Category: Classic

Classic: Two Ways to Analyze Corporate Bonds

Classic: Two Ways to Analyze Corporate Bonds

Posted on by David Merkel

This was written on July 16, 2004. I republish it now because it cannot be found on RealMoney’s website.? If you subscribe to RealMoney, demand that you can see my old posts.

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Tighter corporate spreads imply stronger profits and free cash flow at debt-issuing corporations. But there is another factor at play here that is less known outside of the corporate market.

There are two distinctly different ways to analyze corporate bonds. The first way is the old standard, which relies on fundamental analysis of a company’s financial statements. The second way relies on contingent claims theory (options theory, Merton’s model) and relies primarily on market-oriented variables, such as stock prices and option volatility.

The basic idea behind the latter method is that the unsecured debt of a firm can be viewed as having sold a put option to the equity owners. In an insolvency, the most the equity owners can lose is their investment. The unsecured bondholders (in a simple two asset class capital structure) are the new “de facto” equity holders of the firm. That equity interest is most often worth far less than the original debt. Recoveries are usually 40% or so of the original principal.

Under contingent claims theory, spreads should narrow when equity prices rise, and when implied volatility of equity options falls. Both of these make the implied put option of the equity holders less valuable. Equity holders do not want to give the bondholders a firm that is worth more, or more stable.

So what’s the point? Over the last seven years, more and more managers of corporate credit risk use contingent claims models. Some use them exclusively, others use them in tandem with traditional models. They have a big enough influence on the corporate bond market that they often drive the level of spreads. Because of this, the decline in implied volatility for the indices and individual companies has been a major factor in the spread compression that has gone on. I would say that the decline in implied volatility, and deleveraging, has had a larger impact than improving profitability on spreads.

Classic: The Long and Short of Trend Investing

Classic: The Long and Short of Trend Investing

Posted on by David Merkel

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.

Classic: Get to Know the Holders? Hands, Part 2

Classic: Get to Know the Holders? Hands, Part 2

Posted on by David Merkel

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.

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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.
Classic: Get to Know the Holders’ Hands, Part 1

Classic: Get to Know the Holders’ Hands, Part 1

Posted on by David Merkel

Note: this was published at RealMoney on 7/1/2004.? This was part three 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

Different investor groups in the market have different patterns of funding and disbursement.

Understand those patterns to read market action more clearly and see what trends might emerge.

 

Recently, the firm I work for held a large amount of the common stock of Phoenix (PNX:NYSE). As the stock rallied, I kept moving out my sell target, because the technicals on the stock were so compelling. There were no analysts saying buy, there were a few saying sell and the short interest was high. The company was doing all the right things and the stock had great price momentum, but the valuation was just too high. I wanted to sell, but I couldn’t figure out when.

Finally, on Feb. 21, the stock price began to rally on no news. Going to the message boards, I discovered that there was a momentum investor with a radio show who was making one of his occasional television appearances, and was touting Phoenix. I went to our trader and said that we had our chance. There was a group of valuation-insensitive buyers buying the stock with abandon. I said, “Ride the ask [offer stock at the asking price], and if you get any thick bids near the ask, hit them.” (Read: If there are aggressive large bidders, sell to them at their level.) We sold our position in two weeks, without disturbing the market; we were able to get an average price of about $14.25. (Our trader is top-notch.) Today the price of Phoenix is about 15% lower. The momentum investors choked on the stock that we (and others) fed them.

Why did this work for us? We understood two aspects of how Phoenix traded very well: the fundamentals and technicals. The fundamentals taught us what fair value should be, but the technicals taught us how investors would react to movements in the stock price.

Every investor has a mode of funding and a mode of disbursement. The funding and disbursement modes affect how long and under what conditions an investor wants to, or is able to, hold his position. Some examples will illustrate general principles of these modes. I will describe the ways that various classes of investors fund their investments, how their investments are held, how they are liquidated and how all of this affects what kinds of investments they can use from both an asset class and liquidity standpoint. I also will attempt to explain how the behavior of some classes of investors can become temporarily self-reinforcing, leading to booms and busts.? Finally, I will try to give some practical advice along the way as to how you can benefit from the behaviors of different classes of investors.

 

1. Banks and Other Depositary Institutions

Banks make promises to depositors. Some of these promises are absolute; some are contingent on external events. Bank regulations exist to make the keeping of the promises more certain (or, in modern times, keep the guarantee funds solvent). Banks have to keep adequate capital on hand to provide a margin of safety against insolvency. The amount of capital varies on the immediacy with which deposits may be withdrawn, the degree of equity/credit risk of the assets and how well the asset cash flows are matched to the liability cash flows.

