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Point Forecast for Real Equity Returns in 2018

January 3, 2018 2 comments

Point forecasts are evil.

Economists are asked to make point forecasts, and they oblige. But it’s a dumb thing to do, and they know it. Practitioners, who should know better, rely on these point forecasts far more than they should. Because, in economics and especially in markets, there are enormous error bars around any reasonable point forecast, and those error bars are larger the shorter-term the forecast is (if there is any mean-reversion at all). I can no more forecast tomorrow’s change in stock market prices than I can forecast whether I will draw a red card from a deck of cards that you hand me. I can make a reasonable 5-year or 10-year forecast, at least on a compounded annualized basis, but in the short term the noise simply swamps the signal.[1]

Point forecasts are especially humorous when it comes to the various year-end navel-gazing forecasts of stock market returns that we see. These forecasts almost never have fair error bars around the estimate…because, if they did, there would be no real point in publishing them. I will illustrate that – and in the meantime, please realize that this implies the forecast pieces are, for the most part, designed to be marketing pieces and not really science or research. So every sell-side firm will forecast stock market rallies every year without fail. Some buy side firms (Hoisington springs to mind) will predict poor returns, and that usually means they are specializing in something other than stocks. A few respectable firms (GMO, e.g.) will be careful to make only long-term forecasts, over periods of time in which their analysis actually has some reasonable predictive power, and even then they’ll tend to couch their analysis in terms of risks. These are good firms.

So let’s look at why point forecasts of equity returns are useless. The table below shows Enduring’s year-end 10-year forecast for the compounded real return on the S&P 500, based on a model that is similar to what GMO and others use (incorporating current valuation levels and an assumption about how those valuations mean-revert).[2] That’s in the green column labeled “10y model point forecast.” To that forecast, I subtract (to the left) and add (to the right) one standard deviation, based on the year-end spot VIX index for the forecast date.[3] Those columns are pink. Then, to the right of those columns, I present the actual subsequent real total return of the S&P 500 that year, using core CPI to deflate the nominal return; the column the farthest to the right is the “Z-score” and tells how many a priori standard deviations the actual return differed from the “point forecast.” If the volatility estimate is a good one, then roughly 68% of all of the observations should be between -1 and +1 in Z score. And hello, how about that? 14 of the 20 observations fall in the [-1,1] range.

Clearly, 2017 was remarkable in that we were 1.4 standard deviations above the 12/31/2016 forecast of +1.0% real. Sure, that “forecast” is really a forecast of the long-term average real return, but that’s not a bad place to start for a guess about next year’s return, if we must make a point forecast.

This is all preliminary, of course, to the forecast implied by the year-end figures in 2017. The forecast we would make would be that real S&P returns in 2018 have a 2/3 chance of being between -10.9% and +11.1%, with a point forecast (for what that’s worth) of +0.10%. In other words, a rally this year by more than CPI rises is still as likely as heads on a coin flip, even though a forecast of 0.10% real is a truly weak forecast and the weakest implied by this model in a long time.

It is clearly the worst time to be invested in equities since the early 2000s. Even so, there’s a 50-50 chance we see a rally in 2018. That’s not a very good marketing pitch. But it’s better science.[4]


[1] Obligatory Robert Shiller reference: his 1981 paper “Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends” formulated the “excess volatility puzzle,” which essentially says that there’s a lot more noise than signal in the short run.

[2] Forecasts prior to 2009 predate this firm and are arrived at by applying the same methodology to historical data. None of these are discretionary forecasts and none should be taken as implying any sort of recommendation. They may differ from our own discretionary forecasts. They are for illustration only. Buyer beware. Etc.

[3] The spot VIX is an annualized volatility but incorporating much nearer-term option expiries than the 1-year horizon we want. However, since the VIX futures curve generally slopes upward this is biased narrow.

[4] And, I should hasten point out: it does have implications for portfolio allocations. With Jan-2019 TIPS yielding 0.10% real – identical to the equity point forecast but with essentially zero risk around that point – any decent portfolio allocation algorithm will favor low-risk real bonds over stocks more than usual (even though TIPS pay on headline CPI, and not the core CPI I am using in the table).

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Retail Investors Aren’t As Stupid As They Tell You

December 11, 2017 Leave a comment

Let’s face it, when it comes to the bullish/bearish argument about equities these days, the bears have virtually all of the arguments in their favor. Not all, but almost all. However, I always think the bears hurt their case with certain poor arguments that tend to be repeated a lot – in fact, it’s one way to tell the perma-bears from the thoughtful bears.

