Liquidity is ephemeral, and difficult to define.  The first real article at my blog was about liquidity, and the three things that liquidity can mean, notably: the ability to:

  • Enter into large or exit from commitments to risk assets cheaply (cost)
  • Borrow at tight credit spreads compared to the safest borrowers
  • Make large adjustments to their asset allocations rapidly (speed)

Most of these phenomena can be observed without complex models.  Ask yourself:

  • Is credit growing rapidly?
  • Are the exchanges moving turning over stocks more rapidly?
  • Are credit spreads tight?
  • Have credit terms and conditions deteriorated?
  • Do lenders care more about volume of lending than quality of lending?

My bias is that I think most of the academic mathematical models of liquidity risk are overly technical, and tend to obscure liquidity conditions rather than reveal what is going on.  You may disagree with that view.

But unless you disagree with that view and you like math, this book will not be worth a lot to you.  Yes, there are qualitative sections, and they are good.  For example, the beginning of chapter 2 is very good at illustrating the paradoxical nature of liquidity.  Chapters 1-3 would have made a very good qualitative monograph on liquidity — but it would be so small that you couldn’t charge $80+ for it.

Chapters 4-6 will only be useful to the mathematically inclined.  I’m dubious that they even be useful then, because much of it is calculus, which does not do well with discontinuous events such as market panics.  (You would have thought that the quants on Wall Street would have learned by now, but no…)  Even if the models did work, there are simpler ways to see the same things, as I pointed out above.

As such, I really can’t recommend the book, and at $80+ the price is a lot more expensive than the free Monograph from the CFA Institute “The New Economics of Liquidity and Financial Frictions.” [PDF]  Read that, not this, and save liquidity.


The book could have used a better editor.  Too many typos in the introductory chapters.

Summary / Who Would Benefit from this Book

If you are a math nerd, and want to pay a lot of money to buy a book that I think will at least partially mislead you on liquidity risk, then this is the book for you.  If you want to buy it, you can buy it here: Market Liquidity Risk.

Full disclosure: I received a copy from a friendly PR flack.

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.

Photo Credit: Kevin Dooley || At the Ice Museum, ALL of the assets are frozen!

Photo Credit: Kevin Dooley || At the Ice Museum, ALL of the assets are frozen!

This article is another experiment. Please bear with me.

Q: What is an asset worth?

A: An asset is worth whatever the highest bidder will pay for it at the time you offer it for sale.

Q: Come on, the value of an asset must be more enduring than that.  You look at the balance sheets of corporations, and they don’t list their assets at sales prices.

A: That’s for a different purpose.  We can’t get the prices of all assets to trade frequently.  The economic world isn’t only about trading, it is about building objects, offering services… and really, it is about making people happier through service.  Because the assets don’t trade regularly, they are entered onto the balance sheet at:

  • Cost, which is sometimes adjusted for cost and other things that are time-related, and subject to writedowns.
  • The value of the asset at its most recent sale date before the date of the statement
  • An estimated value calculated from sales of assets like it, meant to reflect the likely markets at the time of the statement — what might the price be in a deal between and un-coerced buyer and seller?

Anyway, values in financial statements are only indicative of aspects of value.  Few investors use them in detail.  Even value investors who use the detailed balance sheet values in their investment decisions make extensive adjustments to them to try to make them more realistic.  Other value investors look at where the prices of similar companies that went private to try to estimate the value of public equities.

Certainly the same thing goes on with real estate.  Realtors and appraisers come up with values of comparable properties, and make adjustments to try to estimate the value of the property in question.  Much as realtors don’t like Zillow, it does the same thing just with a huge econometric model that factors in as much information as they have regarding the likely prices of residential real estate given the prices of the sparse number of sales that they have to work from.

Financial institutions regularly have to estimate values for variety of illiquid assets in a similar way.  I’ve even been known to help with those efforts on occasion, though management teams have not always been grateful for that.

Q: What if it’s a bad day when I offer my asset for sale?  Is my asset worth less simply because of transitory conditions?

A: Do you have to sell your asset that day or not?

Q: Why does that matter?

A: If you don’t need the money immediately, you could wait.  You also don’t have to auction the asset if you think that hiring an expert come in and talk with a variety of motivated buyers could result in a better price after commissions.  There are no guarantees of a better result there though.

The same problem exists on the stock market.  If you want the the money now, issue a market order to sell the security, and you will get something close to the best price at that moment.  That said, I never use market orders.

Q: Why don’t you use market orders?

A: I don’t want to be left at the mercy of those trading rapidly in the markets.  I would rather set out a price that I think someone will transact at, and adjust it if need be.  Nothing is guaranteed — a trade might not get done.  But I won’t get caught in a “flash crash” type of scenario, or most other types of minor market manipulation.

Patience is a virtue in buying and selling, as is the option of walking away.  If you seem to be a forced seller, buyers will lower their bids if you seem to be desperate.  You may not notice this in liquid stocks, but in illiquid stocks and other illiquid assets, this is definitely a factor.