Liquid assets must be set aside to meet the amount of funds that may be withdrawn immediately with little or no penalty. The more that is set aside, the lower the risk and the lower the profit. If the assets are materially longer or contain more equity risk than a money-market-like investment, there may be a loss when the assets are liquidated to pay off depositors. In general, the cash flows of assets must be matched to the liabilities that fund them, at least in aggregate.

This biases banks to hold primarily short- to intermediate-term, high-quality fixed-income assets: bonds, loans, mortgages and mortgage-backed securities. These are generally safe investments, but banks are fairly leveraged institutions. If the market moves against their investments and their capital cushion gets eroded to the point where their ability to operate becomes questionable to regulators (or customers), the banks might be forced to sell investments into a falling market in order to preserve solvency.

The first motive of a financial institution is to survive; the second is to profit. When the first motive is threatened, even if there is a good possibility that the institution will survive and make more money if it retains the assets that now are perceived as risky, in general, the risky assets will get sold to assure survival at the cost of current profitability.

To return to a concept I discussed in the first column I wrote for RealMoney, Valuing Financial Slack in the Steel Sector, banks with a high degree of leverage relative to the overall riskiness of their assets and liabilities possess little in the way of financial slack. Volatility in the markets that cuts against their position harms such companies. They end up becoming forced sellers and buyers.

Banks with financial slack can enjoy volatility. When the markets are dislocated, they can make room on their balance sheets to wave in securities that are distressed and temporarily trading below intrinsic value. During times of volatility, the strong benefit at the expense of the weak, whereas weak firms outperform during periods of stability. As an example, after the real estate crisis in 1989-1992, the banks that did the best over the whole cycle were those that did not become overleveraged, did not over-lend to marginal credits and had diversified operations. During the crisis, they had the flexibility to lend in situations of their choosing at favorable yields.

 

2. Insurance Companies

Insurance companies are like banks but generally have longer funding bases and typically run at a higher ratio of surplus to assets. Insurance companies typically have more ways to lose money than banks, and potential cash flow mismatches in the longer liability structure require more capital to fund potential losses. In principle, the higher surplus levels and the longer liabilities should allow for investment in longer-duration assets like equities, but the regulations make that difficult. Surplus is limited; what gets used for equities can’t be used for underwriting.

As a counterexample, consider what happened to the European insurance industry in 2002. European insurers are allowed to invest much more in equities than their U.S. counterparts can. (Berkshire Hathaway (BRK.A:NYSE) is an interesting exception here.) As the bull market of the 1990s came to an end, European insurers found themselves flush with surplus from years of excellent stock-market returns, and adequate, if declining, underwriting performance. The fat years had led to sloppiness in underwriting from 1997 to 2001.

During the bull market, many of the European insurers let their bets ride and did not significantly rebalance away from equities. Running asset policies that were, in hindsight, very aggressive, they came into a period from 2000 to 2002 that would qualify as the perfect storm: large underwriting losses, losses in the equity and corporate bond markets and rating agencies on the warpath, downgrading newly weak companies at a time when higher ratings would have helped cash flow. In mid-2002, their regulators delivered the coup de grace, ordering the European insurers to sell their now-depressed stocks and bonds into a falling market. Sell they did, buying safer bonds with the proceeds. Their forced selling put in the bottom of the stock and corporate bond markets in September and October of 2002. Investors with sufficient financial slack, like Warren Buffett, were able to wave in assets at bargain prices.

This principle may be articulated more broadly as, “The tightest constraint dominates investment policy.” As an example, an insurer that already was at a full allocation on junk bonds could not take advantage of the depressed levels in the junk bond markets; such investors were biting their nails, wondering if they would make it through alive. Another example occurred in 1994, when the most volatile residential mortgage bonds were blowing up. Insurance companies that had a full allocation to that class could not buy more when prices were at their most attractive. Companies and investors that rarely bought the “toxic waste” of the residential mortgage bond market began scooping up bonds at discounts unimaginable previously. A number of flexible investors, including St. Paul (now St. Paul Travelers) and Marty Whitman both ventured outside their ordinary investment habitats to benefit from the crisis.

 

3. Defined Benefit Pension Plan

Defined benefit plans need cash to fund payments to beneficiaries. The amount and timing of the benefit payments vary with plan demographics (sex, age and income), physical roughness of the industry and the specific plan provisions (e.g., late retirement, early retirement, etc.). Inflows to DB plans depend on funding levels and the financial health of the company sponsoring the plan. For an individual DB plan, the cash inflow and outflow characteristics will help determine the plan’s asset allocation, together with the risk tolerance of the plan sponsor.? The more risk-averse a plan is, the less capable it is of funding inflows, and the older the plan’s participant population, the larger the proportion of assets that will go into bonds and other safer investments.

