of models used by quantitative analysts in the aggregate.

Why do I use so few? Many quantitative analysts re-analyze (torture) their data too many times, until they find a relationship that fits well. These same analysts then get surprised when the model doesn’t work when applied to the real markets, because of the calculated relationship being a statistical accident, or because of other forms of implementation shortfall — bid-ask spreads, market impact, commissions, etc.

This is one of the main reasons I tend not to trust most of the “advanced” quantitative research coming out of the sell side. Aside from torturing the data until it will confess to anything (re-analyzing), many sell-side quantitative analysts don’t appreciate the statistical limitations of the models they use. For instance, ordinary least squares regression is used properly less than 20% of the time in sell-side research, in my opinion.

Sell-side firms make money two ways.They can make via executing trades, so volume is a proxy for profitability.They can make money by helping companies raise capital, and they won’t hire firms that don’t cover them.Thus another proxy for profitability is market capitalization.

Thus trading volume and market capitalization are major factors influencing analyst coverage. Aside from that, I found that the sector a company belongs to has an effect on the number of analysts covering it.

I limited my inquiry to include companies that had a market capitalization of over \$10 million, US companies only, and no ETFs.

I used ordinary least squares regression covering a data set of 4,604 companies. The regression explained 82% of the variation in analyst coverage. Each of the Volume and market cap variables used were significantly different from zero at probabilities of less than one in one million. As for the sector variables, they were statistically significant as a group, but not individually.Here’s a list of the variables:

 Variable Coefficients Standard Error t-Statistic Logarithm of 3-month average volume 0.57 0.04 15.12 Logarithm of Market Capitalization (2.22) 0.15 (14.69) Logarithm of Market Capitalization, squared 0.36 0.01 31.42 Basic Materials (0.53) 0.53 (1.01) Capital Goods 0.39 0.54 0.74 Conglomerates (0.70) 1.95 (0.36) Consumer Cyclical 0.08 0.55 0.14 Consumer Non-Cyclical (1.40) 0.55 (2.52) Energy 2.56 0.53 4.87 Financial 0.37 0.48 0.78 Health Care 0.05 0.50 0.11 Services (0.30) 0.49 (0.61) Technology 0.82 0.49 1.67 Transportation 2.92 0.66 4.40 Utilities (1.10) 0.60 (1.82)

In short, the variables that I used contained data on market capitalization, volume and market sector.

An increasing market capitalization tends to attract more analysts. At a market cap of \$522 million, market capitalization as a factor adds no net analysts. At the highest market cap in my study, Apple [AAPL] at \$469 billion, the model indicates that 11 fewer analysts should cover the company. The smallest companies in my study would have 3.3 fewer analysts as compared with a company with a market cap of \$522 million.

 Market Cap Analyst additions 10.00 2.30 30.00 3.40 100.00 4.61 300.00 5.70 522.20 6.26 1,000.00 6.91 3,000.00 8.01 10,000.00 9.21 30,000.00 10.31 100,000.00 11.51 300,000.00 12.61 469,400.30 13.06

The intuitive reasoning behind this is that larger companies do more capital markets transactions. Capital markets transactions are highly profitable for investment banks, so they have analysts cover large companies in the hope that when a company floats more stock or debt, or engages in a merger or acquisition, the company will use that investment bank for the transaction.

Investment banks also make some money from trading. Access to sell-side research is sometimes limited to those who do enough commission volume with the investment bank. It’s not surprising that companies with high amounts of turnover in their shares have more analysts covering them. The following table gives a feel for how many additional analysts cover a company relative to its daily trading volume. A simple rule of thumb is that (on average) as trading volume quintuples, a firm gains an additional analyst, and when trading volume falls by 80%, it loses an analyst.

 Daily Trading Volume (3 mo avg) Analyst Additions 3 0.6 10 1.3 30 1.9 100 2.6 300 3.2 1,000 3.9 3,000 4.5 10,000 5.2 30,000 5.8 100,000 6.5 300,000 7.1 1,000,000 7.8 3,000,000 8.4 4,660,440 8.7

An additional bit of the intuition for why increased trading volume attracts more analysts is that volume is in one sense a measure of disagreement. Investors disagree about the value of a stock, so one buys what another sells. Sell-side analysts note this as well; stocks with high trading volumes relative to their market capitalizations are controversial stocks, and analysts often want to make their reputation by getting the analysis of a controversial stock right. Or they just might feel forced to cover the stock because it would look funny to omit a controversial company.

#### Analyst Neglect

The first two variables that I considered, market capitalization and volume, have intuitive stories behind them as to why the level of analysts ordinarily varies. But analyst coverage also varies by industry sector, and the reasons are less intuitive to me there.

Please note that my regression had no constant term, so the constant got embedded in the industry factors. Using the Transportation sector as a benchmark makes the analysis easier to explain. Here’s an example: On average, a Utilities company that has the same market cap and trading volume as a Transportation company would attract four fewer analysts.

 Sector Addl Analysts Fewer than Transports Transportation 2.92 Energy 2.56 (0.37) Technology 0.82 (2.10) Capital Goods 0.39 (2.53) Financial 0.37 (2.55) Consumer Cyclical 0.08 (2.84) Health Care 0.05 (2.87) Services (0.30) (3.22) Basic Materials (0.53) (3.46) Conglomerates (0.70) (3.63) Utilities (1.10) (4.02) Consumer Non-Cyclical (1.40) (4.32)

Why is that? I can think of two reasons. First, the companies in the sectors at the top of my table are perceived to have better growth prospects than those at the bottom. Second, the sectors at the top of the table are more volatile than those toward the bottom (though basic materials would argue against that). As an aside, companies in the conglomerates sector get less coverage because they are hard for a specialist analyst to understand.

My summary reason is that “cooler” sectors attract more analysts than duller sectors. To the extent that this is the common factor behind the variation of analyst coverage across sectors, I would argue that sectors toward the bottom of the list are unfairly neglected by analysts and may offer better opportunities for individual investors to profit through analysis of undercovered companies in those sectors.

#### Malign Neglect

Now, my model did not explain 100% of the variation in analyst coverage. It explained 82%, which leaves 18% unexplained. Some of the unexplained variation is due to the fact that no model can be perfect. But the unexplained variation can be used to reveal the companies that my model predicted most poorly. Why is that useful? If my model approximates “the way the world should be,” then the degree of under- and over-coverage by analysts will reveal where too many or few analysts are looking. The following tables lists the largest company variations between reality and my model, split by market cap group.

