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The Limits to Trusting the Robots

October 20, 2017 1 comment

After another day on Thursday of stocks starting to look mildly tired – but only mildly – only to rally back to a new closing high, it hardly seems unusual any more. I have to keep pinching myself, reminding myself that this is historically abnormal. Actually, very abnormal. If the S&P 500 Total Return Index ends this month with a gain, it will be the second time in history that has happened. The other time was in 1936, as stocks bounced back from a deep bear market (at the end of those 12 months, in March 1936, stocks were still 54% off the 1929 highs). A rally this month would also mean that stocks have gained for 19 out of the last 20 months, the longest streak with just one miss since…1936 again.

But we aren’t rebounding from ‘oversold.’ This seems to be a different situation.

What is going on is confounding the wise and the foolish alike. Every dip is bought; the measures of market constancy (noted above, for example) are at all-time highs and the measures of market volatility such as the VIX are at all-time lows. It is de rigeur at this point to sneer “what could go wrong?” and you may assume I have indeed so sneered. But I also am curious about whether there is some kind of feedback loop at work that could cause this to go on far longer than it “should.”

To be sure, it shouldn’t. By many measures, equities are at or near all time measures of richness. The ones that are not at all-time highs are still in the top decile. Buying equities (or for that matter, bonds) at these levels ought to be a recipe for a capitalistic disaster. And yet, value guys are getting carried out left and right.

Does the elimination (with extreme prejudice) of value traders have any implications?

There has been lots of research about market composition: models, for example, that examine how “noise” and “signal” traders come together to create markets that exhibit the sorts of characteristics that normal markets do. Studies of what proportion of “speculators” you need, compared to “hedgers,” to make markets efficient or to cause them to have bubbles form.

So my question is, what if the combination of “buy the dip” micro-time-frame value guys, combine with the “risk parity” guys, represents a stable system?

Suppose equity volatility starts to rise. Then the risk-parity guys will start to sell equities, which will push prices lower and tend to push volatility higher. But then the short-term value guys step in to ‘buy the dip.’ To be clear, these are not traditional value investors, but rather more like the “speculators” in the hedger/speculator formulation of the market. These are people who buy something that has gone down, because it has gone down and is therefore cheaper, as opposed to the people who sell something that has gone down, because the fact that it has gone down means that it is more likely to go down further. In options-land, the folks buying the dip are pursuing a short-volatility strategy while the folks selling are pursuing a long-volatility strategy.[1]

Once the market has been stabilized by the buy-the-dip folks, who might be for example hedging a long options position (say, volatility arbitrage guys who are long actual options and short the VIX), then volatility starts to decline again, bringing the risk-parity guys back into equities and, along with the indexed long-only money that is seeking beta regardless of price, pushing the market higher. Whereupon the buy-the-dip guys get out with their scalped profit but leaving prices higher, and volatility lower, than it started (this last condition is necessary because otherwise it ends up being a zero-sum game. If prices keep going higher and implied volatility lower, it need not be zero-sum, which means both sides are being rewarded, which means that we would see more and more risk-parity guys – which we do – and more and more delta-hedging-buy-the-dip guys – which we do).

Obviously this sort of thing happens. My question though is, what if these different activities tend to offset in a convergent rather than divergent way, so that the system is stable? If this is what is happening then traditional value has no meaning, and equities can ascend arbitrary heights of valuation and implied volatility can decline arbitrarily low.