For all DB plans in aggregate, though, the cash flow and demographic characteristics mirror those of the Old Economy. DB plans were created back in the days when the relationships between employer and employed were more fixed than they are now. In the current era of more short-lived employment relationships and with the average age of participants in DB plans rising, these plans face several challenges:

  1. Net cash outflows are getting closer.
  2. There are fewer cash inflows.
  3. Plans are being terminated (or converted to cash balance plans) due to cost, economic weakness and inflexibility.

DB plans are major holders of equity and debt in the U.S., but they are not as great a force as they once were.? Defined contribution plans (i.e. 401(k)s, 403(b)s, etc.) are bigger now. The relative decline and aging of DB plans has had, and will continue to have, two effects in the market. First, because of aging, there will be a greater relative demand for bonds. Second, DB plans have always had a long investment time horizon. That is shrinking now. DB plans tend to resist trends in the market; they tend to rebalance to a fixed asset allocation, which leads them to buy low and sell high. DB plans were the ones selling equities in March 2000 and buying in October 2002; their rebalancing strategies insured that. As DB plans become a smaller fraction of the investor base, markets will become more volatile due to the reduction in long-horizon capital in the market.

 

4. Endowments

Endowments plan to survive forever. Forever is a tough mandate.

Inflows to endowments are uncertain, and outflows are fairly constant. They have spending formulas, the most common of which has the charity spending a constant percentage each year, usually 4% to 6% of the endowment. (In the old days, say 10 years ago, most formulas allowed charities to spend income, which was defined as dividends plus net capital gains.) Within these constraints, endowments behave like defined benefit plans.

 

5. Mutual Funds

Mutual funds do not face any fixed funding or disbursement. Their flows come from retail money chasing past performance. Managers that do well face the blessing of attracting more funds, which they hope will not dilute their returns. Managers that do poorly have funds withdrawn from them, forcing them to liquidate investments that they otherwise think are promising. If a manager is a big enough investor in a given company’s stock (think of Janus’ concentrated portfolios), this can have the effect of worsening performance as liquidation goes on, or boosting the already good performance of managers that are receiving cash inflows to a concentrated fund.

These tendencies become more pronounced the better or worse that performance gets. When performance is near the median level, say, within the second and third quartiles, performance-driven fund flows are small. For many mutual fund managers, this gives them the incentive to never drift too far away from the benchmark, whether that is an equity index or an average portfolio of peers. There is safety in the pack, even if there might be more grass to eat further from the herd. It is rare for a mutual fund manager to be fired for being mediocre.

 

6. Index Funds

What is true of regular mutual funds is also true of index funds, but the difference between the two helps illuminate a basic idea on demographics. Aside from taking market share away from active managers, when do index funds receive and disburse funds? The answer lies mainly in the demographics of investors.

When investors are younger, they invest surplus cash. When they are older, particularly after retirement, they liquidate investments to generate cash. Given the demographics in the U.S., the excess return for merely belonging to the S&P 500 has been roughly 4% per year over the past 15 years; index funds have received disproportionate large inflows relative to the market as a whole. Aside from that, in aggregate, active equity managers benchmark to something that approximates the S&P 500. Belonging to the S&P 500 ensures a continuing flow of capital.

Or does it? What will happen near 2020, when aggregate investment behavior changes from saving to liquidation?? Belonging to major indices may not have the same cachet as investors liquidate their holdings to fund present needs. What was 4% positive in the 1990s could become 4% negative in the 2020s, absent a continuing move toward passive investing.

I don’t have a firm answer here, but I do have suspicions. I would be cautious of too much index exposure 15 years from now, to the extent it can be avoided. (And of course, this will be anticipated several years before the flows turn negative.)

 

7. Unleveraged Private Investors

Sometimes private investors feel disadvantaged vs. larger institutional players, but there are advantages that unleveraged private investors have that institutional players often don’t: the abilities to invest for the long term, concentrate and do nothing.

Institutional investors are subject to the tyranny of constant measurement because they manage money for others. As I have noted before, measurement affects how a manager invests, particularly when it might affect the amount of assets under management, or the receipt of incentive fees. This encourages managers to be both short-term in their orientation and more like an index. It also encourages hyperactivity; clients often expect a manager to make changes to the portfolio even when doing nothing could be the most prudent policy.

