Sometime in the next few weeks, I am going to dig into my pre-2003 [pre-RealMoney] files and see if there is anything there to share with readers.  Most of my best stories I have already told in my various series.  The one I will tell tonight I don’t think I have told.

In 1994, we had a problem at Provident Mutual’s Pension Division.  Our main external equity manager was having a very lousy year as value managers that focused on absolute yield were getting taken to the cleaners.  This was after a few years of poor performance — the joke was, given the great performance of the past, “Hey, can you develop the 19-year track record?”  (The last 5 years as a group were horrid, but the previous 14 were great.)

Aside: there aren’t many absolute yield managers in equities today.  Back when dividend yields were higher, and corporate bond yields were higher, both absolute and relative yield managers flourished as interest rates and dividend yields crested in the early 1980s, and the stocks paying high dividends got bid up as interest rates fell, much as the same thing happened to zero coupon and other noncallable long duration bonds.

The process started with a call from a manager of managers who proposed that we start up “multiple manger funds,” where we would be the manager of managers.

This offered several advantages:

  • It offered us an easy out with our long-held failing manager, because we are not firing them, just making them a portion of the assets in the value fund.
  • It would make eliminating them easier in a second step, with less PR damage.
  • It would make us look like we were taking action and control in a new way for our clients. (They loved it.)
  • As it was, we did a good job selecting managers, and the funds performed well.
  • We could negotiate lower fees with the managers,
  • It gave us a great marketing story.
  • Our margins and growth improved.

I was critical to the process, being the only member of the team with investment expertise.  Everyone else was a marketer or the divisional head.  (I take that back, one member of the marketing area was genuinely sharp with investments.)  After we chose the managers, I set the allocations.

Now onto tonight’s topic (what a long intro): At the beginning of our relationship with the manager of managers, they did a traditional holdings-based analysis of how a manager managed assets.  About one year into the process, they introduced returns-based style analysis.

Though the Wikipedia article just cited has a bevy of errors, it will still give you a flavor for what it is.  Let me give my own explanation:

It takes a lot of effort and wisdom to look at quarterly portfolio snapshots and analyze what a manager is doing.  You almost have to be as wise as the manager himself to analyze it, but many fund analysts developed the skill.

But returns-based style analysis offered the holy grail: we can understand what the manager is doing simply by comparing the returns of the manager versus returns on  variety of asset indexes, using constrained multiple regression.

The idea was this: the returns of a manager are equal to his alpha versus a composite index that best fits his performance.  Since we were dealing with long-only managers, the weights on the index components could not be negative.

The practical upshot to the manager of mangers was: “Whoopee!  We can analyze every manager under the sun just by looking at their return patterns.  No more time-consuming work.”

After the first meeting with the manager of managers, I expressed my doubts, and asked for a special meeting with their quants.  A week later, I had a meeting with a few members of their staff, of which one was the quant, a nice lady 10 years my junior, who I felt sorry for.  She started her presentation at a very basic level, and asked “Do you have any questions?”  I asked, “Isn’t this just an quadratic optimization problem where you are choosing weights on the convex hull?”  She paused, and said, “Oh, so you *do* understand this.”  The meeting ended son after that — we agreed on the math, and in math, there is no magic.

But that placed me on the warpath; I genuinely felt the advice we were getting had declined in value.  I wrote a 16-page report to our manager explaining why returns-based style analysis was inferior.

  • There is no way to correctly estimate error bounds, because of nonlinear constraints.  (Note: two years later, I guy came up with an approximate way to do it in an article in the Financial Analysts Journal.  I called him, and we had a great talk.  That said, approximate is approximate, and I haven’t seen any adopt it.)
  • Because many of the indexes are highly correlated with each other, small differences in manager returns make a huge difference in the weight calculated for each index.
  • If a manager is changing investments because he senses a factor like market cap size or valuation is cheap, it will get interpreted as a change in his index, and will not come out as alpha, but as beta.
  • If I don’t believe that the CAPM and MPT are valid, why should I believe this monstrosity?
  • And more… I hope I find my 16-page paper in my files.