Options traders see this sort of stability in micro all the time. If there is lots of open interest in options around, say, the 110 strike on the bond contract, and the Street (or, more generally, the sophisticated and leveraged delta-hedgers) is long those options, then what tends to happen is that if the bond contract happens to be near 110 when expiry nears it will often oscillate around that strike in ever-declining swings. If I am long 110 straddles and the market rallies to 110-04, suddenly because of my gamma position I find myself long the market since my calls are in the money and my puts are not. If I sell my delta at 110-04, then I have locked in a small profit that helps to offset the large time decay that is going to make my options lose all of their remaining time value in a short while.[2] So, if the active traders are all long options at this strike, what happens is that when the bond goes to 110-04, all of the active folks sell to try and scalp their time decay, pushing the bond back down. When it goes to 99-28, they all buy. Then, the next time up, the bond gets to 110-03 and the folks who missed delta-hedging the last time say “okay, this time I will get this hedge off” and sell, so the oscillation is smaller. Sometimes it gets really hard to have any chance of covering time decay at all because this process results in the market stabilizing right at 110-00 right up until expiration. And that stabilization happens because of the traders hedging long-volatility positions in a low-volatility environment.

But for the options trader, that process has an end – options expiration. In the market process I am describing where risk-parity flows are being offset by buy-the-dip traders…is there an end, or can that process continue ad infinitum or at least, “much longer than you think it can?”

Spoiler alert: it already has continued much longer than I thought it could.

There is, however, a limit. These oscillations have to reach some de minimus level or it isn’t worth it to the buy-the-dip guys to buy the dip, and it isn’t worth reallocation of risk-parity strategies. This level is much lower now than it has been in the past, thanks to the spread of automated trading systems (i.e., robots) that make the delta-hedging process (or its analog in this system) so efficient that it requires less actual volatility to be profitable. But there is a limit. And the limit is reach two ways, in fact, because the minimum oscillation needed is a function of the capital to be deployed in the hedging process. I can hedge a 1-lot with a 2 penny oscillation in a stock. But I can’t get in and out of a million shares that way. So, as the amount of capital deployed in these strategies goes up, it actually raises the potential floor for volatility, below which these strategies aren’t profitable (at least in the long run). However, there could still be an equilibrium in which the capital deployed in these strategies, the volatility, and the market drift are all balanced, and that equilibrium could well be at still-lower volatility and still-higher market prices and still-larger allocations to risk-parity etc.

It seems like a good question to ask, the day after the 30th anniversary of the first time that the robots went crazy, “how does this stable system break down?” And, as a related question, “is the system self-stabilizing when perturbed, or does it de-stabilize?”

Some systems are self-stabilizing with small perturbations and destabilizing with larger perturbations. Think of a marble rolling around in a bowl. A small push up the side of the bowl will result in the marble eventually returning to the bottom of the bowl; a large push will result in the marble leaving the bowl entirely. I think we are in that sort of system. We have seen mild events, such as the shock of Brexit or Trump’s electoral victory, result in mild volatility that eventually dampened and left stocks at a higher level. I wonder if, as more money is employed in risk parity, the same size perturbation might eventually be divergent – as volatility rises, risk parity sells, and if the amount of dip-buyers is too small relative to the risk parity sellers, then the dip-buyers don’t stabilize the rout and eventually become sellers themselves.

If that’s the secret…if it’s the ratio of risk-parity money to dip-buyer money that matters in order to keep this a stable, symbiotic relationship, then there are two ways that the system can lose stability.

The first is that risk parity strategies can attract too much money. Risk parity is a liquidity-consumer, as they tend to be sellers when volatility is rising and buyers when volatility is falling. Moreover, they tend to be sellers of all assets when correlations are rising, and buyers of all assets when correlations are falling. And while total risk-parity fund flows are hard to track, there is little doubt that money is flowing to these strategies. For example one such fund, the Columbia Adaptive Risk Allocation Fund (CRAZX), has seen fairly dramatic increases in total assets over the last year or so (see chart, source Bloomberg. Hat tip to Peter Tchir whose Forbes article in May suggested this metric).

The second way that ratio can lose stability is that the money allocated to buy-the-dip strategies declines. This is even harder to track, but I suspect it is related to two things: the frequency and size of reasonable dips to buy, and the value of buying the dip (if you buy the dip, and the market keeps going down, then you probably don’t think you did well). Here are two charts, with the data sourced from Bloomberg (Enduring Intellectual Properties calculations).