One of the arguments I have seen recently is that retail investors are wayyy out over their skis, and are very heavily invested in stocks with very low cash assets. This chart, which I saw in a recent piece by John Mauldin, is typical of the genre.

Now, bears are supposed to be the skeptics in the equation, and there is just nowhere near enough skepticism being directed at the claim that retail investors are being overly aggressive. Gosh, the first place a person could start is with asking “shouldn’t allocations properly be lower now, with zero returns to cash, than they were when yields were higher?”

But as it turns out, we don’t even have to ask that question because there’s a simpler one that makes this argument evaporate. Consider an investor who, instead of actively allocating to stocks when they’re “hot” (stupid retail investor! Always long at the top!) and away from them when they’re “cold” (dummy! That’s when you should be loading up!), is simply passive. He/she begins in mid-2005 (when the chart above begins) with a 13% cash allocation and the balance of 87% allocated to stocks. Thereafter, the investor goes to sleep for twelve years. The cash investments gain slowly according to the 3-month T-Bill rate; the equity investments fluctuate according to the change in the Wilshire 5000 Total Market index. This investor’s cash allocation ends up looking like this.

How interesting! It turns out that since the allocation to cash is, mathematically, CASH / (CASH+STOCKS), when the denominator declines due to stock market declines the overall cash ratio moves automatically! Thus, it seems that maybe what we’re looking at in the “scary” chart is just the natural implication of fluctuating markets and uninvolved, as opposed to returns-chasing, investors.

Actually, it gets better than that. I put the second chart on top of the first chart, so that the axes correspond.

It turns out that retail investors are actually much more in cash than a passive investor would be. In other words, instead of being the wild-and-woolly returns chasers it turns out that retail investors seem to have been responding to higher prices by raising cash, doing what attentive investors should do: rebalancing. So much for this bearish argument (to be clear, I think the bears are correct – it’s just that this argument is lame).

Isn’t math fun?

Higher Wages: Good for You, Not Good for Stocks

November 27, 2017 2 comments

The documentation of the endless march of asset markets higher has become passé; the illustration of the markets’ overvaluation redundant and tiresome. After years in which these same arguments have been made, without any discernable correction, the sober voices of warning have been discredited and discounted. The defenders of higher valuations have grown more numerous, more vocal, and more bulletproof.

I recently commented in a forum on cryptocurrencies…something to the effect that while I see blockchain as being a useful technology – although one which, like all technologies, will be superseded someday – I don’t expect that cryptocurrency in any of its current forms will survive because they don’t offer anything particularly useful compared to traditional money, and moreover have a considerable trust hurdle to overcome due to the numerous errors, scandals, and betrayals that have plagued the industry periodically since MtGox. Whatever you say about ‘traditional’ money, no one worries that it will vanish from your bank account tomorrow due to some accident. I don’t see anything particularly controversial about that statement, although reasonable people can disagree with my conclusion that cryptocurrency will never gain widespread acceptance. However, the reaction was aggressive and unabashed bashing of my right to have an opinion. I hadn’t even uttered an opinion about whether the valuation of bitcoin is a bubble (it obviously is – certainly there’s no sign of the stability you’d want in a currency!), and yet I almost felt the need to run for my life. The bitcoin folks make the gold nuts look like Caine in the TV show “Kung Fu”: the epitome of calm reasonableness.

But, again, chronicling the various instances of bubble-like behavior has also become passé. It will all make sense after it’s over, when the crowd recovers its senses “slowly, and one by one” as Mackay had it about 170 years ago.

Today though I want to address a quantitative error that I hope is hard to argue with. It has become de rigeur throughout this…let’s call it the recent stages of an extended bull market…to list all of the reasons that a continued rally makes sense. I always find this fascinating because such enumeration is almost never conducted with reference to whether these things are already “in the price.” On the weekend money shows I heard several pundits opine that the stock market’s rally was likely to continue because “growth is pretty good, at around 3%; interest rates are relatively low; inflation is relatively low; government has become more business-friendly, and wages seem to be going up again.” As I say, it seems to me that most of this should already be in the market price of most securities, and not a cause for further advance. But one of those items is in fact a bearish item.

Make no mistake, wages going up is a great thing. And it’s nice to hear that people are finally starting to note that wages are rising (I pointed this out in April of 2016, citing the Atlanta Fed’s macroblog article on the topic, here. But not everyone reads this column, sadly). The chart below shows the Atlanta Fed’s Wage Growth Tracker, against Median CPI.