After six more months we terminated the manager of managers, and hired a better one.

  • Lower fees
  • Lower fees from managers (they had greater bargaining power)
  • We reduced our fees to clients
  • Better marketing name
  • Holdings based manager analysis

After that, things were much better, and we continued to grow.

My years at Provident Mutual were exceedingly fruitful — this was just one of many areas where my efforts paid off well.

All that said, there is no way to fix returns-based style analysis.  It is a bogus concept and needs to be abandoned.  Those who use it do not grasp the limits of econometrics, and are Sorcerer’s apprentices.

PS — Need I mention that the originator of the idea, Bill Sharpe, is not all that sharp with econometrics?  He’s a bright guy, but it is not his strong suit.

PPS — there are not many actuaries with a background in econometrics.  That is why I have written this.

There are several reasons to avoid illiquidity in investing, and some reasons to embrace it.   Let me go through both:

Embrace Illiquidity

  • You are offered a lot of extra yield for taking on a bond that you can’t easily sell, and where you are convinced that the creditor is impeccable, and there are no sneaky options that you have implicitly sold embedded in the bond to take value away from you.
  • An unusual opportunity arises to invest in a private company that looks a lot better than equivalent public companies and is trading at a bargain valuation with a sound management team.
  • You want income that will last for your lifetime, and so you take some of the money you would otherwise allocate to bonds, and buy a life annuity, giving you some protection against longevity.  (Warning: inflation and credit risks.)
  • In the past, you bought a Variable Annuity with some good-looking guarantees.  The company approaches you to buy out your annuity at a 10-20% premium, or a 20-30% premium if you roll the money into a new variable annuity with guarantees that don’t seem to offer much.  Either way, turn the insurance company down, and hold onto the existing variable annuity.
  • In all of these situations, you have to treat the money as money lost to present uses.  If there is any significant probability that you might need the money over the term of the asset, don’t buy the illiquid asset.

Avoid Illiquidity

  • Often the premium yield on an illiquid bond is too low, or the provisions take value away with some level of probability that is easy to underestimate.  Wall Street does this with structured notes.
  • Why am I the lucky one?  If you are invited to invest in a private company, be skeptical.  Do extra due diligence, because unless you bring something more than money to the table (skills, contacts), the odds increase that they are after you for your money.
  • Often the illiquid asset is more risky than one would suppose.   I am reminded of the times I was approached to buy illiquid assets as the lead researcher for a broker-dealer that I served.
  • Then again, those that owned that broker-dealer put all their assets on the line, and ended up losing it all.  They weren’t young guys with a lot of time to bounce back from the loss.  They saw the opportunity of a lifetime, and rolled the bones.  They lost.
  • We tend to underestimate how much we might need liquidity in the future.  In the mid-2000s people encumbered their future liquidity by buying houses at inflated prices, and using a lot of debt.  When everything has to go right, the odds rise that everything will not go right.
  • And yet, there are two more more reason to avoid illiquidity — commissions, and inability to know what is going on.


Illiquid assets offer the purveyor of the assets the ability to pay a significant commission to their salesmen in order to move the product.   And by “illiquid” here, I include all financial instruments that carry a surrender charge.  Do you want to know how much the agent made selling you an insurance product?  On single-premium products, it is usually very close to the difference between the premium you paid, and the cash surrender value the next day.

Financial companies build their margins into their products, and shave off a portion of them to pay salesmen.  This not only applies to insurance products, but also mutual funds with loads, private REITs, etc.  There are many brokers masquerading as financial advisers, who do not have to act strictly in the best interests of the client.  The ability to receive a commission makes them less than neutral in advising, because they can make a lot of money selling commissioned products.  In general, it is good to avoid buying from commissioned salesmen.  Rather, do the research, and if you need such a product, try to buy it directly.