Behemoth Stocks

 Ticker Company Sector Excess analysts BRK.A Berkshire Hathaway Inc. 07 – Financial (25.75) GE General Electric Company 02 – Capital Goods (20.47) XOM Exxon Mobil Corporation 06 – Energy (19.32) CVX Chevron Corporation 06 – Energy (14.64) PFE Pfizer Inc. 08 – Health Care (14.57) MRK Merck & Co., Inc. 08 – Health Care (12.76) GOOG Google Inc 10 – Technology (11.44) JNJ Johnson & Johnson 08 – Health Care (11.39) MSFT Microsoft Corporation 10 – Technology (10.39) PM Philip Morris International In 05 – Consumer Non-Cyclical (10.21)

Too many

 Ticker Company Sector Excess analysts V Visa Inc 09 – Services 2.58 DIS Walt Disney Company, The 09 – Services 2.95 SLB Schlumberger Limited. 06 – Energy 4.15 CSCO Cisco Systems, Inc. 10 – Technology 5.22 QCOM QUALCOMM, Inc. 10 – Technology 5.34 ORCL Oracle Corporation 10 – Technology 5.98 FB Facebook Inc 10 – Technology 8.28 AMZN Amazon.com, Inc. 09 – Services 9.34 AAPL Apple Inc. 10 – Technology 10.57 INTC Intel Corporation 10 – Technology 11.85

Large Cap Stocks

 Ticker Company Sector Excess analysts SPG Simon Property Group Inc 09 – Services (16.15) BF.B Brown-Forman Corporation 05 – Consumer Non-Cyclical (16.03) LUK Leucadia National Corp. 07 – Financial (15.93) L Loews Corporation 07 – Financial (15.90) EQR Equity Residential 09 – Services (15.87) ARCP American Realty Capital Proper 09 – Services (15.75) IEP Icahn Enterprises LP 09 – Services (15.50) LVNTA Liberty Interactive (Ventures 09 – Services (15.36) ABBV AbbVie Inc 08 – Health Care (15.01) GOM CL Ally Financial Inc 07 – Financial (14.87)

Too Many

 Ticker Company Sector Excess analysts UA Under Armour Inc 04 – Consumer Cyclical 16.68 BRCM Broadcom Corporation 10 – Technology 17.29 RRC Range Resources Corp. 06 – Energy 17.33 SWN Southwestern Energy Company 06 – Energy 17.70 RHT Red Hat Inc 10 – Technology 18.08 NTAP NetApp Inc. 10 – Technology 19.82 CTXS Citrix Systems, Inc. 10 – Technology 19.84 COH Coach, Inc. 09 – Services 20.87 VMW VMware, Inc. 10 – Technology 21.60 CRM salesforce.com, inc. 10 – Technology 22.64

Mid cap stocks

 Ticker Company Sector Excess analysts FNMA Federal National Mortgage Assc 07 – Financial (13.84) UHAL AMERCO 11 – Transportation (12.23) O Realty Income Corp 09 – Services (12.06) CIM Chimera Investment Corporation 07 – Financial (11.49) SLG SL Green Realty Corp 09 – Services (11.46) NRF Northstar Realty Finance Corp. 09 – Services (11.34) FMCC Federal Home Loan Mortgage Cor 07 – Financial (11.14) EXR Extra Space Storage, Inc. 11 – Transportation (10.97) KMR Kinder Morgan Management, LLC 06 – Energy (10.94) CWH CommonWealth REIT 09 – Services (10.51)

Too Many

 Ticker Company Sector Excess analysts AEO American Eagle Outfitters 09 – Services 17.00 DRI Darden Restaurants, Inc. 09 – Services 17.40 RVBD Riverbed Technology, Inc. 10 – Technology 17.50 CMA Comerica Incorporated 07 – Financial 17.74 GPN Global Payments Inc 07 – Financial 18.30 WLL Whiting Petroleum Corp 06 – Energy 19.67 DO Diamond Offshore Drilling Inc 06 – Energy 21.57 URBN Urban Outfitters, Inc. 09 – Services 24.06 RDC Rowan Companies PLC 06 – Energy 24.48 ANF Abercrombie & Fitch Co. 09 – Services 26.02

Small cap stocks

 Ticker Company Sector Excess analysts BALT Baltic Trading Ltd 11 – Transportation (7.96) ERA Era Group Inc 11 – Transportation (7.45) PBT Permian Basin Royalty Trust 06 – Energy (7.42) SDR SandRidge Mississippian Trust 06 – Energy (7.18) PHOT Growlife Inc 02 – Capital Goods (6.79) SBR Sabine Royalty Trust 06 – Energy (6.74) CAK CAMAC Energy Inc 06 – Energy (6.64) FITX Creative Edge Nutrition Inc 09 – Services (6.57) BLTA Baltia Air Lines Inc 11 – Transportation (6.53) VHC VirnetX Holding Corporation 10 – Technology (6.49)

Too many

 Ticker Company Sector Excess analysts WLT Walter Energy, Inc. 06 – Energy 12.19 ANGI Angie’s List Inc 10 – Technology 12.31 FRAN Francesca’s Holdings Corp 09 – Services 12.58 ZUMZ Zumiez Inc. 09 – Services 13.49 GDP Goodrich Petroleum Corp 06 – Energy 15.02 DNDN Dendreon Corporation 08 – Health Care 15.89 ACI Arch Coal Inc 06 – Energy 16.04 HERO Hercules Offshore, Inc. 06 – Energy 16.19 AREX Approach Resources Inc. 06 – Energy 17.64 ARO Aeropostale Inc 09 – Services 20.80

Microcap Stocks

 Ticker Company Sector Excess analysts SGLB Sigma Labs Inc 06 – Energy (6.18) AEGY Alternative Energy Partners In 10 – Technology (5.97) WPWR Well Power Inc 06 – Energy (5.83) TTDZ Triton Distribution Systems In 10 – Technology (5.53) SFRX Seafarer Exploration Corp 11 – Transportation (5.15) PTRC Petro River Oil Corp 06 – Energy (4.99) UTRM United Treatment CentersInc 08 – Health Care (4.82) BIEL Bioelectronics Corp 08 – Health Care (4.80) DEWM Dewmar International BMC Inc 01 – Basic Materials (4.74) FEEC Far East Energy Corp 06 – Energy (4.61)