Unleveraged private investors can make aggressive investment decisions. They can concentrate their portfolios or consider more esoteric areas of the market. They also can back away from the market if they feel that opportunities are absent. Finally, they can buy and hold, which is not always an option for institutions. They can’t always ride out long but temporary dips in the price of an asset.

That an unleveraged private investor can do these things doesn’t mean he should. Using these advantages presumes a level of expertise in the market well in excess of the average investor. Most investors are average and should index. Those with skill should use it to their maximum advantage, realizing that they are taking their own financial life in their hands; the risks to such an approach are significant, but the same is true of the rewards.

Unleveraged private investors have needs for cash. Some will need it for college, retirement, a second home, etc.? The sooner that an investor will need to liquidate a significant portion of his portfolio, the more conservative the portfolio must be to achieve those spending goals. Looking at private investors in aggregate, this would mean that as the baby boomers approach and enter retirement, there might be a tendency for the overall willingness to take risk in the markets to decline. Also, once the baby boomers are in retirement, assets will have to be liquidated to support them, which will be a drag on the markets at that time.

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In the second part of this column, I will describe how the funding and disbursement modes of three more key groups of investors affect the market, \and how balance sheet players and total return players further mix up the market forces. I’ll also use the Long Term Capital Management crisis to illustrate how illiquidity can shape and shake the market.

Classic: Separating Weak Holders From the Strong

Classic: Separating Weak Holders From the Strong

Posted on by David Merkel

Note: this was published at RealMoney on 3/23/2004.? This was part two 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.? The copy I received has no links, so sorry for the lack of links.? I hope you enjoy the article.

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Stock Analysis

Watch how the stock reacts to news.

Examine the short side.

Pay attention to what insiders are doing.

 

A little more than a month ago, I wrote a column to help explain some of the differences between the market’s strong and weak hands, and I received quite a response.

It’s been a while since the story appeared, so here’s a chart to clarify some of the ideas I put forth in it.

?Table of Actions for Investors With Long Positions

Consider four classes of investors and how they behave with respect to market action

Market Action
Investor Action Stock price goes down Stock price goes up
More likely to hold Valuation-sensitive and strong holders Momentum investors and strong holders
More likely to sell Momentum? investors and weak holders Valuation-sensitive and weak holders

To put it another way:

  • Weak holders play for small gains and losses.
  • Strong holders play for big gains, will ride out big losses and sometimes get killed with the firm.
  • Momentum investors require liquidity from the market and exacerbate price moves.
  • Valuation-sensitive (or mean-reverting) investors provide liquidity to the market. They hold or buy more when prices decline, and they sell when prices rise enough to hit their valuation targets. This category describes my normal posture in the market.

These four descriptions here are ideal investor types. Some investors and institutions fit only one of them, but many use a mix in their investing activity.

After Part 1 of this piece appeared, the most common reader question was, “How do you identify whether a stock’s holders are weak or strong?” There’s no simple answer, but I can offer a bevy of techniques and tools that I use for this purpose. Some require a good deal of experience and judgment; beginners can use others easily. Here are some tips to get you started:

Assess how the stock reacts to news

Good news should make a stock go up, and bad news should make it go down. But we learn the most when the price reaction is different from what we expect. For example, if a stock refuses to go down much — or even rises — on significant bad news, then it has many strong holders. If it doesn’t go up much — or even falls — on significant good news, then it has many weak holders.

Examine the short side.

Short-sellers are typically weak antiholders of a stock. The percentage of the float that is shorted will tell you how much of the stock is subject to buyback if the price rises significantly.

Now, short-selling is a double-edged sword. Although short-sellers have an impulse to buy back into strength, high short interest usually indicates problems at the company. If you encounter a heavily shorted stock, take a close look to see whether it’s strictly a valuation issue or if something is fundamentally broken at the company on an accounting or operational basis. Short interest is available on Yahoo! Finance; here’s an example of a heavily shorted stock, Phoenix (PNX:NYSE) .

Find out who the large holders are.

The higher the proportion of stock held by insiders and long-term investors, the more strongly a stock’s holder base is. I track this by reviewing proxy statements as well as 13D and 13G filings, which are freely available at the SEC Web site.

These data require some judgment to interpret. First, I find out who holds more than 5% of the stock, because some of those large holders tell a story about the stock.

Most institutional investors will not take stakes of more than 5% in a corporation’s shares, as getting into and out of such large positions requires careful trading. Once they are above the 5% limit, changes in position size must be disclosed via 13D filings, which give away information to other traders who may trade against the large holder.? Large holders by their very nature tend to be strong holders; the costs of exiting a position are significant.

