I have not been a fan of this rally, and I have been selling into it.  I do have a rule for equity clients — cash never goes above 20%.  I have been close to that recently, and after rebalancing some companies that have hit the top of the weighting band, I have bought those companies with the lowest weights in the portfolio.  I have also added some stable companies in the recent past — Berkshire Hathaway, Ingram Micro, Validus Holdings, AFLAC, and CST Brands.

My next quarterly reshaping comes up next week, and again, I will be looking at neglected industries in the market for areas to purchase.  When the momentum runs this hard, I have to be content to trail (though I haven’t been trailing).  I have to ask where things will be three or more years from now, rather than ponder the next quarter.  The answer to that is more murky than I would want, because of abnormal economic policy.  It makes us all more skittish, and obscures price signals.

I have suggested in the past that a good solution in the face of uncertainty is to do half of what you would like to do. Doing half breaks the psychological stranglehold of fear and greed, because regardless of what happens, part of your decision was a success.

You could also start to make a “shopping list.”  Start looking for names that you would like to buy 10, 20, 30% lower, and set alerts.  Who knows how rapidly things will move when the correction or bear market comes.

You could keep a close eye on the 200-day moving average for the S&P 500, waiting for the index to cross under that as a sell signal, but if you want to be ahead of the crowd, maybe you want to use the 190-day moving average.

I tend to use industry selection and other factors, like balance sheet strength and reliability of cash flows as my main risk reduction tools rather than outright reduction of equities owned.  In general, I have been a good picker of stocks over the last 13 years, and I want to continue using that advantage.

With bonds, I am playing it safe with short and intermediate corporates, and taking reasoned chances with emerging markets debt.  Beyond that, I am thinking of buying long Treasuries as a deflation hedge.

The equity market is well above where long-term valuation measures like the Q-ratio, and CAPE10 would value it.  Most of that is due to low interest rates and high levels of QE.  How certain are you that both will persist, and for how long?  Personally, I think both will persist for some time, but not forever.  Profits attract competitors, and low rates discourage savers.

Though we don’t know when change is coming, we have to be ready for change.  Whatever you do for defense, make preparations now to be defensive; this era and valuation levels will not persist.

Aside from that, remember that when a system is so artificially supported, it relies on peace & continued support from governments.  Either could vary.  Peace is not certain, and neither is the current set of economic policies.  Be ready, because there can be all manner of surprises.

Full disclosure: long BRK/B, IM, VR, AFL, CST

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

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

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

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

If you use any of this, choose what you use off of your own trading style. If you trade frequently, stay in the red zone. Trading infrequently, play in the green zone — don’t look for momentum, look for mean reversion.  I generally play in the green zone because I hold stocks for 3 years on average.

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

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

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

I like some technology names here, some telecom related, some basic materials names, particularly those that are strongly capitalized.

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

At present, I am trying to be defensive. I don’t have a lot of faith in the market as a whole, so I am biased toward the green zone, looking for mean-reversion, rather than momentum persisting. The red zone is pretty cyclical at present. I will be very happy hanging out in dull stocks for a while.

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

The Red Zone has a Lot of Financials; be wary of those.  I’m considering paring back my insurers.

What I find fascinating about the red momentum zone now, is that it is loaded with noncyclical companies. That said, it has been recently noted in a few places how cyclicals are trading at a discount to noncyclicals at present.

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

That said, it is tough when noncyclical companies are relatively expensive to cyclicals in a weak economy. Choose your poison: high valuations, or growth that may disappoint.

But what would the model suggest?

Ah, there I have something for you, and so long as Value Line does not object, I will provide that for you. I looked for companies in the industries listed, but in the top 3 of 5 safety categories, an with returns estimated over 18%/year over the next 3-5 years. The latter category does the value/growth tradeoff automatically. I don’t care if returns come from mean reversion or growth.

But anyway, as a bonus here are the names that are candidates for purchase given this screen. Remember, this is a launching pad for due diligence.

Full disclosure: Long APOL IM

Twitter is serendipitous to me.  I don’t track it all day long, or I would never get anything done.  Usually, I keep it off, unless I am sending off tweets.  But I accidentally saw a tweet from Cardiff Garcia of FT Alphaville. regarding a presentation done by Brad DeLong.  Here it is:

This slide from @delong‘s presentation made me belly-laugh (via http://bit.ly/ZmqeKq ): pic.twitter.com/beZcJs8Rwk

So I looked, and here is what I found:

I looked at it and said, “Huh, yeah, whoever did this was a total hack.  Totally arbitrary curve drawing.”  But then I thought a little more.  “If I estimated a quadratic equation (parabola) what would it look like versus the data?”

So I took the points and eyeball estimated the values, and dropped them into an Excel spreadsheet, and ran the regression.  Turns out that both DeLong and the Wall Street Journal, and those they relied on were wrong.  Remember that the horizontal axis is marginal corporate tax rate, and the vertical axis is corporate taxes received as a percentage of GDP.

At a 5% level of significance, the equation is not significant, and the coefficients are not significant, though they are close.  The signs all go the right way, and the intercept is near zero.  That said, the prob-value for the equation as a whole (F test), is 6.5%, not far from the 5% threshold, so it looks like there is some validity to the idea that as marginal corporate tax rates rise, so do corporate taxes as a percentage of GDP, until the taxes get too high.

Only one data point of the above analysis, Norway, is statistically significant, with an error 3+ standard deviations versus the model.  Norway is different, with its huge sovereign wealth fund, so what happens to the model if we exclude it, and re-run the model?

Under these conditions, at a 5% level of significance, the equation is significant, with a prob-value of 1.4%, and all but one of the coefficients are significant, and the coefficient on the squared term has a prob value of 11.6%.  The signs all go the right way, and the intercept is near zero.  It looks like there is some validity to the idea that as marginal corporate tax rates rise, so do corporate taxes as a percentage of GDP, until the taxes get too high.

