============================================================

Most days, I don’t trade.  I study.  I model.  I muse.  I plan.

There are some clever traders out there.  I am not one of them.  What little trading I do is done efficiently and effectively to get the best prices for assets that I want to buy and sell, but that’s not where most of the money is made in investing.

I can be like a chef who goes out to the market in the morning and buys the best ingredients available that day at great prices, except that my period of analysis is years, not a day.  The point is that I consider the deals that the market is offering, and choose attractive ones that will benefit my clients and me for years to come.  (I am still the largest investor that I manage money for — I eat the exact same meal that I serve to clients.)

What trading I do divides into two categories, which are designed for two different time horizons.  The first time horizon is long — 3-10 years in length.  Can I find companies with good or better business prospects trading at prices more attractive than the businesses that I currently own?

This is mostly a patient thing, unless I conclude that I got something materially wrong, in which case I try to be quick to sell.  Patience is needed, because investing is like farming.  It doesn’t grow overnight.  It will take time for value to be built, and time for people to recognize that the company is better than they thought it was.  It won’t be a linear process, either, unless something unusually good happens.  There are setbacks with almost every winning investment.  Keep your eyes on the main drivers of growth in value, and whether management is using excess cash to the best ends, which will vary by company.

At least half of my winners spent time as an unrealized capital loss at some point.  My timing is sometimes nonideal, but ideal timing is not required for great results if the time horizon is years.  So I watch and monitor, and occasionally trade away the position when I find something with materially better prospects.

As an aside, not all RIA clients would like this, because it looks like I’m not doing that much.  I sometimes wonder how much better money management would be if clients were happy with portfolios that don’t change much and don’t have many of the current hottest and most recognizable companies in them.  Portfolios filled with unknown companies in boring but profitable industries… difficult to talk about at parties, but often more profitable.

What I have mentioned above is 85% of what I do.  The shorter-run movements of the market provide the other 15% as ideas and companies go in and out of favor in the short run.  I mentioned that my timing is often not the best.  This gives me an opportunity to do a little better.

I’ve mentioned that I use a 20% band around my position weights for the companies that I own.  As prices fall and hit the bottom of the band, I buy enough to come back up to the target weight.  Vice-versa for when the price hits the upper band.

20% is a significant move — it’s enough to justify the trading costs.  If the company is still a good one, the fall in price gives me the opportunity to lower my average cost modestly.  Note that this is a modest change — I’m not trying to be a hero or a home-run hitter.  I learned better when I was younger that making timing decisions on that level is too undisciplined.  It is far better to edge in and edge out around a core position — with a good company, a lower price means lower risk, and a higher price means higher risk, so this method is always taking and shedding risk at appropriate levels.

Edge in, edge out — trades like this happen a few times a month — more frequently when the market is lively, less often when it is sleepy.  Hey, don’t force things.  This is gradual reallocation of money from less to more attractive homes for capital.  The time horizon here is 3-12 months, and offers the ability to make a little more off of core positions.

Over 5 years, companies that I own might have a grand total of 5-10 trades from edging in and out.  It will always be a mix of both buys and sells — few companies don’t have moves of 20%+ down amid growth.  (As some will note, if markets are efficient, why is there such a large gap between 52-week highs and lows for individual stocks?  Really, markets aren’t efficient — they are just very hard to beat.)

Now, others will come up with different ways of managing multiple time horizons in investing, but this method offers a decent balance between the short- and long-terms, and does so in a businesslike, disciplined way.  And so I edge in and edge out.

This balances the short- and long-terms, and does so in a businesslike, disciplined way. Click To Tweet

Data Source: Media General || Note: Do not cite or republish this graph without publishing the limitations paragraph below.

Data Source: Media General || Note: Do not cite or republish this graph without publishing the limitations paragraph below.

=====================

Before I start this evening, I want to state again that I welcome comments at this blog. It may not seem so from the last few months, but I have shaken the bugs out of the software that protects my blog, which was hypersensitive on comments. The only thing I ask of commenters is that you be polite and clean in your speech. Disagree with me as you like — hey, even I have doubts about my more extreme positions. 😉

Limitations

The graph above and the text explaining it could very easily be misused, so I am giving a detailed explanation of how I calculated the figures so that people looking at them can more easily critique them and perhaps show me where they are wrong.  Please use the above figures with care.