The former chart suggests that dip-buyers may be getting bored as there are fewer dips to buy (90% of the time over the last 180 days, the S&P 500 has been within 2% of its high). The latter chart suggests that the return to buying the dip has been low recently, but in general has been reasonably stable. This is essentially a measure of realized volatility. In principle, though, forward expectations about the range should be highly correlated to current implied volatility so the low level of the VIX implies that buying the dip shouldn’t give a large return to the upside. So in this last chart, I am trying to combine these two items into one index to give an overall view of the attractiveness of dip buying. This is the VIX, minus the 10th percentile of dips to buy.

I don’t know if this number by itself means a whole lot, but it does seem generally correct: the combination of fewer dips and lower volatility means dip-buying should become less popular.

But if dip-buying becomes less popular, and risk-parity implies more selling on dips…well, that is how you can get instability.

[1] This is not inconsistent with how risk parity is described in this excellent paper by Artemis Capital Management (h/t JN) – risk parity itself is a short volatility strategy; to hedge the delta of a risk parity strategy you sell when markets are going down and buy when markets are going up, replicating a synthetic long volatility position to offset.

[2] If this is making your eyes glaze over, skip ahead. It’s hard to explain this dynamic briefly unless I assume some level of options knowledge in the reader. But I know many of my readers don’t have that requisite knowledge. For those who do, I think this may resonate however so I’m plunging forward.

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Targeting Tuition as a Long Run Goal

September 21, 2017 2 comments

A few months ago, in a couple of articles entitled “The Bias in Investor Perceptions” and “What’s Wrong With the Long Run?”, I started to lay out the case for individuals and family offices to approach the investment challenge like a well-run pension fund or endowment would. Most well-run pensions and endowments these days are run in a “liability-driven” manner, which means that instead of maximizing the performance of the fund’s assets, subject to the risk of those assets – classic “mean variance optimization” based on “Modern Portfolio Theory” – the manager aims to maximize the funded status of the plan subject to the variance in the funded status. That is, the manager recognizes that having assets which mimic the behavior of the liabilities is valuable and worth at least some sacrifice in expected return. Many such portfolios, especially when they are fully funded, have two “buckets” for assets, one that is designated as the “liability immunizing” portfolio and one that is designated the “return-seeking” portfolio.

The reason this is a valuable mode of thought for an individual or family office is that it tends to force a focus on the long run, since the “liabilities” in question (such as retirement, college education, bequests, etc) tend to be long-term in nature. But there are a couple of challenges.

One such challenge is to get the client to focus on that long run, rather than on the brokerage statement that shows up in the mail every month and is always one mouse-click away. And that’s what I discussed/lamented on in those prior two articles.

The other challenge is that, unlike a pension fund or endowment, an individual has a kaleidoscope of different liabilities that behave differently from each other. Some of these, like saving for retirement, can be approximated by general consumer price inflation (CPI). But some, like saving for college or saving for future health care costs, behave in their own unique ways. And so the conundrum for many years has been “sure, personal Liability-Driven-Investing makes sense, but what assets do I hold against those liabilities?”

This has driven calls for “goal-appropriate financial instruments,” led by people like Arun Muralidhar (who specifically used that term in “Goals Based Investing, the KISS Principle, and the Case for New Financial Instruments”) and Robert Shiller, who muses on making “previously untradable risks tradable” in Finance and the Good Society, and has a history of innovative enterprises to attempt the same.

What I am excited about is a step forward in creating these instruments…one that my company Enduring Intellectual Properties has had a key role in. Last week, S&P Dow Jones Indices announced the launch of the “S&P Target Tuition Inflation Index,” which is designed to reflect inflation of college tuition and fees over long-term periods. The index was designed by S&P on the basis of a method that we developed a very long time ago but could never figure out how to commercialize. It involves liquid securities, and so can easily be made into investible products such as mutual funds, ETFs, UITs, and other structured products that individual investors can buy. The chart below shows the index, alongside CPI for College Tuition and Fees (NSA).