I didn’t test anything else.  With both equations we learn two ideas:

• The tax take tops out at a 30% marginal rate
• You don’t give up much if you set the marginal rate at 20%

Now, this is a cursory analysis on a limited data set.  But the idea that corporations start to go elsewhere when tax rates get too high is a reasonable hypothesis.  The WSJ analysis was a joke, but so was DeLong’s dismissal of the data.

I’m no great fan of the idea of the Laffer Curve, never have been, but this was the first time I gained some sympathy for the idea.  So, be wary who you listen to, study statistics and their limitations, and generally, be skeptical, but not cynical.  There is truth out there, we just need to find it.

PS — If anyone wants me to publish the detailed statistics, I will, but I omitted them because they make most of my readers’ eyes glaze over.

From one of my longtime readers:

I just wanted to toss this suggestion your way and the motivation is partly selfish, but given the decline in gold the last 3-4 days (I actually exited all my long positions around 1500-1505 last Friday based on the breach of the technical support level at 1525-1535 and am now short in my trading account from that same level) I’d be interested to get your qualitative thoughts and maybe an update on your refined quantitative model with negative real interest rates and where it says gold should be trading.

If it turns out substantially above the current price of 1360, I’d be curious if you think that model isn’t valid or if gold is a bargain here.  This article here got my wheels turning that bases on a gold price model on ratio to CPI:

http://www.marketwatch.com/story/golds-fair-value-is-800-an-ounce-2013-04-16?link=MW_story_popular

But to come up with an estimate of gold’s fair value, they calculate a ratio of gold to inflation going back as far as they were able to obtain data. They report that this ratio, when expressed in terms of the U.S. Consumer Price Index, has averaged about 3.2-to-1. Even at $1,400 an ounce, this ratio stands at 6.03-to-1, or nearly double this average. 

From a qualitative standpoint, the negative interest rate model made the most sense to me simply from a critical thinking standpoint.  The relationship to CPI seems less reasonable to me if one starts with premise that gold is an alternative currency.

Anyways, thanks for any response or addressing this on your blog.

Links: The Gold Medal Gold Model, Gold does Nothing.

I updated my gold model.  This is what it looks like without re-estimating the parameters:

And this is what it looks like after re-estimating the parameters:

The real cost of carry in holding gold is negative, and it has been consistently negative for the last five years. and mostly so for the last ten years.  Thus the run-up in the price of gold over the last 10 years.

Now models are just that, models.  I can make three seemingly contradictory statements about this model:

• The old models did not predict the path of the gold prices well.
• The re-estimated models fit the data better than the old models.
• If the model is accurate, there is economic pressure to make the price of gold rise.

My hypothesis at this point in time is that easily tradable products based on gold encouraged speculative pressure, leading the price of gold to overshoot, and now it is correcting.  That said, when the real cost of carry is so negative, gold should appreciate.

Alternatively, we could try to develop a supply-driven model of gold, where we estimate the marginal costs of mining an additional ounce of gold.  Ore depletion is significant, but the effect is relatively constant compared to demand for gold.  It also helps to explain why the stocks of most gold miners have not done well, even with a rising gold price.

We often like to think that if a commodity price is rising, the stock of the producer must do even better.  Not always true, if the prices of extraction/production rise faster than the commodity price, as it has been with gold producers, the stocks will be a bad investment.

My final opinion is this: if you have a 5-year time horizon, I think you will do well with physical gold, where you take delivery, and store it yourself.  With easily tradable paper versions of gold, it is less clear, because you would need to analyze the actual assets.  There might be some credit risk involved.

I don’t think the currency devaluation competition is going away anytime soon, so gold will likely do well against paper.  The real question is when will some major country decide to give up and raise taxes dramatically, inflate, or default.  Aside from the raising taxes scenario, gold should do pretty well.  I might get less optimistic if the gold miners began making significant money,producing much more gold, but producing gold remains a hard business.

The following was published by RealMoney on 4/26/2006.  As with all of these “classic” articles, I republish them because they aren’t available at RealMoney any more.  They changed their system for links, and so articles and comments that I put a lot of work into have disappeared.

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Investing

If you believe in the trend but prices are high, take a half position.

Despite U.S. automakers’ woes, cars will be built by someone; this makes stronger parts suppliers a good play.

Global economic development means more demand for chicken.

One of the most important things to understand with investment ideas is what time period they are for. Sometimes a given asset can take different directions over the short, intermediate and long terms.

Imagine for a moment that you buy the thesis that a large portion of the world is joining the capitalist economy, and that this will lead many more people and businesses in developing countries to demand more goods consistent with what we view as a middle-class lifestyle. That’s a secular trend that will play out over many years. It can be a guiding theme that can help organize investment ideas over the long term.

Now, say that your interpretation of that secular trend implies higher worldwide demand for foodstuffs, metals, timber and energy. However, when you look at the valuations of some of the companies affected by the trend, they appear to be too high, and profit margins are above historical norms. (Valuations are in fact reasonable for many companies in these sectors, but play along with me for a moment.)

You are faced with a problem, then. You think the secular trend is valid, but that much of the story is presently anticipated by current valuations. What to do? One technique that I have used in situations like this is to buy half of what I would if valuations were reasonable (which occasionally aggravates my boss, who is an all-or-nothing kind of guy).

If the stocks go down, I would come up to a full position. If the market gets crazier and valuations rise, I would punt out the smaller position for a gain. If the market muddles somewhat trendlessly, I would buy and sell using my rebalancing discipline, which will clip a couple of extra percentage points over time.

There are alternatives, though. You could buy a full position, but then you are committing to the stock for the long run on the idea that the secular trend will dominate over valuations. You’d better be right, because with higher valuations than normal, being wrong has a greater cost.