I summed up the net income data for 2706 firms in the Media General database used in the AAII Stock Investor Pro screening software.  Those firms had:

  • Seven years of historical earnings data (2009-2015)
  • Earnings estimates that go out to 2018, and
  • An estimate of the diluted common shares of each

In short, it is all of the firms trading on US exchanges (that Media General covers) that have seven years of earnings history, and significant analyst coverage extending out for two years.  Please note that not all fiscal years are equivalent, and that the historic data is on fiscal years, aside from 2016YTD, which is a trailing twelve months figure.  That means 2016YTD is largely from the first half of calendar 2016 and the last half of calendar 2015.

Note that companies that went out of existence between 2009 and today are not reflected in these figures.  They represent only the companies that exist as publicly traded firms today.  Also note that foreign firms trading on US exchanges are in these figures.

The projected Non-GAAP earnings are the product of average sell-side earnings estimates and the most recent estimate of fully diluted common shares.  2016, 2017 & 2018 are the current, next year, and two years ahead estimates of adjusted earnings, which are Non-GAAP.

Remember that sell-side estimates are designed (in theory) to eliminate transitory factors and provide an estimate of run rate earnings for the future.  Whether that is true in practice is another matter, as we may see here.

There is one more piece of data that you need before you can interpret the above graph: because of foreign firms that are included, the total market capitalization underlying the graph is $28.8 Trillion.

Analysis

After my recent piece Practically Understanding Non-GAAP Earnings Adjustments, I felt there was something more to say, because regularly I would see earnings estimates that were higher than historic earnings by a wide margin, which would make me say “How does it get from here to there?”  The answer is simple.  It doesn’t.

Why?  We’re comparing apples and oranges.  GAAP earnings deduct many expenses out that were incurred in prior periods, but deferred.  GAAP earnings also have unusual and extraordinary charges that are expected not to occur.  Non-GAAP earnings exclude those (among other things, sometimes excluding interest and taxes).  As such, they are considerably higher than GAAP earnings.

Take a look at this table of price-earnings ratios.

Year

2009

2010201120122013201420152016YTD20162017

2018

P/E

36.61

24.8422.1323.2621.0121.7029.6930.6818.6816.02

14.06

Note: the same warning on the graph applies to this table.

Note that the current market capitalization is being applied against historic net income 2009-2016YTD.  2016-2018 are on projected non-GAAP net income estimated by the sell-side.  Obviously, in 2009 the market capitalization was much lower, and so the P/E then would have been higher.  Survivorship bias will have some impact here, but I’m not sure which way it would go.

See how much lower the P/Es are for the sell-side estimates (these would be bottom-up, not top-down).  Figures like this get cited by pundits who say the market isn’t that expensive.

Also, note how GAAP earnings have shrunk since 2014, and haven’t grown much since 2009.  I know only the media compares actual to prior, which is an anachronism, but maybe we need to do that more.

Summary

That leaves us with a few sticky questions:

  • Which is a better measure for growth in value?  GAAP or non-GAAP earnings?  (I think the answer varies by industry, and how long of a period you are considering.)
  • Should we allow non-GAAP earnings to be published? (Yes, after all management is going to explain the non-GAAP adjustments orally as they explain why the quarter was good or bad.)
  • Does this mean that the market is overvalued?  (Not necessarily.  Rational businessmen are still buying some firms out, which partially validates current levels. Also, free cash flow is not affected by accounting rules, so questions of overvaluation should not rely on accounting methods.  If it is overvalued on one, it should be overvalued on all, etc.)
  • Should we create a fifth main statement for GAAP accounting, that formalizes non-GAAP and gives it real rules? (Probably, but like most of GAAP, there will be some flexibility and industry-specific rules.)

As for me, this will give me a little help in making adjustments to earnings estimates as I try to think through valuation issues, and give me some rough idea as to whether the hockey stick that the sell side illustrates is worth considering or not, or to what degree.

Again, comments are welcome.  Please note that my findings are tentative here.

I’ve said this before, but I like it when research destroys a preconceived notion of mine.  Today’s post stems from an exchange that I had with Jackdamn (what a name) on Stocktwits, talking about a chart created by dshort.

S&P 500 Percent Off High Since March 9, 2009. Chart by Doug Short. $SPX $SPY $DIA

— Jack Damn (@jackdamn) Sep. 3 at 09:39 AM

I responded:

@jackdamn over a 7.5 year period, how frequently do you get 5-10%. 10-15%, 15-20%, 20%+ drawdowns? This graph looks tame to me. $$

— David Merkel (@AlephBlog) Sep. 5 at 02:52 PM

To which he responded: That’s a great question.  And it is a great question, but I’m not going to answer it directly here… because I think I am answering a better question.

Let me take you through my thought process, because I went through four different ways of trying to answer the question before settling on the better question, and getting the answer.