That’s all for now.  If anyone has any ideas on if, where, or how I should continue this piece, let me know in the comments, or send me an e-mail.  Thanks for reading.


Imagine that you are in the position of a high cost crude oil producer that has a lot of debt to service.  The price that you can sell your oil for is high enough that you make some cash over your variable cost.  The price is low enough that you are not recouping the cost of what you paid to buy the right to develop the oil, the development cost, and cost of equity capital employed.

In this awkward situation you continue to produce oil, because it may keep you from defaulting on your debts, even though you are not earning what is needed to justify the GAAP book value of your firm.  You’re destroying value by producing, but because of the debt, you don’t have the option of waiting because not surviving loses more money than pumping oil and seeing if you can survive.

Where there is life, there is hope.  Who knows, one of three things could “go right:”

  1. Enough competitors could fail such that global industry capacity reduces and prices rise.
  2. Demand for oil could rise because it is cheap, leading prices to rise.
  3. You could get bought out by a more solvent competitor with a longer time horizon, who sees the assets as eventually valuable.

Trouble is with #1, you could fail first.  With #2, the process is slow, and who knows how much the Saudis will pump.  With #3, the price that an acquirer could pay might not be enough for shareholders, or worse, they could buy out your competitors and not you, leaving you in a worse competitive position.

One more thought: think of the Saudis, the Venezuelans, etc… all of the national oil companies.  They’re not in all that different a spot than you are.  They need cash to fund government programs or they may face unrest.  For some like the Saudis, who assets in reserve, the odds are lower.  For the Venezuelans, who have had their economy destroyed by the politics of Chavez, the odds are a lot higher.

There will be failures among energy producers, and that could include nations.  Failures with each will be temporary as debts get worked through/compromised and new management takes over, and high cost supply gets shut down.  The question is: who will fail and who won’t.  The job of the hypothetical firm that I posited at the beginning of this article is to survive until prices rise.  What will a survivor look like?

  • Relatively high contribution margins (Price – variable cost per barrel)
  • Relatively little debt
  • Debt has long maturities and/or low coupons.

Now, I’m going to give you 40% of the answer here… I’m still working on the contribution margin question, but I can give you a useful measure regarding debt.  My summary measure is total debt as a ratio of market capitalization.  It’s crude, but I think it is a good first pass on debt stress, because the market capitalization figures carry an implicit estimate of the probability of bankruptcy.

Anyway here’s a list of all of the oil companies in the database that have debt greater than their market cap:

CompanyCountrytickerMkt capDebt / Market Cap
Energy XXI LtdBermudaEXXI17126.93
SandRidge Energy Inc.United StatesSD26416.63
Comstock Resources IncUnited StatesCRK1469.45
Linn Energy LLCUnited StatesLINE1,1728.81
EXCO Resources IncUnited StatesXCO2137.2
Cosan Limited(USA)BrazilCZZ1,0156.34
W&T Offshore, Inc.United StatesWTI2456
Halcon Resources CorpUnited StatesHK6205.89
BreitBurn Energy Partners L.P.United StatesBBEP6145.05
Magnum Hunter Resources CorpUnited StatesMHR1885.05
California Resources CorpUnited StatesCRC1,3254.92
Sanchez Energy CorpUnited StatesSN3684.74
Crestwood Equity Partners LPUnited StatesCEQP5434.64
Rex Energy CorporationUnited StatesREXX1714.51
Penn West Petroleum Ltd (USA)CanadaPWE4034.19
Atlas Resource Partners, L.P.United StatesARP3654.09
Gastar Exploration IncUnited StatesGST1083.8
Petroleo Brasileiro PetrobrasBrazilPBR35,7483.71
Stone Energy CorporationUnited StatesSGY2923.59
Bill Barrett CorporationUnited StatesBBG2523.19
EP Energy CorpUnited StatesEPE1,5523.15
Memorial Production Partners LUnited StatesMEMP5993.05
Premier Oil PLC (ADR)United KingdomPMOIY8282.95
Triangle Petroleum CorporationUnited StatesTPLM2862.88
Ultra Petroleum Corp.United StatesUPL1,2812.68
Bonanza Creek Energy IncUnited StatesBCEI3332.55
Northern Oil & Gas, Inc.United StatesNOG3592.47
Denbury Resources Inc.United StatesDNR1,4792.37
Jones Energy IncUnited StatesJONE3542.36
Chesapeake Energy CorporationUnited StatesCHK4,9172.35
Vanguard Natural Resources, LLUnited StatesVNR8332.27
LRR Energy LPUnited StatesLRE1282.23
Pengrowth Energy Corp (USA)CanadaPGH7052.21
Legacy Reserves LPUnited StatesLGCY4612.1
Aegean Marine Petroleum NetworGreeceANW3911.85
GeoPark LtdChileGPRK2021.8
Mitsui & Co Ltd (ADR)JapanMITSY23,7271.74
Oasis Petroleum Inc.United StatesOAS1,3901.69
Santos Ltd (ADR)AustraliaSSLTY3,8131.59
Whiting Petroleum CorpUnited StatesWLL3,5931.46
Midcoast Energy Partners LPUnited StatesMEP5581.45
Paramount Resources Ltd (USA)CanadaPRMRF1,0061.35
Encana Corporation (USA)CanadaECA5,9441.33
Clayton Williams Energy, Inc.United StatesCWEI5971.25
Clean Energy Fuels CorpUnited StatesCLNE4681.23
EV Energy Partners, L.P.United StatesEVEP4051.23
WPX Energy IncUnited StatesWPX1,6601.2
Baytex Energy Corp (USA)CanadaBTE1,0681.19
ONEOK, Inc.United StatesOKE7,4531.18
SunCoke Energy Partners LPUnited StatesSXCP5051.18
TransAtlantic Petroleum LtdUnited StatesTAT1261.13
Global Partners LPUnited StatesGLP1,0711.12
NGL Energy Partners LPUnited StatesNGL2,6591.12
Sprague Resources LPUnited StatesSRLP4951.11
Amyris IncUnited StatesAMRS2661.07
Sunoco LPUnited StatesSUN1,6051.06
SM Energy CoUnited StatesSM2,3601.05
Solazyme IncUnited StatesSZYM2021