As with any liquid markets-based index compared to a periodic economic indicator, the tracking error on a day to day basis is not necessarily good. But it is also not terribly relevant – how your fund does next week should not affect how you feel about your college fund! The strategy is built on an understanding of what the main drivers of college tuition are, and these turn out to be fairly simple (unlike is the case with, say, Medical Care). Because the main drivers of college tuition inflation are the same as the drivers of the index, the errors tend to be “mean-reverting,” meaning that the longer you hold the index the closer (in annualized terms) you tend to be to the target.

Investing in a product linked to this index will not be a substitute for saving money in the first place. But, having saved, investing in such a product should help to reduce the risk that the money saved for college suddenly evaporates, as it did for many parents in 2000-2002 and 2007-2009.

I am ecstatic that we were able to team up with S&P to create such an important index – one that will help investors save in a goal-driven way, with their eyes turned to the future rather than to the latest wiggle in the markets.

The Gold Price is Not ‘Too Low’

August 1, 2017 2 comments

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Before I start today’s article, let me say that I don’t like to write about gold. The people who are perennially gold bulls are crazy in a way that is unlike the people who are perennial equity bulls (Abby Joseph Cohen) or perennial bond bulls (Hoisington). They will cut you.

That being said, they are also pretty amusing.

To listen to a gold bull, you would think that no matter where gold is priced, it is a safe haven. Despite the copious evidence of history that says gold can go up and down, certain of the gold bulls believe that when “the Big One” hits, gold will be the most prized asset in the world. Of course, there are calmer gold bulls also but they are similarly dismissive of any notion that gold can be expensive.

The argument that gold is valuable simply because it is acceptable as money, and money that is not under control of a central bank, is vacuous. Lots of commodities are not under the control of a central bank. Moreover, like any other asset in the world gold can be expensive when it costs too much of other stuff to acquire it, and it can be cheap when it costs lots less to acquire.

I saw somewhere recently a chart that said “gold may be forming a major bottom,” which I thought was interesting because of some quantitative analysis that we do regularly (indeed, daily) on commodities. Here is one of the charts, approximately, that the analyst used to make this argument:

I guess, for context, I should back up a little bit and show that chart from a longer-term perspective. From this angle, it doesn’t look quite like a “major bottom,” but maybe that’s just me.

So which is it? Is gold cheap, or expensive? Erb and Harvey a few years ago noticed that the starting real price of gold (that is, gold deflated by the price index) turned out to be strikingly predictive of the future real return of holding (physical) gold. This should not be terribly shocking – although it is hard to persuade equity investors today that the price at which they buy stocks may affect their future returns – but it was a pretty amazing chart that they showed. Here is a current version of the chart (source: Enduring Investments LLC):

The vertical line represents the current price of gold (all historical gold prices are adjusted by the CPI relative to today’s CPI and the future 10-year real return calculated to derive this curve). It suggests that the future real return for gold over the next decade should be around -7% per annum. Now, that doesn’t mean the price of gold will fall – the real return could be this bad if gold prices have already adjusted for an inflationary future that now unfolds but leaves the gold price unaffected (since it is already impounded in current prices). Or, some of each.

Actually, that return is somewhat better than if you attempt to fit a curve to the data because the data to the left of the line is steeper than the data to the right of the line. Fitting a curve, you’d see more like -9% per annum. Ouch!

In case you don’t like scatterplots, here is the same data in a rolling-10-year form. In both cases, with this chart and the prior chart, be careful: the data is fit to the entire history, so there is nothing held ‘out of sample.’ In other words, “of course the curve fits, because we took pains to fit it.”