Too many

 Ticker Company Sector Excess analysts PRSS CafePress Inc 09 – Services 3.99 SANW S&W Seed Company 05 – Consumer Non-Cyclical 4.03 KIOR KiOR Inc 01 – Basic Materials 4.06 PRXG Pernix Group Inc 02 – Capital Goods 4.08 EYNON Entergy New Orleans, Inc. 12 – Utilities 4.17 PARF Paradise, Inc. 05 – Consumer Non-Cyclical 4.40 SUMR Summer Infant, Inc. 05 – Consumer Non-Cyclical 4.52 LAND Gladstone Land Corp 05 – Consumer Non-Cyclical 4.57 JRCC James River Coal Company 06 – Energy 6.38 GNK Genco Shipping & Trading Limit 11 – Transportation 7.11

My advice to readers is to consider buying companies that have fewer analysts studying them than the model would indicate.  This method is certainly not perfect but it does point out spots where Wall Street is not focusing its efforts, and might provide some opportunities.

Full disclosure: long BRK/B & CVX

### On Intrinsic Value

Tuesday, March 4th, 2014

In his annual report, though not his more well-known letter, Buffett talked about intrinsic value.  It was on pages 107-108, and here it is:

Now let’s focus on a term that I mentioned earlier and that you will encounter in future annual reports.

Intrinsic value is an all-important concept that offers the only logical approach to evaluating the relative attractiveness of investments and businesses. Intrinsic value can be defined simply: It is the discounted value of the cash that can be taken out of a business during its remaining life.

The calculation of intrinsic value, though, is not so simple. As our definition suggests, intrinsic value is an estimate rather than a precise figure, and it is additionally an estimate that must be changed if interest rates move or forecasts of future cash flows are revised. Two people looking at the same set of facts, moreover – and this would apply even to Charlie and me – will almost inevitably come up with at least slightly different intrinsic value figures. That is one reason we never give you our estimates of intrinsic value.  What our annual reports do supply, though, are the facts that we ourselves use to calculate this value.

Meanwhile, we regularly report our per-share book value, an easily calculable number, though one of limited use. The limitations do not arise from our holdings of marketable securities, which are carried on our books at their current prices. Rather the inadequacies of book value have to do with the companies we control, whose values as stated on our books may be far different from their intrinsic values.

The disparity can go in either direction. For example, in 1964 we could state with certitude that Berkshire’s per-share book value was \$19.46. However, that figure considerably overstated the company’s intrinsic value, since all of the company’s resources were tied up in a sub-profitable textile business. Our textile assets had neither going-concern nor liquidation values equal to their carrying values. Today, however, Berkshire’s situation is reversed: Now, our book value far understates Berkshire’s intrinsic value, a point true because many of the businesses we control are worth much more than their carrying value.

Inadequate though they are in telling the story, we give you Berkshire’s book-value figures because they today serve as a rough, albeit significantly understated, tracking measure for Berkshire’s intrinsic value. In other words, the percentage change in book value in any given year is likely to be reasonably close to that year’s change in intrinsic value.

You can gain some insight into the differences between book value and intrinsic value by looking at one form of investment, a college education. Think of the education’s cost as its “book value.” If this cost is to be accurate, it should include the earnings that were foregone by the student because he chose college rather than a job.

For this exercise, we will ignore the important non-economic benefits of an education and focus strictly on its economic value. First, we must estimate the earnings that the graduate will receive over his lifetime and subtract from that figure an estimate of what he would have earned had he lacked his education. That gives us an excess earnings figure, which must then be discounted, at an appropriate interest rate, back to graduation day. The dollar result equals the intrinsic economic value of the education.

Some graduates will find that the book value of their education exceeds its intrinsic value, which means that whoever paid for the education didn’t get his money’s worth. In other cases, the intrinsic value of an education will far exceed its book value, a result that proves capital was wisely deployed. In all cases, what is clear is that book value is meaningless as an indicator of intrinsic value.

There are two problems with intrinsic value:

1. We don’t really know what future free cash flow will be, nor the willingness and ability of management to use it wisely.
2. We really don’t know what the cost of capital is for a firm, particularly not the cost of equity.

With simple firms, we can try to do a sum-of-the-parts analysis off of comparable companies — but often the differences are significant.  It’s not easy.

Buffett has drawn a line in the sand, and that line has held so far — he buys back shares when the price drops below 1.2x book value.  At present, that is his proxy for what I suspect is his minimum view of what BRK is worth.

This offers an experimental way of attempting to estimate intrinsic value, that is, if you are the CEO or CFO.  Set up a valuation metric off of book or sales, since they don’t move as much as earnings, and then offer to buy back shares at a multiple of the metric that you think represents intrinsic value.

If you don’t buy many shares, you might want to move the multiple up.  If shares come flooding in, move the multiple down. Oops, did I forget to mention “be conservative initially?”

I think Buffett is setting an example to other management teams on how to run an intelligent buyback.  The main principle is this: buy back shares during a panic, or during a malaise that does not reflect future prospects.  Don’t buy back shares all of the time.  Wait for bargains to buy back shares.

If you set the barrier on when you buy shares such that it happens a few times a year, and cash levels never get too low, you’ve probably set up a good buyback plan, and as a bonus, you have a decent conservative idea of what the intrinsic value is for your stock.

I think it would make a lot of sense for CEOs and CFOs to imitate Buffett, and make their buybacks contingent on a certain multiple of book value or sales.  Adapt the level to demand, and be conservative — it is far better to not make so much money, than to give away give away value by overpaying for your stock.  You can always buy back more stock later; you can’t un-buy stock.

The side benefit of an exercise like this to a corporation is that it will understand its cost of capital well, and will be all the more able to make intelligent decisions on mergers and acquisitions, stock buybacks and issuance.

Full Disclosure: Long BRK/B for clients and me

### On Emergent Phenomena

Thursday, February 20th, 2014

How do you deal with a risk that has never been seen before?  I’m going to focus on financial risks here, but clever people can generalize to other classes of human risk, like war and terrorism.

By “emergent phenomena” I mean what happens when people act as a group pursuing the same strategy.  One person doing a given strategy means nothing.  But when millions do it, that can be significant.  Same for corporations, but the numbers are lower, because corporations are far bigger economically than the average household.