You also could do nothing. After all, valuations are extended, and you won’t just pay anything for a stock. This strategy presumes an interruption in the general trend will be coming. That may or may not happen; high valuations often get higher for stocks in a winning thesis. Paying up for a good idea is often a good strategy, but the tradeoff between valuation and the secular trend is a difficult balancing act.

Part of working that tradeoff comes with experience, but I would argue that it also requires humility — the market always finds a new way to make a fool out of you. Always consider what could go wrong. Conservatism means that you will always stay in the game, and staying in the game for a long time is the secret to compounding returns.

The Internet Bubble

Let me give you a few real-world examples. Think of the Internet bubble. The long-term prognosis that the Internet would be big was correct (in hindsight), but valuations were screaming “Don’t play here,” and many concepts were quite marginal from a cash-flow standpoint. That said, the technicals were screaming, “Momentum, baby! Time to play!”

My solution was to sit it out. I figured that, eventually, the cheap financing would run out and the market trend would shift. The problem was, it lasted two years longer than I anticipated.

Maybe I left something on the table. I could have played with smaller position sizes, or played with a mental “stop order” in the back of my mind. That said, it didn’t fit my personality, and I didn’t feel that I could evaluate who the survivors would be, so my optimal decision was to sit it out. (I didn’t short it because the momentum was too great. Never argue with a liquidity wave.)

Industries in Secular Decline

What if you are looking at an industry in secular decline, such as the photo film business (think of how Kodak (EK:NYSE) has fumbled, or, worse, Polaroid), fixed-wire phone service companies, or the newspapers? All of these are being displaced by new technologies.

Verizon (VZ:NYSE) looks cheap and has a nice dividend. Is it a candidate to buy?

This is an example of Warren Buffett’s concept of “cigar butt” investing: Someone may have tossed it on the ground, but you can still get a few good puffs out of it. The company has limited growth potential unless a radical new strategy gets introduced, and that could be costly, or even fail. I had better get this company extremely cheap to compensate for potentially falling earnings at some point in the future. Even a wasting trust has a proper price, so if I can get it at a level that reflects a 15% annualized return, that could be a great investment.  One nice thing about declining industries is that there usually isn’t a lot of direct competition.

Here’s one more example: auto parts. I own Johnson Controls (JCI:NYSE) and Magna International (MGA:NYSE) , two companies with strong balance sheets that are picking up market share against weaker competitors. Automobiles are going to be built, even if GM and Ford aren’t going to be building as many of them.

This is one part of the auto sector where you can have moderate growth, and the stronger suppliers can do far better than the average. I still want to buy them cheap, but I can afford to pay a little more for quality in markets where quality is scarce. In this case, lower-quality companies could be cheaper, but they aren’t the ones to buy when an industry is under stress.

Playing Chicken

As the developing world grows, so will demand for animal protein. To me, that means chicken.

Valuations are favorable here, because many investors are scared about avian flu. Whole flocks of birds might have to be culled if even a few get sick. That said, large North American poultry producers isolate their birds from wild birds, and even from humans who have the flu.

The risk is overstated, and once the pandemic is over, valuations will rise. (Some people are mistakenly avoiding chicken, even though there is no chance of getting avian flu if the chicken is properly cooked.) I own Gold Kist (GKIS:Nasdaq) and Industrias Bachoco SA (IBA:NYSE) , but am considering whether I shouldn’t increase my exposure and add Pilgrim’s Pride (PPC:NYSE) , or Sanderson Farms (SAFM:Nasdaq). Tyson (TSN:NYSE) is too diversified, and I’m not crazy about the management.

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Full disclosure in 2013: I am still long Industrias Bachoco SA [IBA] — what a great unknown company.

Note: this was published at RealMoney on 7/2/2004.  This was part four of a  four part series. Part One is lost but was given the lousy title: Managing Liability Affects Stocks, Pt. 1.  If you have a copy, send it to me.

Fortunately, these were the best three of the four articles.

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Investing Strategies

Some groups can reinforce their own behavior in the market, causing booms and busts.

Balance sheet players tend to be strong holders.

Liquidity can change the market landscape.

In Part 1 of this column, I began describing the various classes of investors and their investment behavior. In Part 2, I’ll continue that description, and will follow it up by explaining how some classes of investors can temporarily reinforce their own behavior, causing booms and busts. Finally, I will offer practical ways you can benefit from understanding the behaviors of different investor classes.

8. Leveraged Private Investors

The use of leverage gives the investor the ability to make more out of his bets than his equity capital would otherwise allow, but eliminates some of the advantages that the unleveraged possess. Investors that are leveraged do not entirely control their trade; if their assets decline enough in value, either they or the margin desk will reduce their position.

Leveraged investors are in the same position as the European banks that I discussed in Part 1. Worry sets in as one gets near a margin call, not when the margin call happens. As worry sets in, mental pressures to change the asset positions materialize. The challenge to the investor is to decide whether to liquidate, or take chances. Being forced to make a decision leads to a higher probability, in my opinion, of making the wrong decision.

In addition, leveraged longs have to pay for the privilege of financing additional assets. With overnight rates low today, that might not seem like much of a cost. But when the market is in the tank and interest rates are sky-high, as they were from 1979 to 1982, the cost of leveraged speculation is a deterrent and helps keep a lid on the market.

9. Short-Sellers

Being short is not the opposite of being long. It is closer to the opposite of being a leveraged long. Shorts do not entirely control their trade; if their shorts rise enough in value, either they or the margin desk will reduce their position. This is the opposite of leveraged longs. Remember, unleveraged longs can stay put as long as they like, and almost no one can force them to change. Shorts can be forced to cover through a squeeze, whether through rising prices threatening their solvency or a decrease in borrowable shares from longs moving their shares from margin to cash.

Stocks with a large short interest relative to the float, like Taser (TASR:Nasdaq) , can behave erratically with little regard to anything more than the short-term technicals of trading. (If fundamental investing is akin to a chess game, trading Taser is more akin to a street brawl.)