But that’s not necessarily a damning statement. The reason we tried to fit this curve in the first place is because it makes a priori sense that the starting price of an asset is related to its subsequent return. Whether the precise functional form of the relationship will hold in the future is uncertain – in fact, it almost certainly will not hold exactly. But I’m comfortable, looking at this data, in making the more modest statement that the price of gold is more likely to be too high to offer promising future returns than it is too low and likely to provide robust real returns in the future.

Reversing the “Portfolio Balance Channel”

Note: We are currently experimenting with offering daily, weekly, monthly, and quarterly analytical reports and chart packages. While we work though the kinks of mechanizing the generation and distribution of these reports, and begin to clean them up and improve their appearance, we are distributing them for free. You can sign up for a ‘free trial’ of sorts here.


Today I want to write about something that’s been bothering me a bit recently. It’s about the Fed’s impending decision to start drawing down its balance sheet over some number of years (whether or not we have an announcement about that at tomorrow’s meeting, it seems likely that “balance sheet reduction” is on tap for later this year). Something had been gnawing at me about that, and until now I haven’t been able to put my finger on it.

It concerns the ‘portfolio balance channel.’ This bit of Fed arcana is part of how the central bank explained the importance of the practice of buying trillions in Treasury bonds. Remember that back when the Fed first started doing Large-Scale Asset Purchases (LSAP), they were concerned that a lack of ‘animal spirits’ were causing investors to shy away from taking risk in the aftermath of the credit crisis. Although this is entirely normal, to the FOMC it was something to be corrected – if people and firms aren’t willing to take risk, then it is difficult for the economy to grow.

So, as the Fed explained it, part of the reason that they were buying Treasuries is that by removing enough safe securities from the market, people would be forced to buy riskier securities. When QE1 started, 10-year TIPS were yielding 2.5%, and that’s a pretty reasonable alternative to equities in a high-risk environment. But the Fed’s ministrations eventually pushed TIPS yields (along with other yields, but by focusing on real rates we can abstract from the part of that decline that came from declining inflation expectations rather than the forced decline in real yields) down to zero in 2011, and eventually deeply negative. As expected, despite the risk aversion being experienced by investors they began to move into equities as the “only game in town” – think about how many times you’ve heard people lament they own equities because ‘there’s nothing else worth owning’? The eventual result, of course, was that expected returns to equities began to fall in line with the (manipulated) expected returns to other securities, until we got the current situation where, according to our calculations, TIPS now have a higher expected real return than equities again (but at a much lower level).

What was bothering me, of course, was that shrinking the balance sheet also implies reversing the “portfolio balance channel.” Via QE, the Fed forced investors into stocks because there were fewer Treasury securities outstanding; every time the Fed bought $1 of bonds, some fraction of that went into stocks. The reverse must also be true – for every $1 of bonds the Fed sells, some fraction of that money must come out of stocks.

I’m not the first person to note that reducing the balance sheet should be a negative for equities since it “reduces liquidity.” But I was always uncomfortable with the vagueness of the “liquidity” mechanism…after all, lots of people predicted cataclysm when the Fed “tapered” QE. The reversal of the portfolio balance channel, though, is a real effect. The money to buy the extra bonds that will be on the market – bonds not held by the Fed must be held by someone, after all – will come from somewhere. And some of that “somewhere” will be from equities, some from real estate, some from cash, etc. I don’t know how big an effect it will be, but I know the sign.

Horse Racing and Value Investing

June 28, 2017 2 comments

Momentum and value investing are two classes of strategies that, historically, alternate ascendancy in terms of which strategy is dominating the other. They are largely opposite strategies: a momentum investor buys a security because it has gone higher (because prices aren’t really a random walk, something which has gone up in price is more likely to continue to go up in price) while a value investor buys a security because it has gone lower (since the lower the buying price, the better the return on a security).