How do you summarize an area of a price graph in order to make comparisons of different periods?  How do you determine when the market has been near highs for a long time, or far away for a long time?  How does the intensity/distance below the high matter?  If you are looking at troughs, where does one begin and another end?

I started by trying to identify the troughs individually, and the difficulty was trying to establish that in a mechanical way that did not require interpretation.  I stumbled around playing with minimum periods between troughs, recovery levels before a new trough could start, moving averages to establish when a new trough was genuinely significant.  Sigh.

I tried a lot of different things, and I could create rules that mostly made the troughs look decent, but I could never get it to be fully mechanical or lack arbitrariness.  Why this trough and not that?  The same criticisms can be applied to dshort’s graph as well.

I finally pulled out of my mental gymnastics when I concluded: couldn’t I just take the area under the maximum line in percentage terms and use that as a measure, say over a 200-day period?  200 days is arbitrary, and so is the measure, but that is less than most of the measures that I considered, and at least this one corresponds to a relatively simple calculation.

So if you look at the red line in my graph above, you will note that it has dipped below 2.0 five times in the last 66 years, in 1954, 1959, 1964, 1995 and 2014.  These observations followed periods where the markets moved to new highs rather smartly and without a lot of downside volatility.  Then there were 3 times that the measure peaked higher than 64, in 1975, 2003 and 2009.  These times followed incredible market falls, and were great times to be putting money into the market.

Below you can see  a table of values for how often the measure is below a given threshold.  It’s only above 64 about 5% of the time, and below 2 about 3.5% of the time.  My main thought is this measure is this: high values of the measure probably are a “buy signal.”  Low values of the measure aren’t necessarily a “sell signal.”

That signals are asymmetric should not be surprising.  The largest factor in most long-term market moves, the credit cycle, is also asymmetric.  It’s like my continuing series, Goes Down Double Speed.  Bull markets have shallower moves and longer duration, the same way that the bull phase of the credit cycle goes.  Extend credit, extend credit, extend credit… loosen standards, loosen standards, loosen standards… tighten spreads, tighten spreads, tighten spreads, etc.  Then in the bear phase it is DENY CREDIT!! TIGHTEN STANDARDS!! SHEPHERD LIQUIDITY!! SURVIVE!!  Short and sharp.  Painful.  Prices are lower, and yields higher at the end.

To close this off, where is this indicator now?  It’s around 8, which is near the 40th percentile… kind of a blah figure, not saying much of anything… which is good in its own way.  The market meanders and hits a few new highs, sags a little, comes back, hits a few new highs, etc.  Not many people believe in it, but we are inches off the highs.  Odds are we go higher from here, but not aggressively higher.

One final note: we are in the fourth and final phase of the credit cycle now, so don’t get too aggressive.  Debt is getting higher inside nonfinancial corporations.  Be wary, and do your fundamental due diligence on balance sheets.

PercentileDFHI200MS
1%1.33
5%2.42
10%3.21
20%4.50
30%5.73
40%8.18
50%11.67
60%17.42
70%27.47
80%36.52
90%49.83
95%63.10
99%83.08

-==-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

Before I write this evening, I would like to point out what is going on with Horsehead Holdings [ZINCQ].  There was an article in the New York Times on it recently.  It’s an interesting situation where an equity committee exists in a bankruptcy, largely because the management team looks like it is not trying to maximize the value of the bankruptcy estate, but is perhaps instead trying to sell the company off to creditors cheaply in an effort to receive a benefit later from the new owners.  Worth a look, because if the equity committee wins, it will be unusual, and if the debtors win, it very well may take value that legitimately belonged to the equity.

That said, I don’t have a strong opinion because I don’t have enough data.  But I will be watching.

=-=-=-=-=-=-==-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

I received a letter from a reader yesterday on a related topic from my most recent article.  Here it is:

Hi David,

First of all, it’s nice to find you (and Ed Yardeni and Mohamed El-Erian) working when most analysts seem to be at the beach. That said, a question:

In early ’09, as you will recall, the big banks were begging for relief from mark-to-market accounting for their holdings of mortgage-backed securities, on the grounds that these securities weren’t trading at all.

“Ridiculous!” said Jeremy Grantham. “Put 2 percent of your holding out to auction and you will learn its market value quick enough.”

At the time, I thought Grantham had a fair point. Now I’m not so sure.

What was your view on that issue? John Hussman has said repeatedly that it was the FASB’s relaxation of the mark-to-market rules that set off the dramatic resurgence in stock prices that we have seen (and which he deplores).

Was the FASB’s change of policy warranted, under the circumstances?

And should the mark-to-market rule now be restored?