Short-sellers also have costs that unleveraged longs don’t face. When it is difficult to borrow shares (i.e., the borrow is tight), you might have to pay for the privilege of borrowing. As an example, when I was short Mony Group, I had a 2% annualized rate to pay on the last block of shares that I shorted. The rest came free, but that was before the trade got crowded. (When the borrow is not tight and if you are big enough, it is possible to get a credit, but that’s another story.)

Another cost is paying any dividend that the company might pay. Granted, the stock is likely to drop by the amount of the dividend, but cash going out the door to support a trade makes a trade more difficult to hold on to.

10. Options Traders

Buyers of options fully control their trade and pay a premium for the privilege. Sellers of options give up some control of their trade and receive a premium for their trouble. Being short an option is like being short a stock; theoretically, the risk is unlimited. If the short options of an investor rise enough in value, either they or the margin desk will reduce their position. Long option investors face no such constraints, but they do face the continual decay of the time premium of their options.

When there are company-issued options outstanding, such as warrants, convertible preferreds and convertible bonds, another trading dynamic can develop. Because the company has offered the call options on its stock, unlike other investors, it can issue stock to satisfy calls. The dilution from share issuance can put a ceiling over the price of the stock near the strike price for the call options until enough demand exists for the stock that it overcomes the dilution.

One more example of embedded options shows up in the residential mortgage bond market. Residential mortgages contain an option that allows the mortgage to be prepaid. Mortgage bond managers, who often manage to a constant duration (interest-rate sensitivity), run into the problem that their portfolios lengthen when rates rise, and shorten when rates fall. This can make them buyers of duration (longer mortgages or noncallable Treasuries) when rates fall, and sellers when rates rise.

In either case, with enough mortgage managers (and mortgage originators, who are in the same boat) doing this, it can become self-reinforcing because many market players buy into a rising market and sell into a falling market. This has an indirect effect on the Treasury and swap markets because mortgage hedgers use them to adjust their overall interest-rate sensitivity. In general, mortgage hedgers are weak holders of Treasuries, which they sell off as rates rise.

Balance Sheet Players vs. Total Return Players

I find it useful to divide the players in the investment universe into two camps: balance sheet players and total return players. Balance sheet players can lose it all and then some. Total return players can lose only what they have invested and include mutual funds (including index funds), unleveraged private investors, defined benefit plans, option buyers and endowments. Balance sheet players include banks, insurance companies, leveraged private investors and option sellers.

Total return players tend to resist — or at least are capable of resisting — market trends, which provide stability in the market. At the edges of negative price movements, balance sheet players find that they have to sell risky assets in order to preserve themselves. In severe market conditions, balance sheet players can make market movements more extreme.

I think it helps to view the behavior of balance sheet players through the lens of self-reinforcement. When there are too many of them crowding into a trade, there is the potential for instability. If the price of the asset has been bid up to the point to where a buy-and-hold investor would feel that he could not obtain a free cash flow yield adequate to compensate him for the risk of the purchase, then the asset is unsustainably high, which does not mean that it can’t go higher. When you see long-term investors exiting, it’s usually time to leave.

Fueled by leverage, some players will increase their bets as the price of the asset rises because they have more buying power with a more expensive asset. Finally, a few smart players start to sell and the process works in reverse as leverage levels increase for balance sheet players with a large concentration in the stock and a self-reinforcing cycle of selling begins. The same boom-bust cycle can happen with total return players, but it would be more muted because of the lack of leverage.

At the end of the bust, the buyers typically are unleveraged buy-and-hold investors. For example, I remember picking over tech and telecom stocks in 2001-02 that had been trashed after the bubble burst. This is a sector of the market that I don’t play in often, because I don’t know it so well; that said, it became 30% of my portfolio. Many of those stocks were trading for less than their net cash and a few were even earning money. My thought at the time was that if I tucked a few of these stocks away and held them for five years or so, I’d have something better at the end. With the bull market of 2003, my exit came sooner than I expected; other market players saw the potential of the cheap, conservative tech companies that I held and liked them more than I did.

This brings me back to weak and strong hands. In general, total return players have stronger hands than balance sheet players, at least when market values are out of whack with long-term fundamentals.

Illiquidity and LTCM

An asset is illiquid when the bid-ask spread is wide, or even worse, when there is no bid or ask for a given asset in the short run. This can happen with large orders in small-cap stocks and in “off the run” corporate bonds. Often an illiquid asset offers a higher potential return than a more liquid asset; given the disadvantage of illiquidity, in a normal market it would have to. Even a liquid asset can act illiquid if you hold a large amount of it relative to the total float. Trying to sell rapidly would drive down its price.

To hold illiquid assets, you either have to hold them with equity or a low degree of leverage with a funding structure for the leverage that can’t run away. One example is the type of portfolio I ran in the mid-1990s: unleveraged micro-cap value stocks. Another example is Warren Buffett’s portfolio. He buys whole companies and large positions in other companies, and funds those purchases with a modest amount of leverage from his insurance reserves.

My counterexample is more interesting (failure always is). Long Term Capital Management for the most part bought illiquid bonds and shorted liquid bonds that were otherwise similar to the illiquid bonds. When LTCM was small relative to the markets that it played in, it could move in and out of positions reasonably well, and given the nature of bonds, absent a default, there was a natural tendency for the bonds to converge in value as they got close to maturity.

As LTCM became better known, it received more capital to invest. Assets grew from profits as well. Wall Street trading desks began to figure out some of the trades that LTCM was making and started to mimic the firm. This made LTCM’s position more illiquid. It was fundamentally short liquidity, leveraged up using financing that could disappear in a crisis and had LTCM wannabes swarming around its positions.