So wages are going up for continuously-employed persons, and this is good news for workers. But it’s bad news for corporate earnings. Corporate margins have been very high for a very long time (see chart, source Bloomberg), and that’s partly because a large pool of available labor was keeping a lid on wages while weak global demand was helping to hold down commodity input prices.

Higher wages are, in fact, a negative for stocks.

The argument for why higher wages seem like they ought to be a positive for stocks goes through consumption. If workers are earning more money, the thinking goes, then they can buy more stuff from companies. But this obviously doesn’t make a lot of sense – unless the worker is spending more than 100% of his additional wages in consumption (which can happen if a worker changes his/her savings pattern). If a worker earns $10, and spends $9 buying goods, then business revenues rise by less than wage expenditures and business profits fall, all else being equal.

This shows up in the Kalecki profits equation, which says that corporate profits equal Investment minus Household Savings minus Government Savings minus Foreign Savings plus Dividends. (Look up Kalecki Profit Equation on Wikipedia for a further explanation.) Rearranging, Kalecki profits equal Investment, minus Government Savings (that is, surplus…so currently the deficit contributes to profits), minus Foreign Savings, plus (Dividends minus Household Savings). So, if workers save some of their new, higher earnings then corporate profits decline. The chart below shows how the Kalecki decomposition of profits tends to track pretty well with reported business profits (source: Bloomberg).

Now, profit margins have been high over the last year despite the rise in wages (not because of it) because the personal savings rate has been declining (see chart, source Bloomberg).

If wages continue to grow, and workers start to save more of their earnings (paying off credit cards perhaps?), then it means that labor is taking a larger portion of the pie compared to the historically-large portion that has been going to capital. This is good for workers. It is not good for stocks.

Categories: Stock Market, Theory, Wages

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.

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.

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

Is This Bubble Smaller Than We Thought?

I haven’t written in a few weeks. It has been, generally, a fairly boring few weeks in terms of market action, with inflation breakevens oscillating in a narrow range and equities also fairly somnolent. But I can’t blame my lack of posts on a lack of interesting things to remark upon, nor on March Madness, nor on New Jersey Transit (although each of these is a very valid excuse for the general lackadaisical nature of trading in recent weeks). In my case, I plead business exigencies as we are working on a few very exciting projects, one of which I expect to be able to announce in the next week or two.

But writing a blog post/article is never far from my mind. I’ve been doing it for far too long – since the ‘90s if you count the daily letters I wrote for client distribution when I was on Wall Street – and when I haven’t written something in a while it is a bit like an itch on the sole of my foot: I am constantly being reminded about it and the only way to make it stop is to rip the shoe off and scratch. Which tickles. But I digress. What I mean to say is that I have a long list of things I’ve written down that I could write about “if I have time this afternoon,” and it’s only the lack of time that has stopped me. (Some of these are also turning into longer, white-paper type articles such as one I am writing right now estimating the cost of the “Greenspan Put.”)

Some of these ideas are good ideas, but I can’t figure out how to address my hypothesis. For example, I suspect that inflation swaps or breakevens, now that they are near fair value for this level of interest rates, have some component in them right now that could be interpreted as the probability that the Border Adjustment Tax (BAT) eventually becomes law. If the BAT is implemented, it implies higher prices, and potentially much higher depending on the competitive response of other countries. If the BAT fails, then breakevens may not set back very much, but they should decline some; if the BAT looks like it is fait accompli, then inflation quotes could move sharply higher (at least, they should). But prediction markets aren’t making book on the BAT, so I don’t have a way to test (or even illustrate) this hypothesis.

But enough about what I can’t do or won’t be doing; today I want to revisit something I wrote back in December about the stock market. In an article entitled “Add Another Uncomfortable First for Stocks,” I noted that the expected 10-year real return premium for equities over TIPS was about to go negative, something that hadn’t happened in about a decade. In fact, it did go slightly negative at the end of February, with TIPS guaranteed real return over ten years actually slightly above the expected (risky) real return of equities over that time period. At the end of March, that risk premium was back to +3bps, but it’s still roughly the same story: stocks are priced to do about as well as TIPS over the next decade, with the not-so-minor caveat that if inflation rises TIPS will do just fine but stocks will likely do quite poorly, as they historically have done when inflation has risen.

But I got to wondering whether we can say anything about the current market on the basis of how far stocks have outperformed the a priori expectations. That is, if we made a forecast and a decade goes by and stocks have shattered those expectations, does that mean that the forecast was bad or that stocks just became overvalued during that period so that some future period of underperformance of the forecast is to be expected? And, vice-versa, does an underperformance presage a future outperformance?