Here was my reply:

Hi,

I wrote a lot about this at the time.  I remain in favor of mark-to-market accounting.  The companies that got into trouble from the effects of mark-to-market accounting had engaged in sloppy risk management practices, and got caught with their pants down.

The difficulty that most of the complaining companies had was a mix of liquid liabilities requiring prompt payment, and relatively illiquid assets that would be difficult to sell.  It was the classic asset-liability mismatch — long illiquid assets financed by short liquid liabilities.  Looks like genius during the bull phase.  Toxic during the bear phase.

On Grantham’s comments: my comments Saturday night are pertinent here for two reasons — anyone selling illiquid CDO tranches, subordinated mortgage bonds, etc., immediately prior to the crisis would find two things: 1) the bids were non-existent or really poor, and 2) if the trade did take place, it would be at levels that reset the pricing grid for that area of the market a LOT lower, leaving the remaining securities looking worse, and a diminution of GAAP equity.

(As an aside, the diminution of GAAP equity might affect the ability to do secondary IPOs of stock at attractive prices, but in itself it did not affect solvency of most financial firms, because statutory accounting allowed for investments to held at amortized cost.  As such the firms could be economically insolvent, but not regulatorily insolvent unless they ran out of cash, or their short-term lending lines of credit got pulled.)

Anyway, this piece is a summary of my thoughts, and provides links to other things I wrote during that era: Fair Value Accounting — It Is What It Is

The regulators were pretty lenient with most of the companies involved — the creditors weren’t.  They enforced margin agreements, and pulled discretionary credit lines.

I’m not of Hussman’s opinion that relaxation of the mark-to-market rules had ANY effect on stock prices.  In general, GAAP accounting rules don’t affect stock prices, because they don’t affect free cash flow, unless the GAAP rules are embedded in credit covenants.  Statutory accounting does affect free cash flow, and can affect the prices of stocks.

Those are my opinions, for what they are worth.

Sincerely,

David

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-

This post may be a little more complex than most. It will also be more theoretical. For those disinclined to wade through the whole thing, skip to the bottom where the conclusions are (assuming that I have any). 😉

Asset Prices are (Mostly) Validated by a Thin Stream of Transactions

One thing that I have been musing about recently is how few transactions exist to validate the pricing of various markets.  I’ll start with two obvious ones, and then I will broaden out to some more markets that are less obvious.  (Hint: markets that have a high level of transactions relative to the underlying asset value have a lot of speculative “noise traders.”)

Let me start with the market that I know best as far as this topic goes: bonds.  Aside from some government and quasi-governmental bonds, very few bonds trade each day — less than a few percent.  It’s very difficult to use the small volume of trades to price the whole market, but it can be done.

When I was a bond manager for a semi-major insurance company, I was the only one of the top managers that was a mathematician, and familiar with all of the structures underlying the bonds.  I could create my own models of bonds if needed, and I often did for interest rate risk analyses (which was still a responsibility amid bond management).  Combined with my knowledge of insurance accounting, it made me ideal to do a certain monthly task: making sure all of the bonds got priced.

The first part of that isn’t hard.  The pricing service typically covers 90-98% the bonds  in the portfolio.  What I would receive on the first day of the month was a list of all the bonds the pricing service could not calculate a price for.  I would take that list and compare it to last month’s list of the same bonds and add to it any new bonds we had bought that month, and who the lead dealer was.  I would then ask the dealers for their prices on the bonds (which were typically illiquid).  I would compare those prices to the prices of the prior month, and maybe ask a question or two about the prices that were out of line.  That would usually elicit a comment from my coverage akin to, “The analyst thinks spreads have widened out for that credit because spreads in that industry have widened out, and a less liquid bond would widen out more.  The why the price fell more (or rose less).

After that was done, that left me with a small number of utterly illiquid bonds that we had sourced totally privately, or where the dealers who had originated the bonds had ceased to exist.  All of those deals lacked options to accelerate or decelerate payment, so it was a question of modeling the cash flows and applying an appropriate yield spread over the Treasury or Swap yield curve.  [Note: the swap curve gives the yield rates at which AA-rated banks are willing to trade fixed rate exposures in their own credit for floating rate exposures in their own credit, and vice-versa.]

But what is appropriate and how did the three methods of getting prices differ?  The second question is easier.  They didn’t differ much at all.  The dealers and I were likely doing the same things — just with different sets of bonds.  The pricing service, on the other hand, was much more complex, and the other two methods relied on its results.