This isn’t a complete analysis by any means. Personally, I would be skeptical of holding any company twice as much debt as market cap without a significant analysis.  Have at it your own way, but be careful, there will be a lot of stress on oil companies with high debt.

Photo Credit: Dr. Wendy Longo || This horizon is distant...

Photo Credit: Dr. Wendy Longo || This horizon is distant…

I ran across two interesting articles today:

Both articles are exercises in understanding the time horizon over which you invest.  If you are older, you may not have the time to recover from market shortfalls, so advice to buy dips may sound hollow when you are nearer to drawing on your assets.

Thus the idea that volatility, presumably negative, doesn’t hurt unless you sell.  Some people don’t have much choice in the matter.  They have retired, and they have a lump sum of money that they are managing for long-term income.  No more money is going in, money is only going out.  What can you do?

You have to plan before volatility strikes.  My equity only clients had 14% cash before the recent volatility hit.  Over the past week I opportunistically brought that down to 10% in names that I would like to own even if the “crisis” deepened.  That flexibility was built into my management.  (If the market recovers enough, I will rebuild the buffer.  Around 1300 on the S&P, I would put all cash to work, and move to the alternative portfolio management strategy where I sell the most marginal ideas one at a time to raise cash and reinvest into the best ideas.)

If an older investor would be hurt by a drawdown in the stock market, he needs to invest less in stocks now, even if that means having a lower income on average over the longer-term.  With a higher level of bonds in the portfolio, he could more than proportionately draw down on bonds during a crisis, which would rebalance his portfolio.  If and when the stock market recovered, for a time, he could draw on has stock positions more than proportionately then.  That also would rebalance the portfolio.

Again, plans like that need to be made in advance.  If you have no plans for defense, you will lose most wars.

One more note: often when we talk about time horizon, it sounds like we are talking about a single future point in time.  When the time for converting assets to cash is far distant, using a single point may be a decent approximation.  When the time for converting assets to cash is near, it must be viewed as a stream of payments, and whatever scenario testing, (quasi) Monte Carlo simulations, and sensitivity analyses are done must reflect that.

Many different scenarios may have the same average rate of return, but the ones with early losses and late gains are pure poison to the person trying to manage a lump sum in retirement.  The same would apply to an early spike in inflation rates followed by deflation.

The time to plan is now for all contingencies, and please realize that this is an art and not a science, so if someone comes to you with glitzy simulation analyses, ask them to run the following scenarios: run every 30-year period back as far as the data goes.  If it doesn’t include the Great Depression, it is not realistic enough.  Run them forwards, backwards, upside-down forwards, and upside-down backwards.  (For the upside-down scenarios normalize the return levels to the right side up levels.)  The idea here is to use real volatility levels in the analyses, because reality is almost always more volatile than models using normal distributions.  History is meaner, much meaner than models, and will likely be meaner in the future… we just don’t know how it will be meaner.

You will then be surprised at how much caution the models will indicate, and hopefully those who can will save more, run safer asset allocations, and plan to withdraw less over time.  Reality is a lot more stingy than the models of most financial Dr. Feelgoods out there.

One more note: and I know how to model this, but most won’t — in the Great Depression, the returns after 1931 weren’t bad.  Trouble is, few were able to take advantage of them because they had already drawn down on their investments.  The many bankruptcies meant there was a smaller market available to invest in, so the dollar-weighted returns in the Great Depression were lower than the buy-and-hold returns.  They had to be lower, because many people could not hold their investments for the eventual recovery.  Part of that was margin loans, part of it was liquidating assets to help tide over unemployment.

It would be wonky, but simulation models would have to have an uptick in need for withdrawals at the very time that markets are low.  That’s not all that much different than some had to do in the recent financial crisis.  Now, who is willing to throw *that* into financial planning models?

The simple answer is to be more conservative.  Expect less from your investments, and maybe you will get positive surprises.  Better that than being negatively surprised when older, when flexibility is limited.