At the beginning of 1998, it had earned huge returns and its managers were considered geniuses. The only problem was that they were running out of places to put money. The yield spreads between their favored illiquid and liquid bonds had narrowed considerably. “The juice had been squeezed out of the trade,” but they still had a lot of money to manage.

By mid-1998, with the Asian crisis brewing and Russia defaulting, there came a huge premium for liquidity. Everyone wanted to get liquid all at once. Liquid bonds rose in price, while illiquid bonds fell. The LTCM imitators on Wall Street got calls from their risk control desks telling them that they had to liquidate the trades that mimicked LTCM; the trades were losing too much money. In at least one case, it imperiled the solvency of one investment bank. But at least the investment banks had risk-control desks to force them to take action. LTCM did not, and the unwinding of all the trades by the investment banks worsened its position.

When the severity of the situation finally dawned on the investment banks, with the aid of the Federal Reserve, the investment banks realized that there was no way to easily solve the situation. LTCM couldn’t be liquidated; its positions were so large that a “fire sale” meant that the investment banks that lent it money would have to take a haircut. LTCM needed time and a bigger balance sheet, if the investment banks were to be repaid. The investment banks eventually agreed to recapitalize LTCM funds and unwind the trades at a measured pace. Even the equity investors got something back when the liquidation of LTCM was complete. LTCM’s ideas weren’t all bad, but it was definitely misfinanced.

Final Advice

Keep these basic rules in mind as you consider how to apply these concepts to your own trading. They aren’t commandments, but paying attention to them will help you make more informed investment decisions.

1. All good investment relies at least implicitly on sound asset-liability management. Assets should be matched to the type of investor and funding structure that can best support them.
2. Understand the advantages that you have as an investor, particularly how your own cash flow and funding structure affect your investing.
3. Try to understand who else is in a trade with you, what their motivations are, their ability to carry the trade, etc.
4. Don’t overleverage your positions. Always leave enough room to be able to recover from a bad scenario.
5. Be aware of the effects that changing demographics may have on pension plans and individual investors.
6. Always play defense. Consider what can go wrong before you act on what can go right.
7. Be contrarian. Maximize your flexibility when the market pays you to do so. Be willing to sell into manias and buy after crashes.

I left one small question for last; I gave a partial answer to this one at the conference.  I think I was the only one that said much on it.  Here it is:

Where does academic theory fail in finance and in economics?

Little questions, big answers.  How do you eat an elephant?  One bite at a time.  Let’s start with math in economics:

1) We have to reduce the complex math in economics — I think we are trying to apply math where it is not valid.  As such, the true strength of ability to explain what is going on decreases, while economic becomes an odd “inside game” for a funny group of mathematicians trying to make sense of an idealized world that bears little resemblance to our own world.

2) The next piece is on maximizing utility or profits.  Maximizing takes work, assuming one can even do it.  Work is a negative, so people conserve on that.  Most of us know this: we look for a solution that is “good enough,” and do it.  That means we don’t maximize utility, and the pretty equations don’t represent reality.

What’s worse is that men care more about relative results than absolute results.  We would rather be kings over an impoverished realm rather than middle class in a wealthy country.  We are worse than greedy; we are envious.

It’s even worse for firms.  There you have agency problems where the management often has its own goals that do not maximize profits, or their net present value, but maximize the benefits they receive.  Boards are frequently a cover for management, rather than advocates for the shareholders.

Regardless, since firms don’t maximize, the elegant math does not work. Putting it simply, if you want to understand economics better, don’t listen to economists — become a businessman.  An ordinary businessman knows more about how the world works than a neoclassical economist.

3) One of the beauties of a capitalist economy is its dynamism.  It adapts to changing needs and desires.  The variation is considerable; as an example, go through your supermarket and try to count the total number of different tomato products.  Or  look at the amazing degree of variety in a major tools catalog.  Or go to Costco, Walmart, Home Depot, Ikea, and look at the incredible variety that exists under one roof.

But that level of variety cannot be mathematically accommodated by economics.  They have to aggregate the complexity into categories, and a lot of the reality is lost in the process.  That is why I distrust  many economic aggregates, such as inflation, GDP, etc.  Politicians find “economists” to suit their political ends, and they come up with complex reasoning for why measured inflation is higher than it should be, inequality is rising, etc.  You can find an economic advocate for almost anything.

Macroeconomics

4) Because of the aggregation problem, the link between microeconomics and macroeconomics is made weak, especially since utility cannot be compared across any two people, and yet the economists mumble, and implicitly do it anyway.

5) At least with microeconomics, we can agree that demand falls as prices rise, and supply rises as prices rise.  But with macroeconomics, there is little agreement as to whether a given policy aids real growth or not.  Modern neoclassical economics is to me a bunch of sorcerer’s apprentices playing around with very large and crude tools that they think can affect the economy, only to find the results are not what they expect.  Somewhere, economists got the naive idea that they could eliminate the boom-bust cycle, only to find that by eliminating minor busts, they set up the conditions for growth in indebtedness, leading to a huge bust.  Far better to be McChesney Martin, or Volcker, who let recessions do their work, than slaves of the government who did not — Burns, Miller, Greenspan or Bernanke.

Take inflation as an example.  Does printing more money, or creating more credit boost asset prices, product & services prices, both, or neither?  The answer to this is not clear.  The Fed has taken many actions over the past 30 years, using a model that assumes tight relationships between short interest rates and inflation/ labor unemployment.  The evidence for these relationships are not evident, except at the extremes.

6) The idea that running deficits to “stimulate” the economy is questionable.  Debts have to be paid back, repudiated or inflated away, any one of which would make business and consumers less confident.  Further, the way the the money is spent makes a great deal of difference.  Much government spending inhibits or does not help economic growth; think of the complexity of the tax code — a recipe for wasted time, and unneeded social enginerring.  Some government spending does aid economic growth, where it lowers the costs of consumption or production — critical infrastructure projects, etc.  But those are rare.  If it were really needed, lower level governments or private industry would do it.