Not Knowing What Is Going On

There are some that try to turn a bug into a feature — in this case, some argue that the illiquid asset has no volatility, while its liquid equivalents are more volatile.  Private REITs are an example here: the asset gets reported at the same price period after period, giving an illusion of stability.  Public REITs bounce around, but they can be tapped for liquidity easily… brokerage commissions are low.  Some private REITs take losses and they come as a negative surprise as you find  large part of your capital missing, and your income reduced.

What I Prefer

In general, I favor liquid investments unless there is a compelling reason to go illiquid.  I have two private equity investments, both of which are doing very well, but most of my net worth is tied up in my equity investing, which has done well.  I like the ability to make changes as time goes along; there is value to being able to look forward, and adjust.

No one knows the future, but having some slack capital available to invest, like Buffett with his “elephant gun,” allows for intelligent investing when liquidity is scarce, and yet you have some.  Many wealthy people run a liquidity “barbell.”  They have a concentrated interest in one company, and balance that out by holding very safe cash equivalents.

So, in closing, avoid illiquidity, unless you don’t need the money, and the reward is very, very high for making that fixed commitment.

FinEconMost of my readers are not going to want to buy this book, because they are not inclined toward math.  But for those that are math-inclined, I would encourage you not to buy the book.  Why?

Well, there are much better books on Econometrics out there, that could teach the subject better.  I can safely say that no Econometrics class would use this book as a text.

Beyond that, the book does not come up with a lot of areas where “this is where you have to be careful in using regression on econometric data.”

I did learn a few things from the chapter on factor analysis, but that is not typically classified as econometrics.

As such, I don’t see any class of people that would benefit from this book.


Already mentioned.


There is no good audience for this book.  If you still want the book, you can buy it here: The Basics of Financial Econometrics: Tools, Concepts, and Asset Management Applications (Frank J. Fabozzi Series).

Full disclosure: The publisher asked me if I would like a copy and I said yes.

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.

71zM0CNU4QL This is an ambitious book.  It tries to draw together financial statement analysis, value investing, short-selling, technical analysis, market timing, and portfolio management into one slim book of 254 pages.

It spends the most time on financial statement analysis, going over revenue recognition, inventories, and all of the squishier areas of accounting that most industrial companies face.  It will not help you much with financial companies, they are far more complex, and deserve a book all their own.

I was surprised that the book did not suggest common summary measures of accounting quality, such as Normalized Operating Accruals.  It did feature Cash Flow from Operations less Net Income, which is almost as good.

The book focuses on the short side — how do you make money from failure?  The long side suggests maxing out on small cap value stocks, and idea which  I like, but can get overfished at times.

Think of it this way: do you want to run a portfolio that is systematically short company size, long value, short liquidity, long quality, etc?  I helped do that for 4.5 years at a hedge fund, and boy that ride was bumpy.  The market can remain insane longer than you can remain solvent.

But, to the book’s credit, it understands position sizing for short positions, which is momentum following.  Short more of things that fall.  Do not add to shorts when the prices rise.  This is a key insight of the book, and it is a reason why value managers often don’t do well in a long-short context.

My last complaint is that the book does not explain even in broad terms how they balance the various portfolio management ideas.  If you buy this book, you are on your own.  You do not  have a full roadmap to guide you.  If you were going to use this as a main strategy, you would have to fill in a lot of holes.

Now, I’m often critical of turn-the-crank books — follow my rules, and you will make money.  But I am more critical of almost turn-the-crank books — follow my rules, and you still won’t know exactly what to do.

Is this a good book?  Yes.  Read it and you will learn a lot.  Will it help you analyze stocks?  Also yes.  You can make a lot more money by avoiding stocks with a high probability of losing money.  Will it tell you exactly what to do?  No.  That is a strength and a weakness — I’m not sure any book on investing that offers a formula can be exact, and be good.  Investing is an art, not a science.  Then again, science is an art, not a science, but that’s another topic — all the great discoveries come from not following the scientific method.