Too often in debates regarding the recent financial crisis, the event was regarded as a surprise that no one could have anticipated, conveniently forgetting those who pointed out sloppy banking, lending and borrowing practices in advance of the crisis.  There is a need for a well-developed model of how a financial crisis works, so that the wrong cures are not applied to the financial system.

All that said, any correct cure will bring about a predictable response from the banks and other lending institutions.  They will argue that borrower choice is reduced, and that the flow of credit and liquidity to the financial system is also reduced.  That is not a big problem in the boom phase of the financial cycle, because those same measures help to avoid a loss of liquidity and credit availability in the bust phase of the cycle.  Too much liquidity and credit is what fuels eventual financial crises.

To get to a place where we could have a decent model of the state of overall financial credit, we would have to have models that work like this:

  1. The models would have to have both a cash flow and a balance sheet component to them — it’s not enough to look at present measures of creditworthiness only, particularly if loans do not fully amortize debts at the current interest rate.  Regulatory solvency tests should not automatically assume that borrowers will always be able to refinance.
  2. The models should try to go loan-by-loan, and forecast the ability of each loan to service debts.  Where updated financial data is available on borrowers, that should be included.
  3. The models should try to forecast the fair market prices of assets/collateral, off of estimated future lending conditions, so that at the end of the loan, estimates can be made as to whether loans would be refinanced, extended, or default.
  4. As asset prices rise, there has to be a feedback effect into lowered ability to finance new loans, unless purchasing power is increasing as much or more than asset prices.  It should be assumed that if loans are made at lower underwriting standards than a given threshold, there will be increasing levels of default.
  5. A close eye would have to look for situations where if the property were rented out, it would not earn enough to pay for normalized interest, taxes and maintenance.  When asset prices are that high, the system is out of whack, and invites future defaults.  The margin of implied rents over normalized interest, taxes and maintenance would be the key measure, and the regulators would have to have a function that attributes future losses off of the margin of that calculation.
  6. The cash flows from the loans/mortgages would have to feed through the securitization vehicles, if any, and then to the regulated financial institutions, after which, how they would fund their future liabilities would have to be estimated.
  7. The models would have to include the repo markets, because when the prices of collateral get too high, runs on the repo market can happen.  The same applies to portfolio margining agreements for derivatives, futures, and other types of wholesale lending.
  8. There should be scenarios for ordinary recessions.  There should also be some way of increasing the Ds at that time: death, disability, divorce, disaster, dis-employment, etc.  They mysteriously tend to increase in bad economic times.

What a monster.  I’ve worked with stripped-down versions of this that analyze the Commercial Mortgage Backed Securities [CMBS] market, but the demands of a model like this would be considerable, and probably impossible.  Getting the data, scrubbing it, running the cash flows, calculating the asset price functions, implied margin on borrowing, etc., would be pretty tough for angels to do, much less mere men.

Thus if I were watching over the banks, I would probably rely on analyzing:

  • what areas of credit have grown the quickest.
  • where have collateral prices risen the fastest.
  • where are underwriting standards declining.
  • what assets are being financed that do not fully amortize, including all repo markets, margin agreements, etc.

The one semi-practical thing i would strip out of this model would be for regulators to score loans using a model like point 5 suggests.  Even that would be tough, but even getting that approximately right could highlight lending institutions that are taking undue chances with underwriting.

On a slightly different note, I would be skeptical of models that don’t try to at least mimic the approach of a cash flow based model with some adjustments for market-like pricing of collateral and loans.  The degree of financing long assets with short liabilities is the key aspect of how financial crises develop.  If models don’t reflect that, they aren’t realistic, and somehow, I expect that non-realistic models of lending risk will eventually be the rule, because it helps financial institutions make loans in the short run.  After all, it is virtually impossible to fight loosening financial standards piece-by-piece, because the changes seem immaterial, and everyone favors a boom in the short-run.  So it goes.

From a friend who is a client:

Here are a couple of things I have been pondering.

  • Market capitalization is pretty fictitious. It assumes that all the shares of a company are worth the price at which the last block sold. However, if you tried to sell all of the shares of a large company (hypothetically), the price would drop to almost zero.
  • It seems to me the primary reason the stock market goes up over time is because the money supply increases. To put it another way, if the money supply did not increase the stock market could only increase in value by increasing the % percentage of the money supply spent on stocks, which is obviously limited.

My views here might be somewhat naive. Comments/criticism/feedback welcome.

Dear Friend,

Ben Graham used to talk about the stock market being a cross between a voting machine and a weighing machine.  On any given day, economic actors vote by buying and selling shares, and in the short run, the trades happen at the levels dictated by whether the buyers or sellers are more aggressive.  That is the voting machine of the market.  In the short run, values can be pretty senseless if one side or the other decides to be aggressive in their buying or selling.