The thing is, most of the deficit spending has not been useful; there’s no economic reason to run such large deficits.  If we were rebuilding all of our aging infrastructure, that would be one thing, but the crazy quilt of tax breaks and subsidies affects behavior, but does not compound and aid growth.

7) We need to admit that culture is not a neutral matter.  Some cultures will have faster economic growth, and others will be slower.  There is no universal culture, no generic economic man.  Some cultures are more enterprising than others.  That has a big impact on growth quite apart from resources, population, education, etc.

8 ) Whether the money is tied to gold or fiat, banking must be tightly regulated.  Solvency of all financial institutions should be tightly regulated.  With financials risks arise when the is too much leverage, and too much leverage that is layered.  Things should be structured such that there is no possibility of dominoes knocking over other dominoes.

• Limit leverage
• Increase liquidity of assets vs liabilities
• Forbid lending to/investing in other financials
• Derivatives should be regulated as insurance, insurable interest must exist, which means that bona fide hedgers must initiate all transactions.

On Finance

9) The first thing to realize is that a mean-variance model for investments is loopy.  First, we can’t estimate the mean or the variance, much less the covariance terms.  There is also good evidence that variances are infinite, or close to it.  Thus the concept of an efficient frontier is bogus.  Far better to try to estimate crudely the likely forward returns on a cash flow basis, the way a businessman would, and weakly factor in the uncertainty of the forecasts.

10) Thus, beta is not risk, and volatility is not risk.  At least at present, until the low volatility funds get too big, there seems to be an anomaly where low volatility equity investing beats high volatility equity investing.  This is consistent with my theory that the relationship of risk and return is non-linear.  Taking no risk brings no return; taking moderate risk brings decent return; taking high risks brings low returns.  There is a sweet spot of prudent risk-taking that brings the best returns on average.

11) Multiple-player game theory indicates that to win, you assemble a coalition with more than 50% of all of the power, and you get disproportionate benefits.  Think about the poor buyer of a home in 2006, going into the closing with the deck staked against him.  Or think about forced arbitration of disputes on Wall Street, where the investors rarely win.

Complexity is not the friend of most ordinary economic actors.  Avoid it where you can.

12) Capital structure does matter; it is not irrelevant like Modigliani and Miller said.  Companies with low leverage tend to return more than companies with high leverage.  There are real costs to being in distress or near distress.

13) Markets can have non-linear feedback loops, like in October 1987, or the “Flash Crash.”  Markets are not inherently stable, and that is a good thing.  Instability shakes out weak players that are relying on a shaky funding base, leaving behind stronger players who understand risk.  It is not wise to try to eliminate the possibility of disasters occurring.  When you do that, pressures build up, and something worse occurs.  Better to let the market be free, and let stupid speculators get burned, so long as they aren’t regulated financial companies.

Ethics matters

14) Economics would be more valuable if it focused what is right, rather than what is “efficient.”  I know there will be differences of opinion here, but a discipline that focused on explicit and implicit fraud could be far more valuable than men who don’t have good models for:

• Inflation
• Asset Allocation
• GDP
• Unemployment
• and more

Imagine applying all of that intelligence to fair dealing in economic relationships, rather than vainly trying to stimulate the economy, and accomplishing nothing good.  It would be like the CFA Institute applied to the economy as a whole.

Hello.  My busy time is over, and I am back to live blogging.  On Tuesday evening, I was one of five speakers at the Towson University Investment Group’s International Market Summit.  It was a fun time.  Before I came, there was a list of 29 questions we could be asked, in addition to Q&A.  As it was we were asked 6 of the questions in the main period, and 2 more in the Q&A.

I told the students at Towson that I would post a bunch of links to my blog for the questions asked that I have already answered.  I will probably do a second post for the questions I am competent to answer that did not get asked.

Anyway, here goes:

1        Give us a short summary of things that keep you up at night and worry you in today’s markets.

Too Many Par Claims versus Sub-Par Assets

2        How big of an impact do you see the unwinding of QE having on the US and global economy?  In the event of inflation, how will markets react?

Easy in, Hard out

3        Give us some insight on how you behaviorally reduce the impact that a volatile market has on your investing strategy?

The Portfolio Rules Work Together Rules 7 & 8 are particularly important for knowing when to sell.

4        Provide some tips to young investors starting out looking for both career and investment advice.

How Do I Find a Job in Finance?

How Do I Find a Job in Finance? (Part 2)

5        Should the current monetary policy of increasing the money supply be continued?

No. We should take losses and let the system reset.  Get the government out of the macroeconomics business.

http://alephblog.com/?s=Queasing

6        Do you believe that High Frequency trading helps add liquidity in the market or that it distorts the market.

23,401 Auctions

391 Auctions

Other useful stuff that we discussed:

Buffett’s Career in Less Than 1000 Words

How to Become Super-Rich?

Hit the “Defer” Button, Thanks…

Winding Down the Eurozone

Aim for the Middle

That’s all for now.  I will follow this up, answering most of the questions not asked at the Towson University Investment Group’s International Market Summit.

More to come…

The following was published on 6/1/2004 at RealMoney.com

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Investing Strategies

 

Models that work well on data about the past may not work in the future.

Check methods for weak points, like overfitting or ignoring illiquidity or business relationships.

Keep in mind some practical considerations when testing a theory.

Other Areas of Data-Mining

In 1992-1993, there were a number of bright investors who had “picked the lock” of the residential mortgage-backed securities market. Many of them had estimated complex multifactor relationships that allowed them to estimate the likely amount of mortgage prepayment within mortgage pools.