Here are some examples of emergent phenomena:

• 1987 — Strategies for dynamic hedging became a large enough part of the market that the market became unstable, where parties would buy as the market rose, and sell as the market fell.
• Tech stocks were the only place to be 1998-2000, until they weren’t 2000-2003.
• Too much hedge fund money was playing the quantitative value plus momentum trade in 2007.  Many players borrowed money to goose returns in 2006-7.  It blew up in August 2007.
• The fear of not getting “free money” caused many to overinvest in residential real estate 2004-7, until the free money was not only not free, but billing you for past indiscretions.
• There was a frenzy among commercial real estate investor toward the end of the 1980s, which bid prices up amid more buying power from then-cheap commercial mortgage debt, leading to an overshoot, and fall in property value in the early 1990s.
• In 2005, the CDO Correlation Trade led to a panic in the corporate bond market, and in auto stocks.
• Into the late 1980s, Japanese households and some foreigners plowed progressively more liquid capital into the Japanese stock and warrant markets.  That was the peak, and few if any have made their money back.

Emergent phenomena stem from:

• Many people and institutions doing the same thing at the same time.
• Using debt to substitute for equity in a trade that has become a “sure thing.”
• Multiple companies and industries pursuing the essentially same trade, but in different corners of the markets.  (Think of the real estate bubble.  There were so many different angles that the bulls played: mortgage insurance, financial guaranty, subprime loans and derivatives thereof, weakened lending standards on prime loans, etc.)
• And it is more intense when economic agents borrow short-term to finance their efforts, because when things go wrong, the feedback loop is quick.

Everyone runs to the exits in a burning theater, and so, fewer get out amid the struggle, than if everyone patiently walked out.  In financial terms, this is why markets are more volatile than expected, particularly on the downside.  Too many people want to sell in a panic, after having pursued a well-known strategy that had been successful for quite a while.

But no tree grows to the sky.  The intelligent investor notes several things:

• Where is the most new debt being applied, and to increasingly little effect?
• What fad are players investing in, that you think can’t be maintained long-run?
• What is happening that would not be happening if it were not for price momentum?
• Where are players relying on price appreciation or else their levered positions will collapse?
• Where is money being borrowed short-term to fund long-term assets?

People are prone to imitate past success, even when a rational person would conclude that it doesn’t make any sense to borrow money and buy an asset at a high price.  It’s easier to imitate than to think independently.

In the present market, I see large increases in government debt and student loans.  Beyond that, there is the income craze in investing.  Don’t look at the yield; look at the underlying business.

Be wary.  The stock market has run hard the last ~5 years, and I see valuation-sensitive investors retreating.  Even with bond rates low, that doesn’t mean stocks are better.

### Why are Pensions so Messed Up

Tuesday, February 18th, 2014

A few days ago, I was reading Felix Salmon’s piece Pension politics.  (Nice title, the type that Tadas likes — the shorter the better.)  I wrote a short response in the comments, largely agreeing with Felix.  Here it is:

Here are the facts:

1) DB pension funding accounting rules are more liberal than life insurance accounting rules.

2) Pension actuaries have long assumed investment earnings rates well in excess of what can be achieved.

3) Longevity has long been increasing for those that buy annuities, and take pensions.

4) Average people are lousy investment managers, they panic and get greedy at the wrong times. Pension asset managers aren’t great, but they largely avoid panic & greed.

5) The PBGC is horribly underfunded, as are most municipal pension plans.

6) Overseas, things can be bad, like Poland, Argentina, India, etc. In those cases being on your own is better. Our custodial systems here are pretty good. (Please ignore MF Global.)

7) Fees are generally too high in asset management, and most people should go for passive management, or a few clever value investors.

8 ) Hedge funds, commodities, and private equity are not the answer. Analyze the returns on an dollar-weighted [IRR] basis and they will be much lower than the illustrated buy & hold returns.

9) Highly paid workers lose out in bankruptcy. Multi-employer trusts are prone to a run on the pension plan if a major employer goes BK.

10) the average person is at best a budgeter, and not an investor. That said, buying inflation insurance is very expensive, if you can achieve it at all.

Summary: in general, you are right, Felix, but it is a question of cost to the corporations funding the DB plans. I think the cost is worth it, but maybe it needs to be shared with workers, taking pre-tax dollars to buy more future DB plan payments. How many people would do that? Sadly, not many.

Pensions have always been a bit of a compromise.  In order to get employers to create Defined Benefit [DB] pensions, the government allowed for funding methods that were liberal — a plan sponsor wouldn’t have to put in as much at the beginning; it can catch up over time.  More than that, the assumptions that DB pensions could use were far more liberal than what life insurers could use for similar contingencies.  Life insurers had to use best estimates and then add risk margins.  Pensions could dream of returns, with no risk margins.

The 401(k) was an accident.  It was tossed into a much larger bill, and no one noticed.  After passage, some benefits consultants, notably Ted Benna, found ways to use it, creating the boom in Defined Contribution [DC] plans.

Corporations initially added DC plans to their DB plans, but as the 90s ended, and equity performance sank, many terminated their DB plans.  Part of it was the asset markets, but another part of it was aging workforces, because the funding rules were weak (unlike life insurance).  Sponsors realized that they would have to spend a lot more on DB plans in the future than they would otherwise want to.  Now stingy corporations cut back on their DC matches, or accept kickbacks out of investment manager fees.

There are two great virtues in defined benefit plans: 1) Investing is handled by professionals.  2) Level payments are made.  Most people can budget.  Few can invest.  Yes, there is the problem of inflation, should it occur, but pensioners should have assets outside of their pension to deal with inflation.  They need longevity insurance, so that they avoid outliving their assets.

Though it might be hard managing a fixed income versus uncertain inflation over an uncertain lifespan, it is much harder to manage a lump sum over a full retirement.  When finances are tight, it is much harder to make the right decisions.  Hope biases average investors in favor of taking chances, whether the market favors taking chances or not.

Add in the troubles with defaults of DB plan sponsors, and significant benefits can be lost, particularly if you have been highly paid.

I would want to tell most asset allocators that there is little to no magic in alternative investments.  The alternatives face the same risk factors as ordinary investments, and they are not underinvested by pension investors.

Closing Notes

Sorry, I forgot to blame the IRS for limiting overfunding for tax reasons, when the overfunding was really funding, and would have been useful today.

Even without the introduction of the 401(k), corporations would have cut back on DB pensions because of costs.  A lot of that was due to bad funding methods, but without those bad funding methods, many DB plans would never have been done.