It was was called “grid” or “matrix” pricing, though it was much more complex than a grid or a matrix.  The pricing service models would look at all of the most recent trades that had happened in the bond market, and use all of the prices to estimate yields that were adjusted for the options inherent in the bonds that could accelerate or decelerate payments.  From that, they would piece together yield curves that varied by industry and collateral type, credit rating (agency or implied by a model that involved stock prices and equity option prices), individual creditors, etc.  Trades on different days were adjusted for market conditions to make the pricing as similar as possible to the end of the month.  After that the yield and yield spread curves generated would be applied to the structures of individual bonds with a adjustments for whether the bonds were:

  • premium or discount
  • large deals that were widely traded or small illiquid deals
  • callable or putable
  • senior or subordinate or structurally subordinate (a bond of a subsidiary not guaranteed by the parent company)
  • secured or unsecured
  • bullet or laddered maturities (sinking funds, etc.)
  • different currencies
  • and more

And there you would have a set of self-consistent prices that would price most of the bond universe.  That’s not where transactions would necessarily take place… particularly with illiquid securities, what would matter most is who was more incented to make the trade happen — the buyer or the seller.

Implicitly, I learned a lot of this not just from modeling for risk purposes, but from trading a lot of bonds day by day.  How do you make the right adjustments when you compare two bonds to make a swap, and, how much of a margin do you put in as a provision to make sure you are getting a good deal without the other side of the trade walking away?  It’s tough, but if you know how all of the tradeoffs work, you can come to a reasonable answer.

One more note before the summary.  The less common it is for a bond or group of related bonds to trade, the more effect a trade has on the overall process.  It becomes a critical datapoint that can redefine where bonds like it trade.  Illiquidity begets volatile prices changes in the grid/matrix as a result.  On the bright side, illiquidity is usually associated with small sizes, so it doesn’t affect most of the market.  There is an exception to this rule: trades done during a panic or the recovery from a panic tend to be sparse as well.  The trades that happen then can temporarily change a wider area of pricing.  I remember that vividly from the whipsaw markets 2001-3, especially when the bond market was restarting after 9/11.  If that crisis had happened later in the month, the quarterly closing prices might not have been as accurate.

Summary for Part 1 (Bonds)

The bond market is complex, far more complex than the stock market.  Pricing the market as a whole is a complex affair, but one for which prices are reasonably calculable.  For the average retail investor investing in ETFs, the bonds are liquidi enough that pricing of NAVs is fairly clean.  But even for a large ugly insurance company bond portfolio, pricing can be significantly accurate.  Next time, I’ll talk about a related market that has its own pricing grid(s) — mortgages and real estate.  Till then.

Photo Credit: Wayne Stadler || Most of us have limited vision, myself included

Photo Credit: Wayne Stadler || Most of us have limited vision, myself included

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

In the time I have been managing money for myself and others in my stock strategy, I set a limit on the amount of cash in the strategy.  I don’t let it go below 0%, and I don’t let it go over 20%.

I have bumped against the lower limit six or so times in the last sixteen years.  I bumped against it around five times in 2002, and once in 2008-9.  All occurred near the bottom of the stock market.  In 2002, I raised cash by selling off the stocks that had gotten hurt the least, and concentrating in sound stocks that had taken more punishment.  In September 2002, when things were at their worst, I scraped together what spare cash I had, and invested it.  I don’t often do that.

In 2008-9 I behaved similarly, though my household cash situation was tighter.  Along with other stocks I thought were bulletproof, but had gotten killed, I bought a double position of RGA near the bottom, and then held it until last week, when it finally broke $100.

But, I had never run into a situation yet where I bumped into the 20% cash limit until yesterday.  Enough of my stocks ran up such that I have been selling small bits of a number of companies for risk control purposes.  The cash started to build up, and I didn’t have anything that I deeply wanted to own, so it kept building.  As the limit got closer, I had one stock that I liked that would serve as at least a temporary place to invest — Tesoro [TSO].  Seems cheap, reasonably financed, and refining spreads are relatively low right now.  I bought a position in Tesoro yesterday.

I could have done other things.  I could have moved the position sizes of my portfolio up, but I would have had to increase the position sizes a lot to have some stocks hit the lower edge of the trading band, but that would have been more bullish than I feel now.  As it is, refiners have been lagging — I can live with more exposure there to augment Valero, Marathon Petroleum and PBF.

I also could have doubled a position size of an existing holding, but I didn’t have anything that I was that impressed with.  It takes a lot to make me double a position size.

As it is, my actions are that of following the rules that discipline my investing, but acting in such a way that reflects my moderate bearishness over the intermediate term.  In the short run, things can go higher; the current odds even favor that, though at the end the market plays for small possible gains versus a larger possible loss.

The credit cycle is getting long in the tooth; though many criticize the rating agencies, their research (not their ratings) can serve as a relatively neutral guidepost to investors.  Corporate debt is high and increasing, and profits are flat to shrinking… not the best setup for longs.  (Read John Lonski at Moody’s.)