Armed with that knowledge, they bought some of the riskiest securities backed by portions of the cash flows from the pools. They probably estimated the past relationships properly, but the models failed when no-cost prepayment became common, and failed again when the Federal Reserve raised rates aggressively in 1994. The failures were astounding: David Askin’s hedge funds, Orange County, the funds at Piper Jaffray that Worth Bruntjen managed, some small life insurers, etc. If that wasn’t enough, there were many major financial institutions that dropped billions on this trade without failing.

What’s the lesson? Models that worked well in the past might not work so well in the future, particularly at high degrees of leverage. Small deviations from what made the relationship work in the past can be amplified by leverage into huge disasters.

I recommend Victor Niederhoffer and Laurel Kenner’s book, Practical Speculation, because the first half of the book is very good at debunking data-mining. But it also mines data on occasion. In Chapter 9, for example, the authors test methods to improve on buying and holding the index over long periods by adjusting position sizes based off of the results of prior years. Enough results were tested that it was likely that one of them might show something that would have worked in the past. My guess is that the significant results there are a statistical fluke and may not work in the future. The results did not work in the recent 2000-2002 downturn.

As an aside, one of the reasons Niederhoffer’s hedge fund blew up is that he placed too much trust in the idea that the data could tell him what events could not happen. The market has a funny way of doing what everyone “knows” it can’t, particularly when a majority of market participants rely on an event not happening. In this case, Niederhoffer knew that when U.S. banks fall by 90% in price and survive, typically they are a good value. Applying that same insight to banks in Thailand demanded too much of the data, and was fatal to his funds.

What to Watch Out for

Investors who are aware of data-mining and its dangers can spot trouble when they review quantitative analyses by looking for these seven signals:

1. Small changes in method lead to big changes in results. In these cases, the method has likely been too highly optimized. It may have achieved good results in the past through overfitting the model, which would interpret some of the noise of the past as a signal to return to the earlier analogy.

2. Good modeling takes into account the illiquidity of certain sectors of the market. Any method that comes out with a result that indicates you should invest a large percentage of money in a small asset class or small stock should be questioned. Illiquid or esoteric assets should be modeled with a liquidity penalty for investment. They can’t be traded, except at a high cost.

3. Be careful of models that force frequent trading, particularly if they ignore commission costs, bid/ask spreads, and, if you are large enough relative to the market, market impact costs. These factors make up a large portion of what is called implementation shortfall. In general, implementation shortfall often eats up half of the excess returns predicted by back-testing, even when back-testing is done with an eye to avoiding data-mining.

For a full description on the pitfalls of implementation shortfall, read Investing by the Numbers, by Jarrod X. Wilcox.  Chapter 10 discusses this issue in detail. This is the best single book I know of on quantitative methods in investing.

4. Be careful when a method uses a huge number of screens in order to come down to a tiny number of stocks and then, with little or no further analysis, says these are the ones to buy or sell. Though the method may have worked very well in the past, accounting data are, by their very nature, approximate and manipulable; they require further processing in order to be useful. Screening only winnows down the universe of stocks to a number small enough for security analysis to begin. It can never be a substitute for security analysis.

5. Avoid using quantitative methods that lack a rational business explanation. Effective quantitative methods usually come from processes that mimic the actions of intelligent businessmen. Never confuse correlation with causation. Sometimes two economic variables with little obvious financial relationship to each other will show a statistically significant relationship in the past. Two financials merely being correlated in the past does not mean that they will be so in the future. This is particularly true when there is no business reason that relates them.

6. Look for the use of a control. A control is a portion of the data series not used to estimate the relationship. It’s left to the side to test the relationship after the “best” model is chosen. Often, the control will indicate that the “best” method isn’t all that good. And beware of methods that use the control data multiple times in order to test the best methods. That defeats the purpose of a control by data-mining the control sample.

7. One of the trends in accounting is to make increasingly detailed rules in an attempt (wrongheaded) to fit each individual company more precisely. The problem with that is it makes many ratios difficult to compare across companies and industries without extra massaging to make the data comparable. This makes thinning out a stock universe via screening to be less useful as a tool. For quantitative analysis to succeed, the data need to represent the same thing across different firms.

Practical Recommendations

There are many pitfalls in quantitative analysis. But three simple considerations will help protect investors from the dangers of data-mining.

1. Paper trade any new quantitative method that you consider using. Be sure to charge yourself reasonable commissions, and take into account the bid/ask spread. Take into account market impact costs if you are trading in a particularly illiquid market. Even after all this, remember that your real-world results often will underperform the model.

2. Think in terms of sustainable competitive advantage. What are you bringing to the process that is not easily replicable? How does the method allow you to use your business judgment? Is the method so commonly used that even if it is a good model, returns still might be meager? Even good methods can be overused.

3. If doing quantitative analysis, do it honestly and competently. Form your theory before looking at the data and then test your theory. Then, if the method is a good one, apply the results to your control. If you perform quantitative analysis this way, you will have fewer methods that seem to work, but the ones that pass this regimen should be more reliable.

The following was published at RealMoney on 5/28/2004:

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Investing Strategies

 

Data-mining attempts to get data to give a sharp answer when one may not be present.

Technical analysis can involve data-mining.

Chance can make a method look better than it is.

Investors often get pitched quantitative methods for investing. These methods can be either fundamental or technical in nature and often have shown great results on a pro forma basis in the past, but when ordinary investors (and often, professional investors) try them out, they don’t work as well in practice. Why?

There are many reasons, but in my opinion, there’s one main reason: data-mining. I’ll define data-mining and give you practical ways to avoid it whether you apply quantitative methods or create new quantitative investment methods.

Data-Mining Defined

I never got my doctorate, but I did complete my field in econometrics in grad school. One of the things that they drilled into us was the danger of overinterpreting your data. As a mythical economist supposedly once said, “If I torture the data enough, I can make it confess to anything.”

When a quantitative analyst mines data, he repeatedly tests new hypotheses against the same data set. When the analyst finds an economically or statistically significant relationship, he stops testing alternative hypotheses. He may start to optimize the hypothesis that gave a significant result.