You can imagine the two strategies in the context of horse racing. The “momentum” strategy would be represented by betting on the “favorite,” the horse with the best odds to win as determined by the prior betting. (Some people think the track sets the odds on the horses, but that’s not the case. The payouts are based on the proportion of the entire betting pool allocated to bets on a particular horse, less the track’s vig. So, a horse with “good odds of winning” is simply the horse that has the most money bet on it to win.) That’s pretty close to exactly what a momentum investor in stocks is doing, right? A “value” investor, in the context of horse racing, is the person who bets on the long shots because they have big payoffs when they hit (and the bettor believes, obviously, that these unloved horses are irrationally disliked because most people like betting on favorites and winning frequent, small amounts instead of winning infrequent, large amounts.)

So at the track, sometimes the favorites win and sometimes the long shots win, and there are people in each camp that will tell you their strategy is the better one in the long run. I don’t know that there have been many studies of whether “value” or “momentum” investing in horse racing is the better strategy, but there have been numerous such studies in finance. Both value and momentum have been shown to improve investing strategies, with better risk-adjusted returns than simply buying and holding a capitalization-weighted basket of securities. They tend to have “seasons,” by which I mean long periods when one or the other of these strategies tends to be dominant. But it is very unlikely that either of these strategies could ever be the winner over the long run.

To see why, think of the horse track. Suppose everyone noticed that the favorites were winning, and so more and more money came in on the favorites. What would happen then is that the payoffs on the favorite would get worse and worse, and the payoff on the long shots would get better and better. Eventually, it would be very hard to make money betting the favorites unless they always won. On the other hand, if lots of money were to come in on the long shots, they wouldn’t be long shots for long. So neither strategy can dominate forever.

The same is true in finance. If everyone is betting on the previous winners, then eventually the “losers” become easy money, and vice-versa. The chart below (which is imperfect for a reason I’ll mention in a moment) illustrates the give-and-take. It shows the Russell 1000 “growth” index (RLG, in white) and the Russell 1000 “value” index (RLV, in orange). The source of the chart is Bloomberg.

You can see clearly how “growth” (which has similarities to momentum) outperformed in the Y2k bubble, depressing the heck out of value investors. But then value beat growth for a while, until the next bubble in 2007. The ensuing bear market crushed both strategies.

One caveat here is that the composition of the “growth” and “value” indices doesn’t change every day, and isn’t based on momentum, so that at the peak in 2007 a lot of stocks in the “value” index were not truly value stocks. But you get the general point.

The second, and more important caveat, applies to the years since 2009. This chart would lead one to believe that both value and growth stocks are doing equally well. And they are, given this definition of growth and value. But what this chart really means is that the distinction of “growth” and “value” are now less important than the single factor “momentum.” Whether you have a growth stock with momentum, or a value stock with momentum, is less important than if you compare performance to something else that does not have momentum.

We can illustrate this concept by calculating portfolios that are built to maximize momentum or value for a given risk constraint, and comparing the performance of the portfolios. I’ve done this for a bunch of different types of portfolios (different commodities, equities only, broad investor stock/bond/cash/commodity portfolios, etc) and they all look something like this chart, which shows the total returns of these two competing portfolios:

What I’m doing here is for the security universe in question, I’m calculating for each security a “momentum” score that is simply the year-on-year percentage change in price, and a simple “value” score that is the inverse of the four-year price change.[1] Then I optimize two portfolios, one which maximizes the value score and one which maximizes the momentum score, and then track that portfolio’s performance for the following month (whereupon the portfolios are reconstructed). If there was no memory to the momentum or value processes, these lines would wander around 100…a high momentum score would not increase next month’s performance, e.g.. But, evidently, it does and it has. Over the last three years, for this security universe, the “momentum” portfolio outperformed the “value” portfolios 78% of the time by a cumulative 50%. And this happens for every universe of securities I test. Even within commodities, which are universally hated, the high-momentum commodities are hated less.

Note that this is at the same time that in the first chart above the “growth” and “value” stocks have been performing about the same. This just means that the dispersion between growth and value has been narrow, which is another way that volatility is low.