So if you want to learn, this is a good book.  If you want a foolproof way to make money, sorry, this won’t do it for you, and the same for almost every other investment book.


There are far better books on all of the topics that they cover, and most of them have been reviewed at my blog.  Far better to read books that specialize on a single topic, than one that is a hodgepodge.


This is a good book, but average investors should not buy it as a formula, because they can’t implement it.  Average investors could benefit from the book, because it gives them a taste of a wide number of investing topics.  Just be aware that you aren’t getting a full dose of anything.  If you still want that, you can buy it here: What’s Behind the Numbers?: A Guide to Exposing Financial Chicanery and Avoiding Huge Losses in Your Portfolio.

Full disclosure: I borrowed this book via Interlibrary Loan.  It is going back tomorrow, and I will not buy a copy to replace it.

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.

Industry Ranks 6_1521_image002

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 retail 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 reinsurers, but I have been adding to P&C insurers. 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 tightened my criteria a little because the number of stocks passing last quarter’s screen was much higher, which was likely an artifact of earnings expectations rolling forward another year.

Anyway, enjoy the list of purchase candidates — I know that I will:

Industry Ranks 6_19997_image002

Full Disclosure: long SYMC

52wk I usually don’t like reviewing books that say, “Follow this formula, and you will make lotsa money.  Thus it was with some hesitance that I requested this book.  I did it partly off of Tweedy, Browne’s study, which is aptly titled, “What Has Worked in Investing.”  For those reading at Amazon, Google “Tweedy Browne What has Worked” for the link.  Stocks that hit new 52-week lows on average are ready to rebound.  So why don’t people buy them?

Are you kidding?  Look at that chart!  Do you really want to catch a falling knife?!  You want to throw good money after bad!?  Why do you want to buy that dog, anyway…

Shhh.  The competition is gone.  There are no friends of failure.  But made some companies get unfairly tarred as losers, when it is simply a good company that made a few mistakes.

That is the idea behind this book.  Analyze companies from which most market players  have fled.  Look for those with  the following characteristics:

  1. They must have a durable competitive advantage.
  2. They must must a strong free cash flow yield.
  3. They must have a return on invested capital that exceeds the cost of that capital.
  4. They must not have too much debt relative to free cash flow.

I Had Troubles Getting to Solla Sollew

But here’s the big problem, and advantage, of the book.  He does not give you the “secret sauce.”  He gives you the principles.  Indeed he can’t give a formula, because many of his criteria don’t admit an easy formula.  You can’t calculate free cash flow from looking at GAAP accounting — you would need to know what portion of capital expenditure is to maintain existing assets, and that is nowhere disclosed.  Typically, when you see free cash flow in screening software, all capital expenditure is deducted from cash flow from operations, producing too conservative of a figure.

Thus we can’t replicate points 2 & 4.  What about 1 & 3?  Companies do not comes with tags saying “Durable Competitive Advantage” and “No Durable Competitive Advantage.”  That is a judgment call.  You could use Morningstar’s Moat Ratings, or Gross Margins as a fraction of assets.  The author does not give explicit guidance.  As to point 3, the main problem is that we don’t know what a company’s cost of capital is.  There are a lot of assumptions lying behind that, and they matter a great deal.

The easiest of his five criteria to calculate is the price vs the 52-week low.  Still, he doesn’t give us a threshold.

So What Good is This Book?!

Unless you are an expert, not much good, unless you simply want to play the 52-week low anomaly.  That said, actionable strategy would be to review the 52-week lows, and analyze companies with low debt and high past profitability that seem to have a franchise that is not easily attacked.  I think the theory is solid.  That said, it does no give a lot of the details, not that most readers would understand it if they did.

This book is good, in that it is realistic.  Though not explicit, it informs you that it is very difficult to choose superior stocks, and it it does not give you a cut-and-dried method.

So If You Can’t Do It Yourself, Then What Is This Book?!