Just be grateful you don’t live in other parts of the world, where governments are more graspy, and pension assets are a target to plug holes in the government deficit.

### Industry Ranks February 2014

Sunday, February 9th, 2014

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.”

You might notice that  I have no industries from the red zone. That is because the market is so high. I only want to play in cold industries. They won’t get so badly hit in a decline, and they might have some positive surprises.

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. I generally play in the green zone because I hold stocks for 3 years on average.

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 stocks here, some industrials, some consumer stocks, particularly those that are strongly capitalized.

I’m looking for undervalued 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.

That said, some dull companies are fetching some pricey valuations these days, particularly those with above average dividends. This is an overbought area of the market, and it is just a matter of time before the flight to relative safety reverses.

The Red Zone has a Lot of Financials; be wary of those. I have been paring back my insurers, but I have been adding to P&C reinsurers.  What I find fascinating about the red momentum zone now, is that it is loaded with cyclical companies.

In the green zone, I picked almost all of the industries. If the companies are sufficiently well-capitalized, and the valuation is low, it can still be an rewarding place to do due diligence.

Will cyclical companies continue to do well?  Will the economy continue to limp along, or might it be better or worse?

But what would the model suggest?

Ah, there I have something for you, and so long as Value Line does not object, I will provide that for you. I looked for companies in the industries listed, but in the top 5 of 9 balance sheet safety categories, and with returns estimated over 12%/year over the next 3-5 years. The latter category does the value/growth tradeoff automatically. I don’t care if returns come from mean reversion or growth.

But anyway, as a bonus here are the names that are candidates for purchase given this screen. Remember, this is a launching pad for due diligence, not hot names to buy.

I’ve loosened my criteria a little because the market is so high, but I figure I will toss out lot when I do my quarterly evaluation of the companies that I hold for clients and me.

### An Expensive Kind of Insurance

Thursday, February 6th, 2014

Strategy One: “Consistent Losses, with Occasional Big Gains when the Market is Stressed”

Strategy Two: “Consistent Gains, with Total Wipe-out Risk When Market is Highly Stressed”

How do these two strategies sound to you?  Not too appealing?  I would agree with that.  The second of those strategies was featured in an article at Bloomberg.com recently — Inverse VIX Fund Gets Record Cash on Calm Market Bet.  And though the initial graph confused me, because it was the graph for the exchange traded note VXX, which benefits when the VIX spikes, the article was mostly about the inverse VIX exchange traded note XIV.

Why would someone pursue the second strategy?  Most of the time, it makes money, and since January 2011 we haven’t a horrendous market event like the one from August 2008 through February 2009, it makes money.

I would encourage you to look at the decline in the second half of 2011, where it fell 75% when the VIX briefly burped up to around 50.  But given the amazing comeback as volatility abated, the lesson that some investors drew was this: “Volatility Spike? Time to buy XIV!”  And that explains the article linked above.

You might remember a recent book review of mine — Rule Based Investing.  In that review, I made the point that those that sell insurance on financial contracts tend to win, but it is a volatile game with the possibility of total loss.  To give another example from the recent financial crisis: most of the financial and mortgage insurers in existence prior to 2007 are gone.  Let me put it simply: though financial risks can be insured, the risks are so volatile that they should not be insured.  You are just one colossal failure away from death, and that colossal failure will tend to come when everyone is certain that it can’t come.

But what of the first strategy?  How has it done?

Wow!  Look at the returns over the last few weeks!  Rather, look at a strategy that consistently loses money because it rolls futures contracts for the VIX where the futures curve is upward-sloping almost all the time, leading to buy high, sell low.

Does it pay off in a crisis?  Yes.  Can you use it tactically?  Yes.  Can you hold it and make money?  No.

Back to the second strategy.  People are putting money into XIV because they “know” that implied volatility always mean-reverts, and so they will make easy money after a volatility spike.  But what if they arrive too early, and volatility spikes far higher than expected?  Worse yet, what if Credit Suisse goes belly-up in the volatility?  After all, it is an exchange-traded note where owners of XIV are lending money to Credit Suisse.

Back to Basics

Do I play in these markets?  No.

Do I understand them?  Mostly, but I can’t claim to be the best at this.

What if I try both strategies at the same time?  You will lose.  You are short fees and trading frictions.

What if I short both strategies at the same time?  Uncertain. It comes down to whether you can hold the shorts over the long term without getting “bought in” or panic when one side of the trade runs the wrong way.

Recently, someone pinged me to speak to CFA Institute, Baltimore, where he wanted to talk about “not all correlations of risky assets go to one in a crisis” and pointed to volatility investing as the way to improve asset allocation.  Sigh.  I’m inclined to say that “you can’t teach a Sneech.”

I favor simplicity in investing, and think that many exchange traded products will harm investors on average because the investors do not understand the underlying economics of what they own, while Wall Street uses them as a cheap way to hedge their risk exposures.

There may be some value to speculators in using “investments” like strategy one for a few days at a time.  But holding for any long time is poison.  Worse, if you are accidentally right, and the world comes to an end — this is an exchange-traded note, and the bank you lent to will be broke.  That will also kill strategy two.

So, my advice to you is this: avoid either side of this trade.  Stick with simple investments that do not invest in futures or options.  Complexity is the enemy of the average investor.  I can understand these investments and they don’t work for me.  You should avoid them too.

PS — before I close, let me mention:

Good article in both places.

### Differences in US States’ Unemployment over the Last 36 Years

Tuesday, February 4th, 2014

I would encourage you to have a read of the 2014 Baltimore Business Review.  Produced by the CFA Institute  — Baltimore, and Towson University, it  is a great example of how academics and practitioners can work together.  Here is my article, reformatted so that it looks better on my blog:

### Differences in US States’ Unemployment over the Last 36 Years

Unemployment is often treated as a national issue, but unemployment is often driven by regional or industry sector issues. This article pries apart the causes of unemployment since 1976, state-by-state.

Though there is a national component to every US state’s unemployment level, it is notable that local factors often dominate national trends. Here are some examples:

• North Dakota has an energy boom amid increasing unemployment following the housing bust in 2008.
• Texas had increasing unemployment in the mid-1980s as energy prices fell dramatically, in the midst of an economic boom.
• Coastal economies benefited during the housing boom (pre-2008), and were punished in the bust – this is parallel to the US economy as a whole, but more severe.
• The Rust Belt prospered slowly in the early 1980s as the rest of the nation began to prosper rapidly.