Look at insider activity.

Insiders, if they hold large positions, tend to be strong holders of a company’s shares. Additionally, they often have a clearer perspective on the company’s prospects. Insider buying can be a great indicator of potential value, particularly if the insider pays for the shares from his or her own pocket. Small insider holdings and holdings acquired for compensation are more likely to be cashed in when insiders need the proceeds. Insider data are freely available at Yahoo!; here is an example.

One exception needs to be understood regarding large insider holdings. If the holdings are so large that a single investor has discretionary control over the company, then it pays to review how that “control investor” has treated outside passive minority investors (folks like you and me) in the past. If he or she acts on self-interest to the detriment of smaller investors, then it’s time to look elsewhere.

Review proxy statements.

After spending enough time looking at such data, you begin to see what kinds of investors are among the large holders. Most tend to be value investors or long-term growth investors. After this, a list of the remaining institutional investors can be instructive. A limited view of this is freely available on Yahoo!; here’s an example.

This particular example tells me that the major holders are value investors and index managers. These are relatively strong holders of the stock; they don’t run away after minor disappointments. In general, growth investors tend to be weaker holders than value investors.

Take note of turnover.

To the extent that you can obtain turnover rate data, for example, in mutual fund prospectuses, that’s a good proxy for how weakly a manager holds stocks. Quantitative managers tend to be weak holders of securities; many of them try to profit from short-term mispricings of securities, often trading at very high turnover rates. Qualitative managers who are tightly benchmarked to indices, including many institutional managers who are scrutinized by the consultant community, can find themselves in the same boat.

Glance at the message boards.

Although there are exceptions — and this is squishy — the amount and shrillness of postings on message boards seems proportional to the weakness of the holder base. The more reasoned and slow the message board, the less speculative the stock’s retail holder base.

Gauge the volatility of the price action.

If market prices are more volatile compared with the factors that drive the stock’s underlying value, there are relatively more weak holders. If market prices are less volatile compared with the factors that drive the underlying value, there are relatively more strong holders.

Valuation also affects the holder base: The higher the valuations, the lower the proportion of valuation-sensitive investors in the holder base, and that tends to increase price volatility. The lower the valuations are, the lower the proportion of momentum investors in the holder base, and that tends to decrease price volatility.

Review chart action.

I’m not a technician, so bear with me. One simple question to ask is whether buyers or sellers are more motivated.? A simple way to answer that for the immediate past is to look at a money flow graph. Here is an example of a noncumulative money flow graph from Yahoo! The top part of the graph is the price; the bottom part is money flow. When the money flow figure is over 50, more trades have been occurring on upticks than on downticks. The opposite is true when money flow is below 50. Momentum investors dominate the buy side of trading when the money flow indicator is persistently high. Valuation-sensitive investors dominate the buy side of trading when the money flow indicator is persistently low.

As I’m not a technician, I won’t explore levels of support and resistance. I use those techniques, but I’ll leave it to the expert technical analysts to describe them in detail. Levels of support and resistance often indicate where valuation-sensitive investors are accumulating and selling shares.

What I promised at the end of Part 1 will have to wait for Part 3 of this series. In response to the questions I received, I’ll also cover the effect of dividends and weak holders in the Treasury bond market. If you have any questions, please feel free to email me.

Classic: The Fundamentals of Market Tops

Classic: The Fundamentals of Market Tops

Posted on by David Merkel

I wrote the following at RealMoney on 1/13/2004:

 

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Market Analysis

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Watch out for a momentum-driven investor base.

Companies will take advantage of a topping market by raising cash.

A top in the market is not imminent.

 

I am basically a fundamentalist in my investing methods, but I do see value in trying to gauge when markets are likely to make a top or bottom out. The methods that I will describe in this column are somewhat vague, but I always have believed that investment is a game that you win by being approximately right. Precision is of secondary importance.

At the end of this column, I will apply my reasoning to the current market to show what concerns exist and why there is reason for optimism.

The Investor Base Becomes Momentum-Driven

Valuation is rarely a sufficient reason to be long or short the market. Absurdity is like infinity. Twice infinity is still infinity. Twice absurd is still absurd. Absurd valuations, whether high or low, can become even more absurd if the expectations of market participants become momentum-based. Momentum investors do not care about valuation; they buy what is going up, and sell what is going down.