Though the disclosure at the end says otherwise, this book is an advertisement for the author’s method of money management.  In none of his five criteria does he get sharp.  The general principles are correct, but you aren’t given the tools to use them.  That means if you want to use them, you must go through the author.


They have a website —, but it is not laden with data as the book intimates, as of the day that I write this.  That would be worth seeing.


On pages 74-75 he gives a strained view of margin of safety, comparing free cash flow yields to the 10-year Treasury yield.  Margin of safety is more of a balance sheet construct, asking how likely is is that a company will get into financial stress.  What he is actually measuring here is valuation.  What he is doing is not wrong, but it is mislabeled.  Also remember, you can estimate free cash flow, but you never know for sure.

Also, as mentioned before, we have no idea of what his thresholds are and how he actually implements the strategy.

Thus after this article are two attempts to work out the strategy.  What should not be surprising is that there are no companies on both lists.


This is a good book, but average investors should not buy it, because they can’t implement it.  If you still want that, you can buy it here: The 52-Week Low Formula: A Contrarian Strategy that Lowers Risk, Beats the Market, and Overcomes Human Emotion.

Full disclosure: The PR flack asked me if I wanted the book, and I said “yes.”

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.

Application Attempt One

These were the companies selected — Morningstar Wide Moat, 5% Free Cash Flow Yield, Less than 20% above the 52-week low.


And here is the second try: Gross margins as a ratio of Assets over 13%, free cash flow yield over 5%, Long-term debt as a ratio of free cash flow greater than five, less than 20% above the 52-week low.


Not one alike on the two lists.  Tells you that his book would be very difficult to implement.  *I* don’t know how I would implement it.

I read an article today, The Fallibility of the Efficient Market Theory: A New Paradigm  Good article, made me look through a major article cited: An Institutional Theory of Momentum and Reversal.

The former article explains in basic terms what the authors have illustrated.  The latter article, provides all of the complex math.  I get 50%+ of  it, and I think it is right.  This explains value, momentum, and mean-reversion, the largest anomalies that trouble the Efficient Markets Hypothesis.

This article deserves more attention from quants and academics.  The only thing that troubles me about it is that they assume a normal distribution for security returns.

Have a read, and for those that can understand the math, if you disagree with it, let me know.

I get a lot of interesting letters — here is another one:

First, let me say how much I appreciate your blog. I started my career in sellside research covering life insurers (after interning in insurance M&A). Your posts on insurance investing were invaluable in developing my understanding of the industry. My superiors did not have time to teach me the basics – I would have had a hard time getting started without your blog. 

 I’m now in equity research at a large mutual fund company, also covering insurers (and asset managers). However, I do not have an actuarial background. So I am very interested in why you think financial & mortgage insurers don’t have an actuarially sound business models. 

 And as a former life insurance analyst, I am curious what aspect of life insurance reserving you view as liberal – I’m guessing secondary guarantees on VAs? 

 Finally, to digress, do you have any views on medical malpractice insurance? I’ve been looking at PRA, and find it pretty compelling at first glance: massive excess capital, consistently conservative and profitable underwriting, and a relatively reasonable valuation. 90% of policies are claims made. There are headwinds: Obamacare, the reserve releases from mid-2000s accident years rolling off, and a diversifying business model (although PRA has historically proven competent at M&A). My only concerns are management continuing to underwrite at too low a level (currently writing at 0.32x NPW / Equity; regulators would be fine with up to 1.0x), and potentially squandering that capital. 

In the interest of full disclosure, I own no insurance stocks personally for compliance reasons.

Thanks for writing.  Let’s start with mortgage and financial insurance.  It’s not that there isn’t a good way to calculate the risk (in most cases), it is that they do not choose to use those models.  The regulators do not subscribe to contingent claims theory.  They do not look at default as an option, even if it is not efficiently exercised.  They should use those models, and assume efficient execution of default risk.

Even if they use approximations, the recent crisis should have forced reserves higher for mortgage credit, and other credit exposures.