The rest of this article will explain the causes of unemployment over the last 36 years, related to how connected a state is to the rest of the US economy, and how well the industry mix in a given state is doing.

Data & Method

Unemployment data for each state and the US as a whole was obtained from the St. Louis Federal Reserve’s Federal Reserve Economic Data (FRED) database. The data covers the period from 1976 to August 2013. Ordinary least squares regression was used to calculate how sensitive unemployment rates were in each state relative to overall US unemployment rates. The equation looks like this:

Ustate,t = αstate + βstateUUS,t + ϵstate,t

The intuition behind this equation is that the unemployment rate of a given state can be explained by the amount that it varies in proportion to the unemployment rate for the US as a whole (the beta term), a fixed difference (the alpha term), and the error term. Here were the results by State:

 State Alpha Beta Alpha SD Beta SD R-squared Alpha T-stat Beta T-Stat Correlation Group Michigan (2.50) 1.67 0.25 0.04 81.46% (9.98) 17.82 3 Nevada (2.44) 1.42 0.22 0.03 80.77% (11.22) 12.80 2 Indiana (2.47) 1.35 0.20 0.03 81.90% (12.42) 11.63 3 Alabama (1.80) 1.32 0.23 0.03 75.95% (7.72) 9.06 6 West Virginia 0.28 1.24 0.48 0.07 40.15% 0.59* 3.42 6 Ohio (1.10) 1.23 0.17 0.03 83.93% (6.49) 9.21 3 Rhode Island (1.03) 1.17 0.26 0.04 65.95% (3.88) 4.33 5 Illinois (0.51) 1.17 0.14 0.02 87.48% (3.64) 8.08 3 Tennessee (0.86) 1.17 0.15 0.02 85.25% (5.65) 7.29 3 North Carolina (1.44) 1.14 0.19 0.03 77.35% (7.40) 4.85 2 Oregon (0.00) 1.13 0.17 0.03 80.95% (0.03)* 5.11 3 South Carolina (0.72) 1.13 0.18 0.03 79.11% (3.94) 4.69 2 California 0.20 1.12 0.18 0.03 79.43% 1.14* 4.61 5 Washington 0.07 1.09 0.15 0.02 83.84% 0.49* 3.98 6 Florida (0.53) 1.07 0.18 0.03 78.09% (2.93) 2.80 5 Pennsylvania (0.33) 1.07 0.14 0.02 85.43% (2.40) 3.52 6 Wisconsin (1.30) 1.07 0.17 0.03 78.88% (7.49) 2.57 3 Arizona (0.50) 1.06 0.18 0.03 77.62% (2.81) 2.28 2 Kentucky 0.20 1.05 0.20 0.03 72.97% 1.00* 1.56* 3 New Jersey (0.10) 1.01 0.21 0.03 68.87% (0.47)* 0.32* 5 Mississippi 1.57 0.99 0.27 0.04 57.37% 5.86 (0.29)* 3 Missouri (0.28) 0.97 0.12 0.02 86.47% (2.35) (1.54)* 4 Georgia (0.22) 0.96 0.16 0.02 78.07% (1.37)* (1.81)* 2 Delaware (0.83) 0.95 0.20 0.03 67.93% (4.05) (1.73)* 1 Connecticut (0.41) 0.91 0.23 0.03 61.44% (1.80)* (2.79) 5 Utah (0.60) 0.88 0.15 0.02 76.69% (3.91) (5.13) 3 Idaho 0.36 0.88 0.20 0.03 65.13% 1.77* (4.09) 6 Colorado (0.01) 0.87 0.17 0.03 71.86% (0.04)* (5.06) 4 Maine 0.31 0.87 0.19 0.03 66.45% 1.61* (4.49) 1 Massachusetts 0.17 0.86 0.24 0.04 55.28% 0.72* (3.94) 5 Minnesota (0.29) 0.82 0.12 0.02 82.17% (2.43) (9.78) 3 District of Columbia 2.45 0.81 0.18 0.03 66.11% 13.40 (6.86) 1 New York 1.48 0.81 0.18 0.03 67.90% 8.45 (7.28) 1 Arkansas 1.46 0.79 0.18 0.03 66.27% 8.23 (7.87) 6 Virginia (0.30) 0.78 0.08 0.01 90.79% (3.84) (18.90) 1 Maryland 0.31 0.78 0.12 0.02 81.92% 2.66 (12.83) 1 Iowa (0.17) 0.77 0.19 0.03 61.15% (0.86)* (7.98) 3 Vermont 0.06 0.74 0.19 0.03 60.28% 0.34* (9.04) 1 Louisiana 2.45 0.73 0.38 0.06 26.41% 6.40 (4.69) 6 New Hampshire 0.12 0.68 0.20 0.03 52.48% 0.60* (10.73) 1 New Mexico 2.67 0.64 0.21 0.03 48.44% 12.78 (11.36) 6 Montana 1.84 0.61 0.21 0.03 45.25% 8.65 (12.16) 6 Oklahoma 1.49 0.59 0.22 0.03 41.23% 6.70 (12.25) 3 Wyoming 1.48 0.56 0.29 0.04 26.35% 5.08 (10.17) 3 Alaska 4.52 0.53 0.28 0.04 25.87% 16.05 (11.13) 6 Hawaii 1.46 0.52 0.27 0.04 27.58% 5.52 (12.07) 1 Texas 2.89 0.52 0.19 0.03 41.71% 15.10 (16.90) 4 Kansas 1.68 0.48 0.13 0.02 55.86% 12.53 (25.79) 4 Nebraska 0.61 0.46 0.13 0.02 54.10% 4.62 (27.48) 3 South Dakota 0.95 0.45 0.11 0.02 63.95% 8.93 (34.82) 3 North Dakota 1.49 0.39 0.18 0.03 31.54% 8.14 (22.19) 6

* Indicates not statistically significant from zero for alpha, and one for beta at a 5% level.

The difference in sensitivity to the US unemployment rate is considerable by state. If the unemployment rate rose 1% in the US, Michigan’s unemployment rate would tend to rise 1.67%, while the North Dakota’s unemployment rate would only tend to rise 0.39%.