You’ll know a market top is probably coming when:

  1. The shorts already have been killed. You don’t hear about them anymore. There is general embarrassment over investments in short-only funds.
  2. Long-only managers are getting butchered for conservatism. In early 2000, we saw many eminent value investors give up around the same time. Julian Robertson, George Vanderheiden, Robert Sanborn, Gary Brinson and Stanley Druckenmiller all stepped down shortly before the market top.
  3. Valuation-sensitive investors who aren’t total-return driven because of a need to justify fees to outside investors accumulate cash. Warren Buffett is an example of this. When Buffett said that he “didn’t get tech,” he did not mean that he didn’t understand technology; he just couldn’t understand how technology companies would earn returns on equity justifying the capital employed on a sustainable basis.
  4. The recent past performance of growth managers tends to beat that of value managers. (I am using the terms growth and value in a classic sense here. Growth managers attempt to ascertain the future prospects of firms with little focus on valuation. Value managers attempt to calculate the value of a firm with less credit for future prospects.) In short, the future prospects of firms become the dominant means of setting market prices.
  5. Momentum strategies are self-reinforcing due to an abundance of momentum investors. Once momentum strategies become dominant in a market, the market behaves differently. Actual price volatility increases. Trends tend to maintain themselves over longer periods. Selloffs tend to be short and sharp.
  6. Markets driven by momentum favor inexperienced investors. My favorite way that this plays out is on CNBC. I gauge the age, experience and reasoning of the pundits. Near market tops, the pundits tend to be younger, newer and less rigorous. Experienced investors tend to have a greater regard for risk control, and believe in mean-reversion to a degree. Inexperienced investors tend to follow trends. They like to buy stocks that look like they are succeeding and sell those that look like they are failing.
  7. Defined benefit pension plans tend to be net sellers of stock. This happens as they rebalance their funds to their target weights.

Corporate Behavior

Corporations respond to signals that market participants give. Near market tops, capital is inexpensive, so companies take the opportunity to raise capital.

Here are ways that corporate behaviors change near a market top:

  1. The quality of IPOs declines, and the dollar amount increases. By quality, I mean companies that have a sustainable competitive advantage, and that can generate ROE in excess of cost of capital within a reasonable period.
  2. Venture capitalists can do no wrong, so lots of money is attracted to venture capital.
  3. Meeting the earnings number becomes paramount. What is ignored is balance sheet quality, cash flow from operations, etc.
  4. There is a high degree of visible and/or hidden leverage. Unusual securitization and financing techniques proliferate. Off balance sheet liabilities become very common.
  5. Cash flow proves insufficient to finance some speculative enterprises and some financial speculators. This occurs late in the game. When some speculative enterprises begin to run out of cash and can’t find anyone to finance them, they become insolvent. This leads to greater scrutiny and a sea change in attitudes for financing of speculative companies.
  6. Elements of accounting seem compromised. Large amounts of earnings stem from accruals rather than cash flow from operations.
  7. Dividends become less common. Fewer companies pay dividends, and dividends make up a smaller fraction of earnings or free cash flow.

In short, cash is the lifeblood of business. During speculative times, watch it like a hawk. No array of accrual entries can ever provide quite the same certainty as cash and other highly liquid assets in a crisis.

Other Gauges

These two factors are more macro than the investor base or corporate behavior but are just as important.

Near a top, the following tends to happen:

  1. Implied volatility is low and actual volatility is high. When there are many momentum investors in a market, prices get more volatile. At the same time, there can be less demand for hedging via put options, because the market has an aura of inevitability.
  2. The Federal Reserve withdraws liquidity from the system. The rate of expansion of the Fed’s balance sheet slows. This causes short interest rates to rise, making financing more expensive. As this slows down the economy, speculative ventures get hit hardest. Remember that monetary policy works with a six- to 18-month lag; also, this indicator works in reverse when the Fed adds liquidity to the system.

One final note about my indicators: I have found that different indicators work for market bottoms and tops, so don’t blindly apply these in reverse to try to gauge bottoms.

No Top Now

There are reasons for concern in the present environment. Valuations are getting stretched in some parts of the market. Debt capital is cheap today. There are an increasing number of momentum investors in the market. Making the earnings estimate is once again of high importance. Nonetheless, a top in the market is not imminent, for these reasons:

  • The Fed is on hold for now. Liquidity is ample, perhaps too much so.
  • Actual price volatility is muted.
  • Since all of the accounting scandals of the last few years, many corporations have cleaned up their accounting and become more conservative.
  • Cash flow from operations comprises a high proportion of current earnings. More dividends are getting paid.
  • Leverage has not declined, but most corporations have succeeded in refinancing themselves in a low interest rate environment.
  • Conservative asset managers have not been fired yet.
  • Most IPOs don’t seem outlandish.