Credit and mortgage insurers are bull market stocks.  When I was a bond manager, I sold away my few financial insurer bonds from MBIA and Ambac, and avoided the mortgage insurers.  The possibility of default was far higher than he market believed.

With respect to Life Insurers, it is secondary guarantees of all sorts, especially with variable products.  Options that have a long duration are hard to price.  Options that have a long duration, and involve significant contingencies where insureds may make choice hurting the insurer are impossible to price.

On Medmal, I have always liked PRA, but it has never been cheap enough for me to buy it.  Always thought they were the best of the pure plays.  They have survived many other companies by their clever management.  I would not begrudge them their conservatism, Medmal is volatile, and it pays to be conservative in volatile businesses.

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.


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:




 Standard Error


 Logarithm of 3-month average volume




 Logarithm of Market Capitalization




 Logarithm of Market Capitalization, squared




 Basic Materials




 Capital Goods








 Consumer Cyclical




 Consumer Non-Cyclical












 Health Care





















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


























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



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


TickerCompanySectorExcess analysts
BRK.ABerkshire Hathaway Inc.07 – Financial(25.75)
GEGeneral Electric Company02 – Capital Goods(20.47)
XOMExxon Mobil Corporation06 – Energy(19.32)
CVXChevron Corporation06 – Energy(14.64)
PFEPfizer Inc.08 – Health Care(14.57)
MRKMerck & Co., Inc.08 – Health Care(12.76)
GOOGGoogle Inc10 – Technology(11.44)
JNJJohnson & Johnson08 – Health Care(11.39)
MSFTMicrosoft Corporation10 – Technology(10.39)
PMPhilip Morris International In05 – Consumer Non-Cyclical(10.21)


Too many


TickerCompanySectorExcess analysts
VVisa Inc09 – Services 2.58
DISWalt Disney Company, The09 – Services 2.95
SLBSchlumberger Limited.06 – Energy 4.15
CSCOCisco Systems, Inc.10 – Technology 5.22
QCOMQUALCOMM, Inc.10 – Technology 5.34
ORCLOracle Corporation10 – Technology 5.98
FBFacebook Inc10 – Technology 8.28, Inc.09 – Services 9.34
AAPLApple Inc.10 – Technology 10.57
INTCIntel Corporation10 – Technology 11.85


Large Cap Stocks


TickerCompanySectorExcess analysts
SPGSimon Property Group Inc09 – Services(16.15)
BF.BBrown-Forman Corporation05 – Consumer Non-Cyclical(16.03)
LUKLeucadia National Corp.07 – Financial(15.93)
LLoews Corporation07 – Financial(15.90)
EQREquity Residential09 – Services(15.87)
ARCPAmerican Realty Capital Proper09 – Services(15.75)
IEPIcahn Enterprises LP09 – Services(15.50)
LVNTALiberty Interactive (Ventures09 – Services(15.36)
ABBVAbbVie Inc08 – Health Care(15.01)
GOM CLAlly Financial Inc07 – Financial(14.87)


Too Many


TickerCompanySectorExcess analysts
UAUnder Armour Inc04 – Consumer Cyclical 16.68
BRCMBroadcom Corporation10 – Technology 17.29
RRCRange Resources Corp.06 – Energy 17.33
SWNSouthwestern Energy Company06 – Energy 17.70
RHTRed Hat Inc10 – Technology 18.08
NTAPNetApp Inc.10 – Technology 19.82
CTXSCitrix Systems, Inc.10 – Technology 19.84
COHCoach, Inc.09 – Services 20.87
VMWVMware, Inc.10 – Technology 21.60, inc.10 – Technology 22.64