I will close this piece by saying that I am looking over my existing holdings and analyzing them for need for financing over the next three years, and selling those that seem weak… though what I will replace them with is a mystery to me.

Bumping up against my upper cash limit is bearish… and that is what I am working through now.

Full disclosure: long VLO MPC PBF and TSO

ecphilosopher data 2015 revision_21058_image001

You might remember my post Estimating Future Stock Returns, and its follow-up piece.  If not they are good reads, and you can get the data on one file here.

The Z.1 report came out yesterday, giving an important new data point to the analysis.  After all, the most recent point gives the best read into current conditions.  As of March 31st, 2016 the best estimate of 10-year returns on the S&P 500 is 6.74%/year.

The sharp-eyed reader will say, “Wait a minute!  That’s higher than last time, and the market is higher also!  What happened?!”  Good question.

First, the market isn’t higher from 12/31/2015 to 3/31/2016 — it’s down about a percent, with dividends.  But that would be enough to move the estimate on the return up maybe 0.10%.  It moved up 0.64%, so where did the 0.54% come from?

The market climbs a wall of worry, and the private sector has been holding less stock as a percentage of assets than before — the percentage went from 37.6% to 37.1%, and the absolute amount fell by about $250 billion.  Some stock gets eliminated by M&A for cash, some by buybacks, etc.  The amount has been falling over the last twelve months, while the amount in bonds, cash, and other assets keeps rising.

If you think that return on assets doesn’t vary that much over time, you would conclude that having a smaller amount of stock owning the assets would lead to a higher rate of return on the stock.  One year ago, the percentage the private sector held in stocks was 39.6%.  A move down of 2.5% is pretty large, and moved the estimate for 10-year future returns from 4.98% to 6.74%.

Summary

As a result, I am a little less bearish.  The valuations are above average, but they aren’t at levels that would lead to a severe crash.  Take note, Palindrome.

Bear markets are always possible, but a big one is not likely here.  Yes, this is the ordinarily bearish David Merkel writing.  I’m not really a bull here, but I’m not changing my asset allocation which is 75% in risk assets.

Postscript for Nerds

One other thing affecting this calculation is the Federal Reserve revising estimates of assets other than stocks up prior to 1961.  There are little adjustments in the last few years, but in percentage terms the adjustments prior to 1961 are huge, and drop the R-squared of the regression from 90% to 86%, which also is huge.  I don’t know what the Fed’s statisticians are doing here, but I am going to look into it, because it is troubling to wonder if your data series is sound or not.

That said, the R-squared on this model is better than any alternative.  Next time, if I get a chance, I will try to put a confidence interval on the estimate.  Till then.

How Lucky Do You Feel?

How Lucky Do You Feel?

-=-=-=-=-=-=-

Nine years ago, I wrote about the so-called “Fed Model.” The insights there are still true, though the model has yielded no useful signals over that time. It would have told you to remain in stocks, which given the way many panic,, would not have been a bad decision.

I’m here to write about a related issue this evening.  To a first approximation, most investment judgments are a comparison between two figures, whether most people want to admit it or not.  Take the “Fed Model” as an example.  You decide to invest in stocks or not based on the difference between Treasury yields and the earnings yield of stocks as a whole.

Now with interest rates so low, belief in the Fed Model is tantamount to saying “there is no alternative to stocks.” [TINA]  That should make everyone take a step back and say, “Wait.  You mean that stocks can’t do badly when Treasury yields are low, even if it is due to deflationary conditions?”  Well, if there were only two assets to choose from, a S&P 500 index fund and 10-year Treasuries, and that might be the case, especially if the government were borrowing on behalf of the corporations.

Here’s why: in my prior piece on the Fed Model, I showed how the Fed Model was basically an implication of the Dividend Discount Model.  With a few simplifying assumptions, the model collapses to the differences between the earnings yield of the corporation/index and its cost of capital.

Now that’s a basic idea that makes sense, particularly when consider how corporations work.  If a corporation can issue cheap debt capital to retire stock with a higher yield on earnings, in the short-run it is a plus for the stock.  After all, if the markets have priced the debt so richly, the trade of expensive debt for cheap equity makes sense in foresight, even if a bad scenario comes along afterwards.  If true for corporations, it should be true for the market as a whole.