The states were then divided into five beta groups, symmetric around 1.0, with a width of 0.2 for the three middle groups. On a map, it looks like this:

The highest sensitivity states to US unemployment rates are largely found in states with high exposure to the Auto and Gambling industries. When times are bad, people shepherd their money more carefully. They cut back on buying new cars, and gambling. High sensitivity states tend to have a lot of gearing to industrial activity, which tends to be more boom-bust than other economic activity. Average sensitivity states tend to have balanced economies, reflecting a mix of business similar to that of the US as a whole. Low-sensitivity states tend to have a large amount agriculture, resource extraction, financial sector concentration, or Federal government work.

Note that the recent boom and bust would argue that financials are more cyclical than previously believed, but that was during a small period during the study period.  The same applies in reverse to agriculture and resource extraction, which benefited from increased demand for raw materials from the developing world, making these industries appear less cyclical than previously believed.

Betas reflect the overall sensitivity to moves in US unemployment rates from 1976 to 2013, but the correlation of the residuals of the states highlight hidden factors that were influential in unemployment rate movements.

Typically, the factors stemmed from the economic sectors prominent in each group of states, as their profitability waxed and waned.

Starting with ten groups of states randomly divided, the groups were iteratively adjusted, combining groups that were highly correlated with each other until there were no more improvements possible, ending with six groups. Here is the average correlation matrix:

 Avg Corr 1 2 3 4 5 6 Group 1 40% Group 2 -4% 43% Group 3 -34% -15% 43% Group 4 -37% 12% 24% 36% Group 5 41% 26% -51% -22% 61% Group 6 -14% -43% 30% -5% -46% 50%

And here is the map identifying the groups:

Groups 1, 2 and 5 correlate strongly internally and moderately among each other. The same is true for 3, 4 and 6. The rest of the group correlations are weak if not negative.

Groups 3, 4, and 6 cover the center of the US. They have proportionately more economic sectors in agriculture, energy, consumer cyclicals, and basic materials.  Much of the area is rural. Groups 1, 2 and 5 cover the coasts of the US and are more heavily urbanized. Their economic sectors have a greater proportion of finance, healthcare, and technology.  Post-2007 unemployment was relatively worse in groups 1, 2 and 5 versus the other groups, because they were part of the hot housing markets, and lost more construction jobs as a result.

Here is a graph of the average unemployment residuals for the six correlation groups over the 36-year study period:

Description of the Correlation Groups

Group 1 – composed of Maryland, other Mid-Atlantic States, New England and Hawaii, this — had high unemployment relative to the rest of the US in 1976 and 1997, and low unemployment in 1987. It has high relative exposure to the consumer noncyclicals and financials sectors, and low relative exposure to energy and technology. The high weight in financials helps explain the employment gains from 1976 to 1987, as financial companies benefited from falling interest rates, rising equity markets, and expanding product offerings.

Group 2 – composed of the Carolinas, Georgia, Arizona and Nevada — had high unemployment relative to the rest of the US in 2011, and low unemployment in 1984 and 1991. It has a lot of relative exposure to the consumer noncyclicals and utilities sectors, and low relative exposure to energy, financials, and technology.  During the mid-1980s to early 1990s, this group benefited from the growth in demand for noncyclical goods from the Baby Boomers. After the popping of the financial bubble in 2008, weakness in construction and gambling in Arizona and Nevada led to higher levels of unemployment.

Group 3 – composed of the Midwest, parts of the South, Utah and Oregon — had high unemployment relative to the rest of the US in 1976 and 1992, and low unemployment in 1986. It has high relative exposure to the consumer cyclicals and noncyclicals and basic materials sectors, and low relative exposure to energy and technology. The US economy as a whole peaked and troughed along with group 3, which makes sense given their relatively large exposure to cyclical sectors.

Group 4 – composed of Texas, Missouri, Kansas and Colorado — had high unemployment relative to the rest of the rest of the US in 1987 and 2003, and low unemployment in 1976. It has a lot of relative exposure to the energy and utilities sectors, and low relative exposure to financials and technology. Performance of the energy sector is the critical factor here – it was relatively strong in the mid-to late 1970s, but weak after oil prices bottomed out in the mid-1980s and late 1990s.

Group 5 – composed of the densely populated coastal states of California, Florida, New Jersey, Massachusetts, Connecticut and Rhode Island — had high unemployment relative to the rest of the rest of the US in 1976, 1992 and 2012, and low unemployment in 1986. It has a lot of relative exposure to the healthcare and technology sectors, and low relative exposure to energy and consumer noncyclicals. In the early 1990s, the aerospace industry in California went bust while the commercial property markets were at the deepest point of their slump. Most of the rest of the unemployment cyclicality can be attributed to the more cyclical nature of the industries in this group – an amplified version of the US economy.

Group 6 looks like a bunch of leftovers, but it is not.  Composed of states in the Northwest and Alaska, New Mexico, Louisiana, Arkansas and Alabama, West Virginia and Pennsylvania, this group had high unemployment relative to the rest of the rest of the US in 1987, and low unemployment in 1976 and 2009.  It has a lot of relative exposure to the agriculture and basic materials sectors, and low relative exposure to financials. The stagflation of the mid-1970s benefited agriculture and basic materials, as did growth in demand from emerging markets in 2009. Those factors were
absent in 1987, as financial firms were booming.

Maryland’s unemployment rates have held down well being next to Washington, DC. The growth in the US government during the last 10 years has supported employment in Maryland. The grand question to ponder is what would ever happen to Maryland, Washington, DC and Virginia if significant cuts were made to Federal payrolls?

Conclusion

There are two main conclusions:

1) State level unemployment is a result of sensitivity to US unemployment levels and the mix of local industries. Policymakers should know how sensitive their state is to the national economy, and what industries are doing well or poorly before taking credit for low unemployment rates. More often than not, the employment rates are low or high due to factors beyond the control of policymakers.

2) In general, greater employment stability exists when that industry mix is more diversified. This is something policymakers can limitedly affect. Most states have efforts to attract businesses to their states. If you want unemployment levels to be more stable, aim your efforts at attracting businesses that diversify your existing mix.

### On Target Prices & Yields

Saturday, February 1st, 2014

I know that much of the money management business sets target prices for buying and selling, particularly value managers, and sell-side analysts.  I don’t set target prices.  Why?

