I was thinking about this in relation to this article about customer service in the retail world from PandoDaily.

There is simply no such thing as a shortcut when it comes to customer service. You can provide an alternate service, if you don’t want to invest in a local call center of friendly competent people armed with helpful databases of customer information. But don’t call this customer service, because it isn’t. To call a person reading from a script a customer-service representative is like calling a middle school play Broadway. You might as well not have an 800 number.

Zappos, GoDaddy, Qualtrics and Braintree have proven that spending money on customer service isn’t throwing money away — it’s investing in the business. Done well, good customer service is the difference between a mediocre business and a great one. You can get shoes anywhere, and Zappos’ site design has never been that amazing; its entire success is wrapped up in treating people well. GoDaddy doesn’t view its call center as a “cost center,” arguing it has actually generated more than $100 million in annual revenues.

If anything, client service is even more important in wealth management because the product itself is intangible.  How can you put a price on financial security and peace of mind?  And, as GoDaddy shows, good client service can generate revenues, not just add to costs.  People come first.

This particular article appeared August 17, 2010.  The market had just gone through a near 20% decline, as well as the flash crash a few months before.  Here is the front end of the article:

Yes, it’s going to get worse, a whole lot worse … Bill Gross warns this is the “New Normal. Forget 10% returns. Think 5%”. … Economist Larry Kotlikoff, author of The Coming Generational Storm, warns: “Let’s get real. The U.S. is bankrupt. Neither spending nor taxing will help the country pay its bills” … Economist Peter Morici warns: “Unemployment is stuck near 10%. Deflation coming. Stock market threatens collapse. The Federal Reserve and Barack Obama are out of bullets. Near zero federal funds rates, central bank purchases, a $1.6 trillion deficit have failed to revive the economy.” … Simon Johnson, co-author of 13 Bankers, warns: “We came close to another Great Depression, next time we may not be so lucky.” Why? Because Wall Street’s already well into the next bubble/bust cycle — the “doom cycle.”

The doom cycle sounds pretty bad and we are warned that things are going to get a whole lot worse.  I’m not exaggerating.  The whole paragraph was in heavy bold type.

Since then, we’ve gone through another 20% correction.  And the market is more than 25% higher.  Yes, higher.

Before you smirk and think you are immune from getting carried away, think again.  We are all susceptible to emotion—it’s just part of our wiring.  And it’s not just on the downside.  It’s equally easy to get carried away with “new era” thinking on the upside.

Sentiment swings, I think, demonstrate one of the very best reasons to use a systematic investment process.  Our happens to be an adaptive one driven by relative strength, but I’m sure other styles could also be successful.  The important thing is to define a profitable process and then stick to it through thick and thin.

By changing the way you spread out your stock holdings, you can reduce risk and boost returns—even in a highly correlated market like today’s.

The trick? A concept known as “factor investing,” which originated in academia two decades ago and now is finding favor among institutional investors and high-end financial advisers.

Factor investing replaces traditional asset allocation—such as a portfolio with 30% in U.S. stocks, 20% in developed international markets, 10% in emerging markets and 40% in bonds—by focusing on specific attributes that researchers say drive returns. These “risk factors” include the familiar—like small versus large-size companies or growth versus value stocks—as well as more esoteric measures such as volatility, momentum, dividend yield, economic sensitivity and the health of a company’s balance sheet.

As a reader of this blog, you’re probably already familiar with factor investing through relative strength—something that academics call momentum.  Using factors rather than style boxes has some advantages.

“There are a lot of nuances you may be missing by focusing only on style and size,” says Savita Subramanian, head of equity and quantitative strategy at BofA Merrill Lynch Global Research. “You may be missing a whole layer of outperformance you could have gotten.”

Some fairly high-end investors are converting portfolios to focus on factor exposures.  By converting to factor exposure, investors are trying to drill down to the actual return drivers.

Big investors are taking heed. In 2009, researchers assigned to analyze the Norwegian Government Pension Fund recommended it reorient its portfolio around risk factors. And the California Public Employees’ Retirement System underwent a similar change in approach in 2010.

After 2008, big investors discovered that they had factor exposure anyway—it was just exposure they were not aware of and hadn’t controlled.  There’s a lot less potential for surprise if the factor exposures are constructed deliberately!

New products are becoming available to feed the demand for factor exposure as well.