As a value investor, this situation has been tortuous, and has led me to change the way we do certain things to keep from being purely value all the time. But as I said before, the situation cannot remain this way forever. Every computer is chasing every other computer, for now. But at some point, one of the computers will decide it’s time to lean the other way, and the first ones that do so will be the winners while the other computers start to chase momentum lower.

That might not be as fun for investors as the recent period has been, unless you’re the one who was getting paid on the nag at 200-1 odds.

[1] In this I am taking a cue from Asness, Moskowitz, and Pedersen, “Value and Momentum Everywhere,” Journal of Finance Vol LXVIII, #3, June 2013.

What’s Wrong With the Long Run?

June 6, 2017 4 comments

In my last article, I talked about the importance of getting clients to focus on their progress towards a long-term goal rather than on near-term performance. This is to help the clients, not to help the manager, but decreasing performance myopia is also a good thing for the manager. In addition to the obvious reasons this is true – less client turnover means fewer frictional costs for the manager – there is the less-obvious effect that it has on manager behavior. If our clients aren’t demanding that we chase a hot trend or hot stock or hot sector or hot asset class, it means they are also not rewarding those behaviors…and that, in turn, means that the manager can also focus on the long term, to the benefit of the client.

Focusing on progress towards a long-term goal can, unfortunately, introduce other problems. For one thing, it is hard to compare managers on the basis of how their existing clients are progressing successfully towards long-term goals. It is very easy to compare managers on the basis of the historical performance of client portfolios, and indeed both regulators and industry organizations like the CFA Institute have detailed rules about how client performance should be presented to as to make them more easily comparable. So, if my clients are making excellent progress towards their long-term goals but have “underperformed” this year because they didn’t own Tesla, the prospective client who is comparing our performance will probably go to the manager who is invested in that money-losing cult of personality.

Another problem with focusing on the long run is that there are lots of ways to make it appear as if progress is being made, or that the client is doing better than they really are, by tweaking assumptions. An unscrupulous manager could, for example, assume that stocks are going to have a return equal to their prior 5-year return (which right now is pretty darn good), but if the stock market begins to decline the manager could change his mind and instead decide that a 7% nominal return is appropriate. If we are basically trained hamsters pushing the levers that get us the treat from our clients, then there is an incentive to show them good news. In short, it’s harder to disguise adverse changes in current portfolio value than it is to disguise adverse changes in client progress towards a goal.

As an example of this behavior, consider the long-recognized problem that exists in the pension fund industry. A pension fund’s funded status depends importantly on the rate of return it assumes on the assets which are intended to provide for the fund’s future liabilities. And those assumptions have been ridiculously high for years. There is a great recent article by Pension & Investments Online (“Investment return assumptions of public pension funds”) that illustrates the point. Since 2001, the average return assumptions of pension plans has declined from 8% to 7.6%, despite the fact that asset markets are higher now than they were in 2001 (some of them, such as bonds, drastically so). In 2015 pension funds assumed, on average, a 9.7% return on US equities and a 15.2% return (!!) on real estate. Clearly, the models that pension funds use – or hire similarly-incentivized consultants to create – are not consistent with what we know about how markets really behave. But they’re very convenient for showing progress towards being fully funded.

So if a manager wants to help clients focus on the long-term – which he/she should – then he or she needs to use models that are conservative, and that do not get tweaked favorably over time. One way to do that is to employ a third-person analyst that deploys arm’s-length projections, and ideally one who is compensated over time on the basis of the long-term accuracy of the projections…but, since that sort of analytical contract is not generally available, a minimum requirement should be that the manager transparently disclose to clients the asset-class projections currently being used, and the method for generating them.

It is probably self-serving to point this out, but in that vein a manager who frequently exposes his thought processes to the blogosphere, in books, and in speeches is probably a safer bet than a manager who forwards you his company’s latest article on stocks for the long run.