Not all of the indicators that I put forth have to appear for there to be a market top. A preponderance of them appearing would make me concerned, and that is not the case now.

Some of my indicators are vague and require subjective judgment. But they’re better than nothing, and kept me out of the trouble in 1999 and 2000. I hope that I — and you — can achieve the same with them as we near the next top.

The current market environment is not as favorable as it was a year ago, but there are still some reasonably valued companies with seemingly clean accounting to buy at present. Right now, being long the market is more compelling to me than being flat, much less short.

Classic: Avoid the Dangers of Data-Mining, Part 2

Classic: Avoid the Dangers of Data-Mining, Part 2

Posted on by David Merkel

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

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Investing Strategies

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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.

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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.

Classic: Avoid the Dangers of Data-Mining, Part 1

Classic: Avoid the Dangers of Data-Mining, Part 1

Posted on by David Merkel

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

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Investing Strategies

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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.

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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.

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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.

Classic: Using Investment Advice, Part 4 [Tread Warily on Media Stock Tips]

Classic: Using Investment Advice, Part 4 [Tread Warily on Media Stock Tips]

Posted on by David Merkel

The following was published at RealMoney on 9/26/05.? I have augmented it at the bottom, so if you’ve read it before, at the bottom, there is more.

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Often investors, both professional and amateur, will run across what seems like a great investment idea in the media and run to act on it. My advice is simple: Wait. For months, perhaps.

I’ll lay out my approach to media touts, as well as a list of current stock tips, later on. But first, let’s see how the market reacts to them.

Say that the idea is to go long on a stock. At the market open after the story appears, a rush of orders will push the stock’s price higher. Then, as the day progresses, the stock will drop and end the day lower than at the open, but usually higher than the prior close.

For the first few days, the market responds to the supply/demand imbalance, and then the merits of the investment become clear. As Benjamin Graham observed, in the short run, the market is a voting machine; in the long run, it’s a weighing machine.

My experience has been that after the initial supply/demand imbalance period, the performance of media-touted investments is market-like on average, leaving the early buyers with assets that generally underperform.

The degree of underperformance varies with the size and character of the audience that saw the story. In general, the larger the audience, the larger the reaction.

The reaction also tends to be larger the lower the experience level of the audience (as long as there is some investment experience — people with no experience won’t do anything). Novice investors are the ones that jump at ideas that seem to be hot when under the media spotlight. Experienced investors tend to have their own idea-generation processes; they either ignore the idea or throw it into their process for later review.

Naturally, the bigger the media play, the bigger the splash. A front-page article makes waves; a tidbit mentioned in passing should have no impact, even though it might be powerful information in the hands of an informed investor. The impact is also greater depending on the fame, or perceived skill, of the source.

The potential size of the investment is negatively related to the degree of underperformance. A positive article on General Electric will have less impact on the price of GE than a similarly positive article on a smaller company. Naive investors place their market buy orders without thinking through the degree of liquidity of the investment.

Know Your Enemies

A number of media sources are particularly given to sensationalism, such as newsletters, online message boards, radio and sometimes television. The risk is particularly great when the “expert” speaking has an ill-defined financial interest in the idea under discussion.

The higher the level of emotion employed, the lower the level of humility, and the less the focus on what could go wrong, the more you should be skeptical. The adviser can sometimes be an enemy of wealth creation.

There are other enemies as well: sophisticated traders who watch for unusual trading activity off of media play and take a short-term contrary position. They short into bullish news and buy bearish news when they perceive that the money acting quickly on it is naive.

What to Do

My advice is simple: Wait. Invest in a subset of the ideas that still have value and have not fully reacted to the information after a period of time.

Also, compare new ideas as a group vs. each other and against the existing assets in your portfolio. Only add a new idea if you think it will beat the median idea in your portfolio. I have detailed these ideas in a piece titled “Become a Smarter Seller.” [DM in 2013: wish I had a link, it was a great piece.] I usually wait one to three months after I get an externally generated idea before I consider acting on it. I rank new ideas against my current portfolio and choose new ideas based on a mosaic of different factors — mainly cheapness, momentum (or anti-momentum) and industry exposure. I consider selling positions more expensive than the current median idea in my portfolio, and buying ideas that are cheaper than the current median. The following decision/reaction grid helps explain my actions:

 

Decision/Reaction Grid Merit of the idea still good? Merit of the idea bad?
Results have already occurred. Can’t kiss them all. Glad I missed that bad boy.
Results have not occurred yet. Invest. Don?t invest.