The first thing that we have to confess is that the way we project expected real returns will not produce something that we expect to hit the target every decade. Indeed, the misses can be huge in real dollar terms – so this is not a short-term or even a medium-term trading system. Consider the following chart (Source: Enduring Investments), which shows the difference of the actual 10-year return compared with the a priori forecast return from 10 years prior. A positive number means that stocks over the period ending on that date outperformed the a priori forecast; a negative number means they underperformed the forecast. In context: a 5% per year miss in the real return means a 63% miss on the 10-year real return. That’s huge.

What you can really see here is that stocks have – no surprise – very long ‘seasons’ of bear and bull markets where investors en masse are disappointed with their returns, or excited about their returns. But let me update this chart with an additional observation about real yields. During the period covered by this chart, there have been three distinct real yield regimes. In the 1960s and 1970s, real yields generally rose. In the late 1970s, 10-year real yields rose to around 4.25%-4.50%, and they didn’t begin falling again in earnest until the late 1980s. (This is in contrast to nominal yields, which started to fall in the early 1980s, but that was almost entirely because the premium for expected inflation was eroding). Between the late 1970s and the late 1980s, real yields were more or less stable at a high level; since the late 1980s they have been declining. In the following chart (Source: Enduring Investments), I’ve annotated these periods and you may reasonably draw the conclusion that in periods of rising interest rates, stocks underperform a priori expectations in real terms while in periods of falling real interest rates, stocks outperform those expectations.

These rolling 10-year rate-of-change figures are interesting but it is hard to see whether periods of outperformance are followed by underperformance etc. It doesn’t look like it, except in the really big macro picture where a decade of outperformance might set the stage for a decade of underperformance. I like the following look at the same data. I took the a priori 10-year real return forecast and applied it to the then-current real price level of the S&P 500 (deflated by the CPI). That produces the red line in the chart below (Source: Enduring Investments). The real price level of the S&P is in black. So the red line is the price level forecast and the black line shows where it ended up.

As I said, this is not a short-term trading model! It is interesting to me how the forecast real level of equities didn’t change much for a couple of decades – essentially, the declining market (and rising price level) saw the underperformance impounded in a higher forecast of future returns. So the “negative bubble” of the 1970s is readily visible, and the incredible cheapness of stocks in 1981 is completely apparent. But stocks were also cheap in real terms in 1976…it was a long wait if you were buying then because they were cheap. Value investing requires a lot of patience. Epic patience.

However, once equity returns finally started to outpace the a priori forecast, and the actual line caught up with the forecast line, the market leapt higher and the twin bubbles of 1999 and 2006 are also apparent here (as well as, dare I say it, the current bubble). But since the forecast line is climbing too, how bad is the current bubble? By some measures, it’s as large or nearly as large as the 1999 bubble. But if we take the difference between the black line and the red line from the prior chart, then we find that it’s possible to argue that stocks are only, perhaps, 30% overvalued and not as mispriced even as they were in 2006.

This may sound like slim solace, but if the worst we have to expect is a 30% retracement, that’s not really so terrible – especially when you realize that that’s in real terms, so if inflation is 3% per year then you’re looking at a loss of 10-15% per year for two years. That’s almost a yawner.

On the other hand, if we are entering an up cycle for real interest rates, then the downside is harder to figure. In the last bear market for real yields, stocks got 60% cheap to fair!

None of this is meant to indicate that you should make major changes in your portfolio now. If all of the evidence that stocks are rich hasn’t caused you to make alterations before now, then I wouldn’t expect this argument to do it! Rather, this is just a different rationality-check on the idea that stocks are overvalued, and my words could actually be taken as soothing by bulls. The chart shows that stocks can be overvalued, and outperform a priori expectations that incorporate valuation measures, for years, even decades. Maybe we’re back in one of those periods?

But we have to go back to the very first point I made, and that’s that if you don’t feel like betting the 30% overvaluation is going to get worse, you can lock in current real return expectations with zero risk and give up nothing but the tails – in both direction – of the equity bet. The equity premium, that is, is currently zero and stocks are additionally exposed to rising inflation. I see nothing tantalizing about stocks, other than the possibility that the downside is perhaps not as bad as I have been fearing.


Administrative Note: Our website at EnduringInvestments.com is about five years overdue for a facelift. We are currently considering how we want to change it, the look & feel we want, and the functionality we desire and require. If you have a suggestion for something you think would be helpful for us to include, please let me know. (Note that this is not a solicitation for web design services so please do not ask! We have picked a firm to do that. I’m just curious what customers and potential customers might want.)

Categories: Investing, Stock Market
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