Until recently, it was hard for small investors to dabble in factor investing. But that is changing.

In the past year at least six firms—BlackRock’s iShares, Russell Investments, Invesco PowerShares, Factor Advisors, QuantShares and State Street Global Advisors—have launched factor-based exchange-traded funds, or have filed paperwork to do so.

Of course, overlooked among the rush of big firms racing to create factor exposure is the grand-daddy of relative strength, the Powershares DWA Technical Leaders Index (PDP).  It’s actually been around more than five years and has performed nicely over that time, beating the S&P 500 despite a market environment that has been hostile to relative strength strategies.  (We’re looking forward to seeing how it performs in a better RS market!)

One of the big advantages of factor exposure is that some factors offset one another beautifully.  We’ve written before about the nice efficient frontier that is created by combining relative strength and low volatility.  (You can see the chart below.)  These factors work well together because the excess returns are uncorrelated.

Source: Dorsey Wright    (click to enlarge to full size)

In short, there’s more to portfolio construction than asset allocation and style boxes.  Factor exposure should be considered as well if the result is a better portfolio for the client.

See www.powershares.com for more information about PDP.  Past performance is no guarantee of future returns.  A list of all holdings for the trailing 12 months is available upon request.

One of the charts, in particular, struck me.  It was a chart of the S&P 500 real return.  It shows how far in time and distance we are from the all-time index highs, as well as what has happened in past declines.

Source: The Big Picture/ The Chart Store    (click to enlarge)

The real take-away here is that nominal returns can be quite deceptive.  Just because the dollar amount on your statement keeps growing does not mean your purchasing power has been maintained.  And your wait for real returns may be measured in decades!

It also suggests that it is important to look across a broad group of assets to try to capture returns wherever they are.  Investing in stocks has the possibility of augmenting your purchasing power greatly, but there are also long, long periods where market indexes have remained stagnant.  Plenty of individual stocks may have done well, but it’s also possible that the best opportunities were in asset classes outside of equities.  A realistic investment policy will pursue returns wherever they are available.

In an often cynical world, standard financial and macroeconomic quantitative models give people the benefit of the doubt. Fundamental economic theory assumes the best of us, supposing that human beings are perfectly rational, know all the facts of a given situation, understand the risks, and optimize our behavior and portfolios accordingly. Reality, of course, is quite different. While a significant portion of individual and market behavior can be modeled reasonably well, the human emotions that drive cycles of fear and greed are not predictable and can often defy historical precedent.

Economic historian Charles Kindleberger can offer some insight. In his book Manias, Panics, and Crashes, Kindleberger explores the anatomy of a typical financial crisis and provides a framework that considers the impact of the powerful human dynamics of fear and greed. Economic historian Charles Kindleberger can offer some insight. In his book Manias, Panics, and Crashes, Kindleberger explores the anatomy of a typical financial crisis and provides a framework that considers the impact of the powerful human dynamics of fear and greed.

Kindleberger famously dubbed this sequence a “hardy perennial,” probably because the galvanizing human conditions of fear and greed are more often than not prone to overshoot fundamental values compared to the behavior of a rational individual, which exists only in macroeconomic theory.

Loomis Sayles contends that Kindleberger provides the qualitative framework for Hyman Minsky’s pioneering work on boom and bust cycles.  Their graphic is remarkable in its simplicity and explanatory power—and in its distance from traditional economic equilibrium models.  (You can see the image in the article.)

The cycles that Loomis Sayles discusses are driven by behavior, and often not behavior that would be considered ”rational” in the classic economic sense.  Relying on precedent—the last time that happened, this happened—may or may not work.  In fact, each time there is a paradigm shift, precedent will fail.  Overshoots can be significant, so it’s important that an investing approach be adaptive enough to reflect changes in the environment.  Most importantly, investing needs to take human behavior into account.  Asset prices are a reflection of that behavior, suggesting that paying attention to prices may be far more useful than paying attention to economic theory.

• Price represents the intersection of supply and demand.  It is the point at which buyers and sellers can agree on a value at which to exchange.  Everyone in the financial markets knows this.  Even economists get it.
• Price also stimulates demand.  This price function is largely overlooked in the financial markets, but is the very reason why investors pile into strong stocks or strong assets.  Relative strength can obviously be a great help in identifying what those strong assets are.  Economists don’t understand how higher prices can drive demand, and just think investors are irrational.