Think of what a target price means.  It says that at a certain price you are willing to exchange securities for cash (sell), or vice-versa (buy).  The trade-off between an individual security and cash is difficult to calculate.  Even if you have a really good dividend discount model, the target prices are very sensitive to model inputs.  I think the question of whether I would rather have cash or an individual stock or bond is a difficult question.

So why don’t we focus on easier questions?  It is simpler to rank stocks versus other stocks at least in broad, and bonds versus bonds.  I am not saying that you have to optimize.  You can’t be exact in ranking the desirability of stocks or bonds, but if we can’t identify a group of stocks outside the portfolio that are better than a group of stocks inside the portfolio, there is not much sense in trading.  Same for bonds.

Thus, most of my portfolio management is not so much “Aim for the best.”  I’m not sure I can do that.  “Aim for something better than what I currently have” is achievable.  In cases where I can find no clear improvements, sitting on my hands it the best strategy.  After all, time is on the side of a portfolio representing great relative value.

This is not to denigrate those that are better than me, like Seth Klarman.  He has a strong sense of when he would rather hold cash versus taking any risk, and so he manages value in an absolute sense, even giving back money to clients when  he doesn’t have anything to do with it.

I’m still finding some attractive assets to buy, though not many.  Later this month, I will do my formal quarterly reshaping of the portfolio, where I will trade away a few stocks I like less for those I like more.  And if I can’t find any that I like more, I don’t have to do anything, because if I’ve got a really good group of stocks, doing nothing may be the best idea of all.

PS — If you want more, some of the details are in Portfolio Rule Eight.

### On Position Sizing in Equity Long-Short Hedge Funds

Tuesday, January 28th, 2014

This article is prompted by the following article by John Hempton of Bronte Capital.  This is not meant as a criticism of him; I have nothing but respect for him.  The article triggered memories of my own experiences with position sizing at a hedge fund.

The hedge fund I once worked for had great expertise with financial companies, and I worked for them in the boom years of the 2000s.  Our leader was bearish on depositary financials, a view that would eventually be right.  Of course “eventually right” is another way to say “wrong in the short run.”

Let me describe the problem from another angle.  When I was a corporate bond manager, I would mentally set three levels with the bonds that I held.

• Spread necessary for an ordinary-sized position.
• Spread necessary for a big position.
• Spread necessary for a maximum position.

These spreads I would adjust for premium vs discount, optionality, and a bunch of other things.  The point is that I would always have a schedule for where I would be willing to buy more, or lighten up (sell some).  I often dealt in some of the least liquid corporate bonds, and I was patient, and even willing to break rules by holding more than 20% of a given issue.  My analysts almost always did good work, and I trusted them.

When markets are illiquid, they “trade by appointment.”  If you have a balance sheet behind you that is not worried about liquidity, you can do interesting things by buying assets that most ordinary managers won’t touch, because the issue is too small.

I came to the hedge fund after I managed corporate bonds.  In one sense, I had managed a far more complex long-only portfolio.  But being able to short creates complexities of its own.

I can’t tell you how many times at meetings at the hedge fund we had tough discussion on position sizing, more frequently on short positions. We were perpetually long quality, short market capitalization, long insurers, short banks, and long value.  Great idea, if too early. This would be an extreme example:

Boss: “This short position is killing us, it is up 50% from where we shorted it, and now we have a 6% short position, what do we do?”

Others answered in front of me, essentially suggesting no change.  He asked me personally and I said:

David: “If you had no position, and you were approaching this company today, what would you do?”

Boss: “I would short the maximum — 4%.”

Boss: “But that locks in the loss.”

David: “Do you want to risk locking in a bigger loss?”

The boss once said to me that I was the only one on his team that was natively a portfolio manager rather than an analyst.  (That said, I remained an analyst, while an analyst was made an assistant portfolio manager.  I think it would have been too difficult to have the insurance guy to manage the portfolio of what was a banking shop.  That said, as a corporate bond manager, I managed the financials, which were mostly banks.)

Setting position sizes on shorts is always harder than longs.  When your thesis goes wrong on a short, your risk increases, as the position size gets larger.  When it goes wrong on a long position your risk decreases, as the position size gets smaller.

As I have often said, being short is not the opposite of being long, it is the opposite of being leveraged long.  When you are short, or leveraged long, you do not fully control your trade.  The margin desk can take you out of your trade if the equity in the account gets small enough.  They are ruthless in doing so, because the margin desks at brokerages do not want to take losses.

That makes it all the more important to set a schedule of sizes on short positions.  The first question should be: at what price would I put my maximum position on?  That would help in sizing introductory and normal positions.  They would be far smaller than what most hedge funds do.

Again, the same exercise is easier in a long-only format, but the protocol is the same.  Establish introductory, normal and maximum position sizes, and hold to them.  Also put into effect the idea that analysts must give greater scrutiny to large positions.

All That Said

This is a reason I am not a fan of most hedge funds.  I believe in the funds of my former employer and those of Mr. Hempton.  But the difficulties of dealing with bad decisions with a weak balance sheet kills a lot of hedge funds.  Long only — it might survive.  But when you go long and short with leverage, the risk arises of total loss.

So don’t think you are a “cool kid” because you invest in hedge funds.  Long only does better over the long haul, because it is less risky, and compounds value.

# Disclaimer

David Merkel is an investment professional, and like every investment professional, he makes mistakes. David encourages you to do your own independent "due diligence" on any idea that he talks about, because he could be wrong. Nothing written here, at RealMoney, Wall Street All-Stars, or anywhere else David may write is an invitation to buy or sell any particular security; at most, David is handing out educated guesses as to what the markets may do. David is fond of saying, "The markets always find a new way to make a fool out of you," and so he encourages caution in investing. Risk control wins the game in the long run, not bold moves. Even the best strategies of the past fail, sometimes spectacularly, when you least expect it. David is not immune to that, so please understand that any past success of his will be probably be followed by failures.

Also, though David runs Aleph Investments, LLC, this blog is not a part of that business. This blog exists to educate investors, and give something back. It is not intended as advertisement for Aleph Investments; David is not soliciting business through it. When David, or a client of David's has an interest in a security mentioned, full disclosure will be given, as has been past practice for all that David does on the web. Disclosure is the breakfast of champions.

Additionally, David may occasionally write about accounting, actuarial, insurance, and tax topics, but nothing written here, at RealMoney, or anywhere else is meant to be formal "advice" in those areas. Consult a reputable professional in those areas to get personal, tailored advice that meets the specialized needs that David can have no knowledge of.