Categories: Investing

The Bias in Investor Perceptions

June 1, 2017 7 comments

We can do the math. We can, until we are blue in the face, explain to investors why 10% returns in the equity market…even 7% returns…are unlikely going forward. We can show the picture below, sourced from data from Robert J Shiller, illustrating that high starting cyclically-adjusted PE ratios are associated with low future returns (the current level of the CAPE is about 29.5), and admonish that higher levels of the CAPE have been seen on only a few occasions that we all agree ex-post were bubbles.

We can do all this, and yet investors still anticipate that 10-20% returns will be delivered by equities going forward. The pessimistic ones think that only 5-10% is what we will see, ‘in line with historical returns’ that are as high as that of course only because our measurement ends at the current high levels. None of our arguments are new, and research illustrating that investors in the main do not get out just in time to avoid the bear market is hopelessly general because each individual enjoys his or her personal fable: “yeah, but I’m not that guy.”

They can be forgiven, perhaps, their poor memories because, after all, the bad events have been few and far between (at least, the bad events in terms of market returns) for a long time. The chart below shows the rolling 52-week returns of the S&P 500, before dividends, since 1979.

The two financial crises in the 2000s stand out for their deeply negative returns, and contrast with the more-frequent, but shallower, bear markets of the 1980s (of course, there weren’t any bear markets in the 1990s!). The compounded nominal price return since the end of 1978 until the end of last week was 8.76%.

But that’s not how people remember returns. Normal people do not take the ending point, divide by the starting point, and raise to the power of 1/(number of years). Perception is influenced by recency. Over the past five years, if you had asked each week “what has the return of the stock market been over the last year?” the answers would have averaged 12.0%. That’s recency. Perception also weights returns by frequency of observation – and over the 38 years covered by this chart, the average rolling 12-month return has been 9.9%.

So you can understand why individual investors resist when we tell them “the long run return of stocks has been about 7%” or admonish them to be careful about current high prices. In their minds, “stocks have been rising about 10-12% per year” for nearly four decades.

Selective memory also plays a part. When we tell stories about why these events occurred, and the story doesn’t include “we started from very high prices,” we excuse them as exceptions. The bear market in the early 2000s was “the popping of the Internet bubble,” and the one in the late 2000s was “the global financial crisis caused by greedy banks.” So the mind tends to dismiss these exceptions, or weight them less. This is where the “but I will get out next time” fantasy comes in – it justifies this mental calculation. But of course, if we eliminate the “exceptions” when stocks went down, the annual returns are even more remarkable. Since 1979, the rolling 12-month return conditioned on it being positive averaged 16.6%.

These are all irrational, but they are part of perception. From a practitioner’s standpoint, these are inconvenient and the industry has worked for a long time to try and educate investors away from these perceptions since otherwise clients only want to hold stocks. But we can’t change how people think, and how they perceive market returns.

This problem has gotten worse since the early 1990s, because of the accessibility of information about market returns. The Financial News Network was launched in 1981, but it wasn’t until CNBC’s launch in 1989, combined with Chairman Greenspan’s decision to open the Fed’s kimono a few years later, that it became very easy to “check the market.” And, since perception of returns is weighted by the number of observations, more frequent observations of positive numbers has increased the expectations of investors when it comes to stock market returns. Some of the lower-quality advisors actually make the problem worse, by calling clients more often when markets are up than when they are down.

I think education is nice, and we as practitioners should of course try to convey to clients proper expectations for returns. But we can’t beat these cognitive errors; instead, what we should be trying to do is to avoid the focus on recent returns and instead present the client with their progress towards a very long-term goal (see illustration below, which is from something we’re designing), such as a particular sort of goal in retirement (“I’d like to have enough to take two major trips every year.”)

This unfortunately can lead to other games, which I will talk about next week, but it also allows us to manage wealth in a way that beats the outcomes offered by Modern Portfolio Theory’s focus on near-term mean-variance optimization. Now, if only we can persuade clients to do it!

Categories: Investing, Stock Market, Theory
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