 

There is a cost to waiting: Some ideas get away from you. This is called implementation shortfall by some. I say you can’t kiss them all.

However, waiting has the positive effect that with the passage of time, some investment proposals are proved wrong. Missing wrong ideas is a real benefit for any investment program. ?Also, waiting takes some of the emotion out of the decision-making process, which helps to avoid errors.

After the waiting period, I ask whether the underlying investment thesis is still valid and whether that is reflected in the current stock price. The media piece that generated the initial interest is long since forgotten, so the emotion and excess stock price moves are gone. But the value might still be there, and with enough new investment ideas, some of them will present real opportunities for above-average investment returns.

Back to 2013

In 2005, I closed the piece with a list of stocks that were interesting, but that I did not own at present.? Look for my next ?Industry Ranks? piece in late April or early May.? You will get some ideas there.

One more thing to confess, I wrote this series with Cramer in mind, but not only Cramer.? I cringe when I hear people speaking or writing about specific investments with a high degree of certainty.

Investing is not certain, even for those of us who try to invest with a margin of safety.? The proper sense of investing engenders sobriety and caution.? That is the opposite of what sells newsletters, gets listeners on the radio, and viewers on television.

I?ve been invited onto TV three times more than I have been on TV.? In talking with a producer, I will explain the issues involved, and I will tell him they are complex.? This doesn?t make for good soundbites.? The producer either concludes there is no easy story here, or seeks out someone who will make the show snappy.

I leave you with this simple concept: if it is entertaining, it is probably not useful for investing.? (And as an aside, that is why you will not see a word related to entertain in my disclaimer.? I am offering opinions, not advice.)? Truly that?s all anyone in the markets can do, but because so many people dupe the credulous, of which there is one born every minute, that?s why we have extensive regulations for disclosure and advertising.

Be skeptical. Research, and be a buyer.? Do not let yourself be sold to.

Finally, avoid emotive media regarding investing.? Listen to those who write dispassionately or better, learn, and do your own research.

Classic: Using Investment Advice, Part 3

Classic: Using Investment Advice, Part 3

Posted on by David Merkel

The following was published on 3/29/2004:

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Investment Advice

Time horizon usually correlates with return size.

It’s good to have signposts as the investment plays out.

Free advice is seldom cheap.

 

In analyzing any advice, investors have to consider the adviser, personal character issues and the nature of the investment proposed.

In Part 1 of this three-part column, I focused on the adviser. In Part 2, I looked at issues centering on your personal character.

In Part 3 today, the emphasis shifts to the investment itself.

Many Things to Consider

Good investment recommendations give some idea of how much to play for and the likelihood of getting there, even if the appraisal of likelihood is subjective and squishy. Are we looking to scalp a dime, a buck, 10%, 100%, or are we looking to score the elusive ten-bagger?

Most often, the time horizon of an investment corresponds to the amount targeted to be earned. Under normal circumstances, gains are made a little at a time. Bigger gains ordinarily take more time. How long will it take to earn what is expected from the proposed investment?

What risks exist in realizing the value inherent in the investment? What could go wrong? Nothing is certain in investing, so beware of advice that tries to sell hard on the idea of safety. Appeals to safety, particularly with investments that are touted to earn an above-average return, are often dangerous. The price adjustments with supposedly safe investments that disappoint are sometimes severe. I experienced this firsthand with corporate bonds: The most dangerous bond was the one everyone knew was secure, and then accounting irregularities popped up. The price would drop 10% to 20%, and liquidity would drop to nil.

If the investment is going properly, what signposts will you see to validate that the investment idea is on track? Aside from price action, what will yield clues that the investment thesis is wrong or right? What should earnings look like? When is that new product going to be introduced?

What factors in the macroeconomic environment does the investment rely on? If inflation rises, what will happen? Does this investment resist recessions well? If the market falls, will this investment fall harder?

Finally, how well does this investment fit into your portfolio? Does it reduce risk for you, or increase it?? Too much of a good thing can be wonderful, but the more concentrated your bets become, the closer you must watch your positions. The higher the degree of concentration in a portfolio, the higher the amount of expertise relative to the market the portfolio manager must possess.

No one will give you all of this in advice, but these are things to keep in mind to aid in the evaluation of advice that comes your way. In general, a conservative and skeptical posture will serve you best. Keep a tight hand on your wallet, and remember that those who stay in the game the longest often do the best.

Finally, you can remember Ferengi Rule of Acquisition No. 59: “Free advice is seldom cheap.”

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