If you are not convinced, here’s an excerpt from a Bloomberg article today:

Hedge funds trailing the Standard & Poor’s 500 (SPX) Index for the last five months are giving up on bearish bets and buying stocks at the fastest rate in two years.

The reason is obvious, and also mentioned in the article:

The benchmark gauge for U.S. equities is on track for the best first-quarter gain since 1998, according to data compiled by Bloomberg.

Yep.  A strong market stimulates demand for equities.  This tends to be true of all financial markets.

This runs counter to the traditional economic theory of supply and demand, where lower prices are expected to stimulate demand.  Traditional economic theory is correct in a special case—only if there is a concrete end use for the product.  For example, if gasoline prices went to $1.25 per gallon, users might be tempted to put tanks in their driveway and fill them up to take advantage of the low price, knowing they will use the gasoline later.  The same thing will be true of canned goods, toothpaste, and toilet paper.  People will buy as much as they can use before it spoils to take advantage of low prices.

Stocks are different.  Financial assets are intangible.  You can’t eat stock certificates or fuel your car with them.  Their end use is performance.  Performance is intangible, but performance depends on rising prices (assuming you are long, anyway).  When prices are rising strongly, it is a market signal that this asset may be useful for performance—and that is what stimulates additional demand.  Relative strength is like a neon sign in this respect.

Hedge funds and institutional investors are particularly subject to performance pressures, so they are very sensitive to market signals.  When markets are trending, they tend to go after the strongest assets first.  This is entirely rational economic behavior when continued poor performance can put you out of business.  Money flows follow performance.

Source: GlassGiant.com

[Endnote:  It is entirely possible, of course, to buy stocks when they are out of favor and make money too.  This is exactly what contrarian, value investors do.  One of the appeals of value investing is that buyers have very little competition when buying out-of-favor assets.  Yet even a value investor needs demand fueled by rising prices to ultimately profit.  It may be true that for every asset there is some "fair" or "intrinsic" value, but it's probably also true that the asset is correctly priced only twice each cycle---once on the way up, and once on the way down.]

I have a bone to pick with this article and its conclusions, but certain things are not in dispute.  Fama and French, in their article Luck versus Skill in the Cross Section of Mutual Fund Returns, look at the performance of domestic equity funds from 1984 to 2006.  (You can find a summary of the paper here.)  They discover that the funds, in aggregate, are worse than the market by 80 basis points per year–basically the amount of the fees and expenses.  (After backing out fees and expenses, the funds are 10 basis points per year above the market.)  After that, Fama and French run 10,000 simulations with alpha set to zero to see if the distribution of returns from actual fund managers is any different from the distribution of returns from the random simulations.  They conclude it is not very different and suggest that any fund manager that outperforms is simply lucky.

Let me start my critique by pointing out that, based on their sample and their goofy experimental design, their conclusions are probably correct.  Existing mutual funds in aggregate pretty much own the market portfolio and underperform by the amount of fees and expenses.  There clearly are some above-average mutual fund managers, but as Fama and French point out, it’s difficult to tell statistically from just performance data if they are good or simply lucky.  Within a big sample of funds like they had, after all, a few are bound to have good performance just because the sample is so large.

This is quite a quandary for the individual investor, so let’s think about the realistic scenarios and their outcomes–in other words, let’s take actual investor behavior into account.

Scenario 1.  Buy a mutual fund after its good performance is advertised somewhere and bail out when it has a bad year.  Continue this behavior throughout your investment lifetime.  According to Dalbar’s QAIB and other data, this is what actually happens most of the time.  Not a good outcome–underperformance by a large margin, often 500 basis points or more annually.

Scenario 2.  Buy a decent mutual fund and make the radical decision to leave it alone, come hell or high water.  Do not be tempted by the blandishments of currently hot funds or panicked by underperformance in your fund when it inevitably happens.  Close eyes and hold on for dear life.  Continue your ostrich-with-its-head-in-the-sand routine throughout your investment lifetime.  Your outcome, as Fama and French point out, will probably be market returns less the 80 basis point per year in fees.  Your returns will probably be 400 basis points annually or more better than Scenario 1.