What arrests the behavior of the voting machine is the weighing machine.  The price of a stock can’t get too low, or it will get taken over by a competitor, a private equity firm, a conglomerate, etc.  The price of a stock can’t get too high, or valuation-sensitive investors will sell to buy cheaper shares of firms with better prospects.  Also, corporate management will begin thinking of how they could buy up other firms, using their stock as a currency.

I’ve written more on this topic at the article The Stock Price Matters, Regardless.  Within a certain range, the market capitalization of a company is arbitrary.  Outside the range of reasonableness, financial forces take over to push the valuation to be more in line with the fundamentals of the company.

Macro Stock Market Measures

Every now and then, someone comes along and suggests a new way to value the stock market as a whole.  I’ve run across the idea that the stock market is driven by the money supply before.  The last time I saw someone propose that was in the late 1980s.  I think people were dissuaded from the idea because money supply changes in the short run did not correlate that well with the movements in stock indexes over the next 25 years.

Now, in the long run, most sufficiently broad macroeconomic variables will correlate with levels of the stock market.  Buffett likes to cite GDP as his favorite measure.  It’s hard to imagine how over the long haul the stock market wouldn’t be correlated with GDP growth.  (Why do I hear someone invoking Kalecki in the background?  Begone! 😉 )

There are other popular measures that get trotted out as well, like the Q-ratio, which compares the stock market to its replacement cost, or the Shiller Cyclically Adjusted P/E ratio [CAPE]. All of these have their merits, but none of them really capture what drives the markets perfectly.  After all:, various market players note that the market varies considerably with respect to each measure, and they try to use them to time the market.

The best measure I have run into is a little more complicated, but boils down to estimating the amount that Americans have invested in the stock market as a fraction of their total net worth.  You can find more on it here. (Credit @Jesse_Livermore)  Even that can be used to try to time the market, and it is very good, but not perfect.

But in short, the reason why any of the macro measures of the market don’t move in lockstep with the market is that market economies are dynamic.  For short periods of time, our attention can fixate on one item or group of items.  In my lifetime, I can think of periods where we focused on:

  • Monetary aggregates
  • Inflation
  • Unemployment
  • Housing prices
  • Commercial Mortgage defaults
  • Japan
  • China
  • High interest rates
  • Low interest rates
  • Bank solvency

Profit margins rise and fall.  Credit spreads rise and fall.  Interest rates rise and fall.  Sectors of the economy go in and out of favor.  The boom/bust cycle never gets repealed, and economists that think they can do so eventually get embarrassed.

That’s what keeps this game interesting on a macro level.  You can’t tell what the true limits are for market valuation.  We can have guesses, but they are subject to considerable error.  It is best to be conservative in our judgments here, in order to maintain a margin of safety, realizing that we will look a little foolish when the market runs too hot, and when we seem to be catching a falling knife in the bear phase of the market.  Take that as my best advice on what is otherwise a cloudy topic, and thanks for asking — you made me think.

One of the constants in investing is that average investors show up late to the party or to the crisis.  Unlike many gatherings where it may be cool to be fashionably late, in investing it tends to mean you earn less and lose more, which is definitely not cool.

One reason why this happens is that information gets distributed in lumps.  We don’t notice things in real time, partly because we’re not paying attention to the small changes that are happening.  But after enough time passes, a few people notice a trend.  After a while longer, still more people notice the trend, and it might get mentioned in some special purpose publications, blogs, etc.  More time elapses and it becomes a topic of conversation, and articles make it into the broad financial press.  The final phase is when general interest magazines put it onto the cover, and get rich quick articles and books point at how great fortunes have been made, and you can do it too!

That slow dissemination and gathering of information is paralleled by a similar flow of money, and just as the audience gets wider, the flow of money gets bigger.  As the flow of money in or out gets bigger, prices tend to overshoot fair value, leaving those who arrived last with subpar returns.

There is another aspect to this, and that stems from the way that people commonly evaluate managers.  We use past returns as a prologue to what is assumed to be still greater returns in the future.  This not only applies to retail investors but also many institutional investors.  Somme institutional investors will balk at this conclusion, but my experience in talking with institutional investors has been that though they look at many of the right forward looking indicators of manager quality, almost none of them will hire a manager that has the right people, process, etc., and has below average returns relative to peers or indexes.  (This also happens with hedge funds… there is nothing special in fund analysis there.)

For the retail crowd it is worse, because most investors look at past returns when evaluating managers.  Much as Morningstar is trying to do the right thing, and have forward looking analyst ratings (gold, silver, bronze, neutral and negative), yet much of the investing public will not touch a fund unless it has four or five stars from Morningstar, which is a backward looking rating.  This not only applies to individuals, but also committees that choose funds for defined contribution plans.  If they don’t choose the funds with four or five stars, they get complaints, or participants don’t use the funds.

Another Exercise in Dollar-Weighted Returns

One of the ways this investing shortfall gets expressed is looking at the difference between time-weighted (buy-and-hold) and dollar-weighted (weighted geometric average/IRR) returns.  The first reveals what an investor who bought and held from the beginning earned, versus what the average dollar invested earned.  Since money tends to come after good returns have been achieved, and money tends to leave after bad returns have been realized, the time-weighted returns are typically higher then the dollar-weighted returns.  Generally, the more volatile the performance of the investment vehicle the larger the difference between time- and dollar-weighted returns gets.  The greed and fear cycle is bigger when there is more volatility, and people buy and sell at the wrong times to a greater degree.