Data-mining, or as some call it, specification searching, attempts to get the data to give a sharp answer when no sharp answer may be present. Financial data are messy; there is a lot of noise and often not much signal. Every time data get analyzed, there is a small but significant probability that noise in the data will be interpreted as a signal.  Overinterpreting the data increases the odds that what the analyst thought was signal was actually noise.

Examples of Data-Mining

As examples, consider Michael O’Higgins’ Beating the Dow, which introduced and popularized his “Dogs of the Dow” theory, or James P. O’Shaughnessy’s What Works on Wall Street. In each of these books, different hypotheses were tweaked to find a method that would have produced the best result in the past.

The basic idea underlying the “Dogs of the Dow” theory has merit: Buy cheap, large-cap stocks. But in testing multiple theories, the cheapness metric was varied. Which is the best: low price-to-book, earnings, sales, cash flow, low price or dividend yield? Another factor that varied was which stocks would be picked. Would it be the top 10, top five, top one or even the second-best? How often would the strategy get rebalanced: annually, quarterly, or monthly?  With this many permutations, the strategy that ended up performing best likely did so accidentally.

What Works on Wall Street also contained some good core ideas (although it was a bit misnamed; it should have been titled, What Has Worked on Wall Street, but that would not have sold as well). Its core theory: Buy cheap stocks that have positive price and earnings momentum. But in this theory, the cheapness metric also varied, along with the methods for analyzing momentum — enough that more than 50 different theories got tested. The basic idea is sound, but again, the variation with the best result won only by accident.

… And Technical Analysis

Bloomberg has a back-testing technical analysis function [BTST]. It takes eight different technical analysis methods and shows how each would have performed in the past for a given security. Even if some of the methods had validity, if an analyst fed the BTST function a stream of random data instead of a real price series, the function would likely flag one of the methods as profitable.

Another area where I have seen abuse is in “services” that offer to identify “rolling stocks,” i.e., stocks that seem to oscillate between two predictable boundaries. This gives the potential for an investor to make quick and easy profits by buying at the low boundary and selling at the high boundary. The trouble here is that it is easy to identify stocks that have traveled in boundaries in the past, but the past is usually a poor predictor of the future. Results from following advice like this should be random at best, with the danger that your losses could increase if the conditions that created the temporary stability shift.

Data-Mining in Modern Portfolio Theory

Why do stocks always seem to do better than bonds in the long run? How much better should they be expected to do? These questions frame what is called the Equity Premium Puzzle. Academics who use data-mining assume that past is prologue and that initial valuation levels have no impact on the results for their forecast period. Back in 1999, I often commented that since 1926, we’d seen only one and a half full cycles of the equity markets. Naive estimates of the equity premium were popular among academics and practitioners then. We had not seen a second major bear market like that of the 1930s. The bear market of 2000-2002 has adjusted my view, but I am not convinced that valuation levels have returned to normal.

There are many societal and political factors that affect how much better stocks will do than bonds. People do not have infinite investment horizons; they will need at least some of the money at some point in their lives, so long-term total return averages are not indicative of what average investors are likely to achieve. Valuations matter, as do the current yields of bonds. Neglecting equity valuations and bond yields when doing asset allocation work will lead asset allocators to overweight stocks and bonds, which have done well historically but are unlikely to do as well over the next 10 years as the historic averages.

In a past job, I was a quantitative analyst for an asset manager that had a life insurance company as a client. There were a variety of derivative investments that got pitched to us that used diversification of different credit risks as a means for reducing risk. Often I would be shown a correlation matrix of past returns that showed high reductions in volatility from mixing different risky asset classes. I would ask the quantitative analysts on the sell side how stable the correlation matrix was, given how highly correlated most risky fixed-income asset classes were in 1998 during the Long Term Capital Management crisis, and afterward in the recovery. Most of the time, they hadn’t considered the question.

A Big Warning Sign

Anytime you see an analysis that relies on a correlation matrix of returns through some sort of mean-variance framework, be careful. My favorite target here tends to be a fund-of-funds, whether of the CTA, hedge, or mutual fund variety. There are several reasons for that.

First, there usually aren’t enough data to estimate the correlation matrix. Inexperienced practitioners do  so anyway, without realizing that they need at minimum, one data period for each unique correlation coefficient that they calculate. For example, for a correlation matrix of 10 return series, you would need at least 46 periods for the data, and really, you would want more than 70 to gain sufficient statistical credibility on a historical level.

Second, even if there are enough data to calculate correlation coefficients that are statistically credible, the financial processes that produce the correlation coefficients aren’t stable. Past correlation coefficients are poor predictors of future correlation.

Third, “past performance may not be indicative of future returns.” This is not only true of the level of returns, but also the variation of returns. It should not surprise anyone, then, that ratios of historical average return to the variability of return aren’t good predictors of the future ability of a manager to obtain returns with low variability of results. In short, Sharpe ratios (or reward-to-variability ratios) are, in my opinion, poor predictors of the ability of a manager or assets class to produce return and mitigate risk. Efficient frontier analyses draw pretty pictures, but they usually do not produce asset allocations that optimize the future risk/return tradeoff when the parameters are estimated from historical data.

Another data-mining villain is returns-based style analysis, which assumes that a manager’s true style can be discerned from the correlations of his returns with a variety of different asset class indices. Leaving aside the problems of multicollinearity and inability to develop confidence intervals on the constrained regression, the use of short historical data series might give a clear view of the past, but it is poor when used to predict how a manager will perform in the future. In short, the past correlations are poor for predicting future returns.

With academic financial research, it is good to remember that only the survivors get published, and surviving requires statistical or economic significance, either of which can occur for reasons of structure or chance. Data-mining allows marginal academics an opportunity to publish.

In the second part of this column, I will review some practical ways to assess quantitative methods and sidestep data-mining.