Scenario 3. Throw active management overboard entirely.  Buy an S&P 500 index fund or a total market index fund and proceed as in Scenario 2.  Your outcome might be 60-70 basis points per year better from reduced costs than the investor in Scenario 2.  (Your cost is that you don’t get to brag at cocktail parties on the occasions when your actively managed fund has a good year.)  On the other hand, you are no less likely to succumb to Scenario 1 than an actively managed mutual fund investor.  Unfortunately, index mutual funds tend to show the same pattern of lagging returns due to investor behavior as actively managed funds.

Scenario 4. Visit Ken French’s own website.  Look for factors that are tested and that have outperformed consistently over time.  Hint: relative strength.  (Academics tend to call it ”momentum,” I suspect because it would be very deflating to have to admit that anything related to technical analysis actually works.)  Find a manager that exposes a portfolio to the relative strength factor in a disciplined fashion over time.  Buy it and pretend you are Rip Van Winkle.  Continue this dolt-like behavior for your entire investment lifetime.  Your outcome, according to Ken French’s own website, is likely to be market outperformance on the magnitude of 500 basis points per year or more.  (You can link to an article showing a performance chart back to 1927 here, and the article also includes the link to Ken French’s database at Dartmouth University.)

I prefer Scenario 4, but maybe that’s just me.  Since it is well-known even to Eugene Fama and Ken French that momentum has outperformed over time, what is their study really saying?  It’s saying that essentially no one in the mutual fund industry is employing this approach.  That’s more a problem with the mutual fund industry than it is with anything else.  (Mutual fund firms are businesses and they have their reasons for running the business the way they do.)  One option, I guess, is to throw up your hands and buy an index fund, but maybe it would make more sense to seek out the rare firms that are employing a disciplined relative strength approach and shoot for Scenario 4.

Their flawed experimental design makes no sense to me.  Although I am still 6’5″, I can no longer dunk a basketball like I could in college.  I imagine that if I ran a sample of 10,000 random Americans and measured how close they could get to the rim, very few of them could dunk a basketball either.  If I created a distribution of jumping ability, would I conclude that, because I had a large sample size, the 300 people would could dunk were just lucky?  Since I know that dunking a basketball consistently is possible–just as Fama and French know that consistent outperformance is possible–does that really make any sense?  If I want to increase my odds of finding a portfolio of people who could dunk, wouldn’t it make more sense to expose my portfolio to dunking-related factors–like, say, only recruiting people who were 18 to 25 years old and 6’8″ or taller?  In the same fashion, if I am looking for portfolio outperformance, doesn’t it make a lot more sense to expose my portfolio to factors related to outperformance, like relative strength or deep value, rather than to conclude that managers who add value are just lucky?  No investigation of possible sub-groups that were consistently following relative strength or deep value strategies was done, so it is impossible to tell.  Fama and French are right, I think, in their assertion that plenty of luck is involved in year-to-year performance, but their overall conclusion is questionable.

In short, I think a questionable experimental design and possible sub-groups buried in the aggregate data (see this post for more information on tricks with aggregate data) make their conclusions rather suspect.

—-this article originally appeared 12/3/2009.  It turned out to be one of the blog readers’ favorite rants, so I am reprising it here.  I still think active management can add value over time through disciplined exposure to a reliable return factor.

In this blog post, “Understanding How Markets Move,” noted psychologist and trader Brett Steenbarger uses the simple example of a moving average system applied to the S&P 500.  The more you speed up the moving average, the worse it does.  That seems counter-intuitive, but you have to keep in mind that trends are what make money and trends  are often slow.  The faster you go, the more noise you capture, and thus, the worse you do.

We find exactly the same process at work when using relative strength.  Reacting to short-term relative strength does not perform well over time.  The best-performing models follow intermediate to long-term relative strength—and just tough out the periods that are rocky.  Many clients have trouble sitting still when going through a rocky period, but as Steenbarger points out in his post, you have to deal with the asset you’re trading.  Stocks have their own time frames for trends and an impatient investor isn’t going to speed it up.  If you want to trade financial assets, you have to work with them on their own terms.

—-this article originally appeared 12/16/2009.  Repeat after me: going faster is counterproductive.  The last nine months or so have been lousy for trends, so it’s prime time for thinking that trends could be captured if only one were more nimble.  Tough periods don’t last.  The market will trend again when it feels like it!

• What is relative strength?
• Why does it work?
• Where does it work?
• What have been the results?
• What are its drawbacks?
• How does it fit in an asset allocation?

Click here to read the article.