(An aside: much as some pooh-pooh buy-and-hold investing, it generally beats those who trade.  There may be intelligent ways to trade, but they are always a minority among market actors.)

HSGFX Dollar Weighted Returns

HSGFX Dollar and Time Weighted Returns

That brings me to tonight’s fund for analysis: Hussman Strategic Growth [HSGFX]. John Hussman, a very bright guy, has been trying to do something very difficult — time the markets.  The results started out promising, attracting assets in the process, and then didn’t do so well, and assets have slowly left.  For my calculation this evening, I run the calculation on his fund with the longest track record from inception to 30 June 2014.  The fund’s fiscal years end on June 30th, and so I assume cash flows occur at mid-year as a simplifying assumption.  At the end of the scenario, 30 June 2014, I assume that all of the funds remaining get paid out.

To run this calculation, I do what I have always done, gone to the SEC EDGAR website and look at the annual reports, particularly the section called “Statements of Changes in Net Assets.”  The cash flow for each fiscal year is equal to the net increase in net assets from capital share transactions plus the net decrease in net assets from distributions to shareholders.  Once I have the amount of money moving in or out of the fund in each fiscal year, I can then run an internal rate of return calculation to get the dollar-weighted rate of return.

In my table, the cash flows into/(out of) the fund are in millions of dollars, and the column titled Accumulated PV is the accumulated present value calculated at an annualized rate of -2.56% per year, which is the dollar-weighted rate of return.  The zero figure at the top shows that a discount rate -2.56% makes the cash inflows and outflows net to zero.

From the beginning of the Annual Report for the fiscal year ended in June 2014, they helpfully provide the buy-and-hold return since inception, which was +3.68%.  That gives a difference of 6.24% of how much average investors earned less than the buy-and-hold investors.  This is not meant to be a criticism of Hussman’s performance or methods, but simply a demonstration that a lot of people invested money after the fund’s good years, and then removed money after years of underperformance.  They timed their investment in a market-timing fund poorly.

Now, Hussman’s fund may do better when the boom/bust cycle turns if his system makes the right move somewhere near the bottom of the cycle.  That didn’t happen in 2009, and thus the present state of affairs.  I am reluctant to criticize, though, because I tried running a strategy like this for some of my own clients and did not do well at it.  But when I realized that I did not have the personal ability/willingness to buy when valuations were high even though the model said to do so because of momentum, rather than compound an error, I shut down the product, and refunded some fees.

One thing I can say with reasonable confidence, though: the low returns of the past by themselves are not a reason to not invest in Mr. Hussman’s funds.  Past returns by themselves tell you almost nothing about future returns.  The hard questions with a fund like this are: when will the cycle turn from bullish to bearish?  (So that you can decide how long you are willing to sit on the sidelines), and when the cycle turns from bearish to bullish, will Mr. Hussman make the right decision then?

Those questions are impossible to answer with any precision, but at least those are the right questions to ask.  What, you’d rather have the answer to a simple question like how did it return in the past, that has no bearing on how the fund will do in the future?  Sadly, that is the answer that propels more investment decisions than any other, and it is what leads to bad overall investment returns on average.

PS — In future articles in this irregular series, I will apply this to the Financial Sector Spider [XLF], and perhaps some fund of Kenneth Heebner’s.  Till then.

My last post has many implications. I want to make them clear in this post.