The means the “Fed Model” is a good concept, but not as commonly practiced, using Treasuries — rather, the firm’s cost of capital is the tradeoff.  My proxy for the cost of capital for the market as a whole is the long-term Moody’s Baa bond index, for which we have about 100 years of yield data.  It’s not perfect, but here are some reasons why it is a reasonable proxy:

  • Like equity, which is a long duration asset, these bonds in the index are noncallable with 25-30 years of maturity.
  • The Baa bonds are on the cusp of investment grade.  The equity of the S&P 500 is not investment grade in the same sense as a bond, but its cash flows are very reliable on average.  You could tranche off a pseudo-debt interest in a way akin to the old Americus Trusts, and the cash flows would price out much like corporate debt or a preferred stock interest.
  • The debt ratings of most of the S&P 500 would be strong investment grade.  Mixing in equity and extending to a bond of 25-30 years throws on enough yield that it is going to be comparable to the cost of capital, with perhaps a spread to compensate for the difference.

As such, I think a better comparison is the earnings yield on the S&P 500 vs the yield on the Moody’s BAA index if you’re going to do something like the Fed Model.  That’s a better pair to compare against one another.

A new take on the Equity Premium

A new take on the Equity Premium!

=-=-=-=-=-=-

That brings up another bad binary comparison that is common — the equity premium.  What do stock returns have to with the returns on T-bills?  Directly, they have nothing to do with one another.  Indirectly, as in the above slide from a recent presentation that I gave, the spread between the two of them can be broken into the sum of three spreads that are more commonly analyzed — those of maturity risk, credit risk and business risk.  (And the last of those should be split into a economic earnings  factor and a valuation change factor.)

This is why I’m not a fan of the concept of the equity premium.  The concept relies on the idea that equities and T-bills are a binary choice within the beta calculation, as if only the risky returns trade against one another.  The returns of equities can be explained in a simpler non-binary way, one that a businessman or bond manager could appreciate.  At certain points lending long is attractive, or taking credit risk, or raising capital to start a business.  Together these form an explanation for equity returns more robust than the non-informative academic view of the equity premium, which mysteriously appears out of nowhere.

Summary

When looking at investment analyses, ask “What’s the comparison here?”  By doing that, you will make more intelligent investment decisions.  Even a simple purchase or sale of stock makes a statement about the relative desirability of cash versus the stock.  (That’s why I prefer swap transactions.)  People aren’t always good at knowing what they are comparing, so pay attention, and you may find that the comparison doesn’t make much sense, leading you to ask different questions as a result.

 

This is the fourth article in this series, and is here because the S&P 500 is now in its second-longest bull market since 1928, having just passed the bull market that ended in 1956.    Yeah, who’da thunk it?

This post is a little different from the first three articles, because I got the data to extend the beginning of my study from 1950 to 1928, and I standardized my turning points using the standard bull and bear market definitions of a 20% rise or fall from the last turning point.  You can see my basic data to the left of this paragraph.

Before I go on, I want to show you two graphs dealing with bear markets:

As you can see from the first graph, small bear markets are much more common than large ones.  Really brutal bear markets like the biggest one in the Great Depression were so brutal that there is nothing to compare it to — financial leverage collapsed that had been encouraged by government policy, the Fed, and a speculative mania among greedy people.

The second graph tells the same story in a different way.  Bear markets are often short and sharp.  They don’t last long, but the intensity in term of the speed of declines is a little more than twice as fast as the rises of bull markets.  If it weren’t for the fact that bull markets last more than three times as long on average, the sharp drops in bear markets would be enough to keep everyone out of the stock market.

Instead, it just keeps many people out of the market, some entirely, but most to some degree that would benefit them.

Oh well, on to the gains:

Like bear markets, most bull markets are small.  The likelihood of a big bull market declines with size.  The current bull market is the fourth largest, and the one that it passed in duration was the second largest.  As an aside, each of the four largest bull markets came after a surprise:

  1. (1987-2000) 1987: We knew the prior bull market was bogus.  When will inflation return?  It has to, right?
  2. (1949-56) 1949: Hey, we’re not getting the inflation we expected, and virtually everyone is finding work post-WWII
  3. (1982-7) 1982: The economy is in horrible shape, and interest rates are way too high.  We will never recover.
  4. (2009-Present) 2009: The financial sector is in a shambles, government debt is out of control, and the central bank is panicking!  Everything is falling apart.
Sometimes you win, sometimes you lose...

Sometimes you win, sometimes you lose…

Note the two dots stuck on each other around 2800 days.  The arrow points to the lower current bull market, versus the higher-returning bull market 1949-1956.

Like bear markets, bull markets also can be short and sharp, but they can also be long and after the early sharp phase, meander upwards.  If you look through the earlier articles in this series, you would see that this bull market started as an incredibly sharp phenomenon, and has become rather average in its intensity of monthly returns.