Mid cap stocks


TickerCompanySectorExcess analysts
FNMAFederal National Mortgage Assc07 – Financial(13.84)
UHALAMERCO11 – Transportation(12.23)
ORealty Income Corp09 – Services(12.06)
CIMChimera Investment Corporation07 – Financial(11.49)
SLGSL Green Realty Corp09 – Services(11.46)
NRFNorthstar Realty Finance Corp.09 – Services(11.34)
FMCCFederal Home Loan Mortgage Cor07 – Financial(11.14)
EXRExtra Space Storage, Inc.11 – Transportation(10.97)
KMRKinder Morgan Management, LLC06 – Energy(10.94)
CWHCommonWealth REIT09 – Services(10.51)


Too Many


TickerCompanySectorExcess analysts
AEOAmerican Eagle Outfitters09 – Services 17.00
DRIDarden Restaurants, Inc.09 – Services 17.40
RVBDRiverbed Technology, Inc.10 – Technology 17.50
CMAComerica Incorporated07 – Financial 17.74
GPNGlobal Payments Inc07 – Financial 18.30
WLLWhiting Petroleum Corp06 – Energy 19.67
DODiamond Offshore Drilling Inc06 – Energy 21.57
URBNUrban Outfitters, Inc.09 – Services 24.06
RDCRowan Companies PLC06 – Energy 24.48
ANFAbercrombie & Fitch Co.09 – Services 26.02



Small cap stocks


TickerCompanySectorExcess analysts
BALTBaltic Trading Ltd11 – Transportation (7.96)
ERAEra Group Inc11 – Transportation (7.45)
PBTPermian Basin Royalty Trust06 – Energy (7.42)
SDRSandRidge Mississippian Trust06 – Energy (7.18)
PHOTGrowlife Inc02 – Capital Goods (6.79)
SBRSabine Royalty Trust06 – Energy (6.74)
CAKCAMAC Energy Inc06 – Energy (6.64)
FITXCreative Edge Nutrition Inc09 – Services (6.57)
BLTABaltia Air Lines Inc11 – Transportation (6.53)
VHCVirnetX Holding Corporation10 – Technology (6.49)


Too many


TickerCompanySectorExcess analysts
WLTWalter Energy, Inc.06 – Energy 12.19
ANGIAngie’s List Inc10 – Technology 12.31
FRANFrancesca’s Holdings Corp09 – Services 12.58
ZUMZZumiez Inc.09 – Services 13.49
GDPGoodrich Petroleum Corp06 – Energy 15.02
DNDNDendreon Corporation08 – Health Care 15.89
ACIArch Coal Inc06 – Energy 16.04
HEROHercules Offshore, Inc.06 – Energy 16.19
AREXApproach Resources Inc.06 – Energy 17.64
AROAeropostale Inc09 – Services 20.80


Microcap Stocks


TickerCompanySectorExcess analysts
SGLBSigma Labs Inc06 – Energy (6.18)
AEGYAlternative Energy Partners In10 – Technology (5.97)
WPWRWell Power Inc06 – Energy (5.83)
TTDZTriton Distribution Systems In10 – Technology (5.53)
SFRXSeafarer Exploration Corp11 – Transportation (5.15)
PTRCPetro River Oil Corp06 – Energy (4.99)
UTRMUnited Treatment CentersInc08 – Health Care (4.82)
BIELBioelectronics Corp08 – Health Care (4.80)
DEWMDewmar International BMC Inc01 – Basic Materials (4.74)
FEECFar East Energy Corp06 – Energy (4.61)


Too many


TickerCompanySectorExcess analysts
PRSSCafePress Inc09 – Services 3.99
SANWS&W Seed Company05 – Consumer Non-Cyclical 4.03
KIORKiOR Inc01 – Basic Materials 4.06
PRXGPernix Group Inc02 – Capital Goods 4.08
EYNONEntergy New Orleans, Inc.12 – Utilities 4.17
PARFParadise, Inc.05 – Consumer Non-Cyclical 4.40
SUMRSummer Infant, Inc.05 – Consumer Non-Cyclical 4.52
LANDGladstone Land Corp05 – Consumer Non-Cyclical 4.57
JRCCJames River Coal Company06 – Energy 6.38
GNKGenco Shipping & Trading Limit11 – 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