  1. When you analyze a manager, look at the repeatability of his processes.  It’s possible that you could get “the Big Short” right once, and never have another good investment idea in your life.  Same for investors who are the clever ones who picked the most recent top or bottom… they are probably one-trick ponies.
  2. When a manager does well and begins to pick up a lot of new client assets, watch for the period where the growth slows to almost zero.  It is quite possible that some of the great performance during the high growth period stemmed from asset prices rising due to the purchases of the manager himself.  It might be a good time to exit, or, for shorts to consider the assets with the highest percentage of market cap owned as targets for shorting.
  3. Often when countries open up to foreign investment, valuations are relatively low.  The initial flood of money in often pushes up valuations, leads to momentum buyers, and a still greater flow of money.  Eventually an adjustment comes, and shakes out the undisciplined investors.  But, when you look at the return series analyze potential future investment, ignore the early years — they aren’t representative of the future.
  4. Before an academic paper showing a way to invest that would been clever to use in the past gets published, the excess returns are typically described as coming from valuation, momentum, manager skill, etc.  After the paper is published, money starts getting applied to the idea, and the strategy will do well initially.  Again, too much money can get applied to a limited factor (or other) anomaly, because no one knows how far it can get pushed before the market rebels.  Be careful when you apply the research — if you are late, you could get to hold the bag of overvalued companies.  Aside for that, don’t assume that performance from the academic paper’s era or the 2-3 years after that will persist.  Those are almost always the best years for a factor (or other) anomaly strategy.
  5. During a credit boom, almost every new type of fixed income security, dodgy or not, will look like genius by the early purchasers.  During a credit bust, it is rare for a new security type to fare well.
  6. Anytime you take a large position in an obscure security, it must jump through extra hoops to assure a margin of safety.  Don’t assume that merely because you are off the beaten path that you are a clever contrarian, smarter than most.
  7. Always think about the carrying capacity of a strategy when you look at an academic paper.  It might be clever, but it might not be able to handle a lot of money.  Examples would include trying to do exactly what Ben Graham did in the early days today, and things like Piotroski’s methods, because typically only a few small and obscure stocks survive the screen.
  8. Also look at how an academic paper models trading and liquidity, if they give it any real thought at all.  Many papers embed the idea that liquidity is free, and large trades can happen where prices closed previously.
  9. Hedge funds and other manager databases should reflect that some managers have closed their funds, and put them in a separate category, because new money can’t be applied to those funds.  I.e., there should be “new money allowed” indexes.
  10. Max Heine, who started the Mutual Series funds (now part of Franklin), was a genius when he thought of the strategy 20% distressed investing, 20% arbitrage/event-driven investing and 60% value investing.  It produced great returns 9 years out of 10.  but once distressed investing and event-driven because heavily done, the idea lost its punch.  Michael Price was clever enough to sell the firm to Franklin before that was realized, and thus capitalizing the past track record that would not do as well in the future.
  11. The same applies to a lot of clever managers.  They have a very good sense of when their edge is getting dulled by too much competition, and where the future will not be as good as the past.  If they have the opportunity to sell, they will disproportionately do so then.
  12. Corporate management teams are like rock bands.  Most of them never have a hit song.  (For managements, a period where a strategy improves profitability far more than most would have expected.)  The next-most are one-hit wonders.  Few have multiple hits, and rare are those that create a culture of hits.  Applying this to management teams — the problem is if they get multiple bright ideas, or a culture of success, it is often too late to invest, because the valuation multiple adjusts to reflect it.  Thus, advantages accrue to those who can spot clever managements before the rest of the market.  More often this happens in dull industries, because no one would think to look there.
  13. It probably doesn’t make sense to run from hot investment idea to hot investment idea as a result of all of this.  You will end up getting there once the period of genius is over, and valuations have adjusted.  It might be better to buy the burned out stuff and see if a positive surprise might come.  (Watch margin of safety…)
  14. Macroeconomics and the effect that it has on investment returns is overanalyzed, though many get the effects wrong anyway.  Also, when central bankers and politicians take cues from the prices of risky assets, the feedback loop confuses matters considerably.  if you must pay attention to macro in investing, always ask, “Is it priced in or not?  How much of it is priced in?”
  15. Most asset allocation work that relies on past returns is easy to do and bogus.  Good asset allocation is forward-looking and ignores past returns.
  16. Finally, remember that some ideas seem right by accident — they aren’t actually right.  Many academic papers don’t get published.  Many different methods of investing get tried.  Many managements try new business ideas.  Those that succeed get air time, whether it was due to intelligence or luck.  Use your business sense to analyze which it might be, or, if it is a combination.

There’s more that could be said here.  Just be cautious with new investment strategies, whatever form they may take.  Make sure that you maintain a margin of safety; you will likely need it.

Investing ideas come in many forms:

  • Factors like Valuation, Sentiment, Momentum, Size, Neglect…
  • New technologies
  • New financing methods and security types
  • Changes in government policies will have effects, cultural change, or other top-down macro ideas
  • New countries to invest in
  • Events where value might be discovered, like recapitalizations, mergers, acquisitions, spinoffs, etc.
  • New asset classes or subclasses
  • Durable competitive advantage of marketing, technology, cultural, or other corporate practices

Now, before an idea is discovered, the economics behind the idea still exist, but the returns happen in a way that no one yet perceives.  When an idea is discovered, the discovery might be made public early, or the discoverer might keep it to himself until it slowly leaks out.

For an example, think of Ben Graham in the early days.  He taught openly at Columbia, but few followed his ideas within the investing public because everyone was still shell-shocked from the trauma of the Great Depression.  As a result, there was a large amount of companies trading for less than the value of their current assets minus their total liabilities.

As Graham gained disciples, both known and unknown, they chipped away at the companies that were so priced, until by the late ’60s there were few opportunities of that sort left.  Graham had long since retired; Buffett winds up his partnerships, and manages the textile firm he took over as a means of creating a nascent conglomerate.

The returns generated during its era were phenomenal, but for the most part, they were never to be repeated.  Toward the end of the era, many of the practitioners made their own mistakes as they violated “margin of safety” principles.  It was a hard way of learning that the vein of financial ore they were mining was finite, and trying to expand to mine a type of “fool’s gold” was not a winning idea.

Value investing principles, rather than dying there, broadened out to consider other ways that securities could be undervalued, and the analysis process began again.