Conclusion

It may be difficult to swallow, but this bull market that is one of the longest since 1928 is pretty average in terms of its monthly average returns for a long bull market.  It would be difficult for the cost of capital to go much lower from here.  It would be a little easier for corporate profits to rise from here, but that also doesn’t seem too likely.

Does that mean the bull is doomed?  Well, yes, eventually… but stranger things have happened, it could persist for some time longer if the right conditions come along.

But that’s not the way I would bet.  Be careful, and take opportunities to lower your risk level in stocks somewhat.

PS — one difference with the Bloomberg article linked to in the first paragraph, the longest bull market did not begin in 1990 but in 1987.  There was a correction in 1990 that fell just short of the -20% hurdle at -19.92%, as mentioned in this Barron’s article.  The money shot:

The historical analogue that matches well with these conditions is 1990. There was a 19.9% drop in the S&P 500, lasting a bit under three months. But the damage to foreign stocks, small-caps, cyclicals, and value stocks in that cycle was considerably more. Both the Russell and the Nasdaq were down 32% to 33%. You might remember United Airlines’ failed buyout bid; the transports were down 46%. Foreign stocks were down about 30%.

And then Saddam Hussein invaded Kuwait.

That might have been the final trigger. The broad market top was in the fall of 1989, and most stocks didn’t bottom until Oct. 11, 1990. In the record books, it was a shallow bear market that didn’t even officially meet the 20% definition. But it was a damaging one that created a lot of opportunity for the rest of the 1990s.

FWIW, I remember the fear that existed among many banks and insurance companies that had overlent on commercial properties in that era.  The fears led Alan Greenspan to encourage the FOMC to lower rates to… (drumroll) 3%!!!  And, that experiment together with the one in 2003, which went down to 1.25%, practically led to the idea that the FOMC could lower rates to get out of any ditch… which is now being proven wrong.

Every now and then, you will run across a mathematical analysis where if you use a certain screening, trading, or other investment method, it produces a high return in hindsight.

And now, you know about it, because it was just published.

But wait.  Just published?

Think about what doesn’t get published: financial research that fails, whether for reasons of error or luck.

Now, luck can simply be a question of timing… think of my recent post: Think Half of a Cycle Ahead.  What would happen to value investing if you tested it only over the last ten years?

It would be in the dustbin of failed research.

Just published… well… odds are, particularly if the data only goes back a short distance in time, it means that there was likely a favorable macro backdrop giving the idea a tailwind.

There is a different aspect to luck though.  Perhaps a few souls were experimenting with something like the theory before it was discovered.  They had excellent returns, and there was a little spread of the theory via word of mouth and unsavory means like social media and blogs.

Regardless, one of the main reasons the theory worked was that the asset being bought by those using the theory were underpriced.  Lack of knowledge by institutions and most of the general public was a barrier to entry allowing for superior returns.

When the idea became known by institutions after the initial paper was published, a small flood of money came through the narrow doors, bidding up the asset prices to the point where the theory would not only no longer work, but the opposite of the theory would work for a time, as the overpriced assets had subpar prospective returns.

Remember how dot-com stocks were inevitable in March of 2000?  Now those doors weren’t narrow, but they were more narrow than the money that pursued them.  Such is the end of any cycle, and the reason why average investors get skinned chasing performance.

Now occasionally the doors of a new theory are so narrow that institutions don’t pursue the strategy.  Or, the strategy is so involved, that even average quants can tell that the data has been tortured to confess that it was born in a place where the universe randomly served up a royal straight flush, but that five-leaf clover got picked and served up as if it were growing everywhere.

Sigh.

My advice to you tonight is simple.  Be skeptical of complex approaches that worked well in the past and are portrayed as new ideas for making money in the markets.  These ideas quickly outgrow the carrying capacity of the markets, and choke on their own success.

The easiest way to kill a good strategy is to oversaturate it too much money.

As such, I have respect for those with proprietary knowledge that limit their fund size, and don’t try to make lots of money in the short run by hauling in assets just to drive fees.  They create their own barriers to entry with their knowledge and self-restraint, and size their ambitions to the size of the narrow doors that they walk through.

To those that use institutional investors, do ask where they will cut off the fund size, and not create any other funds like it that buy the same assets.  If they won’t give a firm answer, avoid them, or at minimum, keep your eye on the assets under management, and be willing to sell out when they get reeeally popular.

If it were easy, the returns wouldn’t be that great.  Be willing to take the hard actions such that your managers do something different, and finds above average returns, but limits the size of what they do to serve current clients well.

Then pray that they never decide to hand your money back to you, and manage only for themselves.  At that point, the narrow door excludes all but geniuses inside.