My main point this evening is this: when a valid new investing idea is discovered, a lot of returns are generated in the initial phase. For the most part they will never be repeated because there will likely never be another time when that investment idea is totally forgotten.

Now think of the technologies that led to the dot-com bubble.  The idealism, and the “follow the leader” price momentum that it created lasted until enough cash was sucked into unproductive enterprises, where the value was destroyed.  The current economic value of investment ideas can overshoot or undershoot the fundamental value of the idea, seen in hindsight.

My second point is that often the price performance of an investment idea overshoots.  Then the cash flows of the assets can’t justify the prices, and the prices fall dramatically, sometimes undershooting.  It might happen because of expected demand that does not occur, or too much short-term leverage applied to long-term assets.

Later, when the returns for the investment idea are calculated, how do you characterize the value of the investment idea?  A new investment factor is discovered:

  1. it earns great returns on a small amount of assets applied to it.
  2. More assets get applied, and more people use the factor.
  3. The factor develops its own price momentum, but few think about it that way
  4. The factor exceeds the “carrying capacity” that it should have in the market, overshoots, and burns out or crashes.
  5. It may be downplayed, but it lives on to some degree as an aspect of investing.

On a time-weighted rate of return basis, the factor will show that it had great performance, but a lot of the excess returns will be in the early era where very little money was applied to the factor.  By the time a lot of money was applied to the factor, the future excess returns were either small or even negative.  On a dollar-weighted basis, the verdict on the factor might not be so hot.

So, how useful is the time-weighted rate of return series for the factor/idea in question for making judgments about the future?  Not very useful.  Dollar weighted?  Better, but still of limited use, because the discovery era will likely never be repeated.

What should we do then to make decisions about any factor/idea for purposes of future decisions?  We have to look at the degree to which the factor or idea is presently neglected, and estimate future potential returns if the neglect is eliminated.  That’s not easy to do, but it will give us a better sense of future potential than looking at historical statistics that bear the marks of an unusual period that is little like the present.

It leaves us with a mess, and few firm statistics to work from, but it is better to be approximately right and somewhat uncertain, than to be precisely wrong with tidy statistical anomalies bearing the overglorified title “facts.”

That’s all for now.  As always, be careful with your statistics, and use sound business judgment to analyze their validity in the present situation.

Recently I ran across an academic journal article where they posited one dozen or so risk premiums that were durable, could be taken advantage of in the markets.  In the past, if you had done so, you could have earned incredible returns.

What were some of the risk premiums?  I don’t have the article in front of me but I’ll toss out a few.

  • Many were Credit-oriented.  Lend and make money.
  • Some were volatility-oriented.  Sell options on high volatility assets and make money.
  • Some were currency-oriented.  Buy government bonds where they yield more, and short those that yield less.
  • Some had you act like a bank.  Borrow short, lend long.
  • Some were like value investors.  Buy cheap assets and hold.
  • Some were akin to arbitrage.  Take illiquidity risk or deal/credit risk.
  • Others were akin to momentum investing.  Ride the fastest pony you can find.

After I glanced through the paper, I said a few things to myself:

  • Someone will start a hedge fund off this.
  • Many of these are correlated; with enough leverage behind it, the hedge fund could leave a very large hole when it blows up.
  • Yes, who wouldn’t want to be a bank without regulations?
  • What an exercise in data-mining and overfitting.  The data only existed for a short time, and most of these are well-recognized now, but few do all of them, and no one does them all well.
  • Hubris, and not sufficiently skeptical of the limits of quantitative finance.

Risk premiums aren’t free money — eggs from a chicken, a cow to be milked, etc.  (Even those are not truly free; animals have to be fed and cared for.)  They exist because there comes a point in each risk cycle when bad investments are revealed to not be “money good,” and even good investments are revealed to be overpriced.

Risk premiums exist to compensate good investors for bearing risk on “money good” investments through the risk cycle, and occasionally taking a loss on an investment that proves to not be “money good.”

(Note: “money good” is a bond market term for a bond that pays all of its interest and principal.  Usage: “Is it ‘money good?'”  “Yes, it is ‘money good.'”)

In general, it is best to take advantage of wide risk premiums during times of panic, if you have the free cash or a strong balance sheet behind you.  There are a few problems though:

  • Typically, few have free cash at that time, because people make bad investment commitments near the end of booms.
  • Many come late to the party, when risk premiums dwindle, because the past performance looks so good, and they would like some “free money.”

These are the same problems experienced by almost all institutional investors in one form or another.  What bank wouldn’t want to sell off their highest risk loan book prior to the end of the credit cycle?  What insurance company wouldn’t want to sell off its junk bonds at that time as well?  And what lemmings will buy then, and run over the cliff?

This is just a more sophisticated form of market timing.  Also, like many quantitative studies, I’m not sure it takes into account the market impact of trying to move into and out of the risk premiums, which could be significant, and change the nature of the markets.

One more note: I have seen a number of investment books take these approaches — the track records look phenomenal, but implementation will be more difficult than the books make it out to be.  Just be wary, as an intelligent businessman should, ask what could go wrong, and how risk could be mitigated, if at all.