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Horse Racing and Value Investing

June 28, 2017 1 comment

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 2 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 4 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

Pension Fund Perils: Why Conventional Pairing of LDI with De-risking Glide Paths Produces Inferior Outcomes

February 9, 2017 1 comment

Milla Krasnopolsky, CFA and Michael Ashton, CFA[1]

Combined use of traditional Liability Driven Investment (LDI) and funded status responsive de-risking strategies should be decoupled or rebuilt. Embedded inconsistencies in the treatment of risks in these two elements of what has become a popular pension strategy cause irreconcilable conflicts in their execution and imperils the positive pension fund outcome.

This article provides a critique of the combined LDI / De-risking Glide Path strategy as currently implemented by many pension plan managers and also provides an example of an alternative solution that better improves pension plan outcomes.

deriskingboxApproaches to pension risk management have passed though many phases over the past 40+ years.  Higher rate environments of the 1980s made liability immunization programs with treasuries very attractive, but traditional 60/40 or balanced fund strategies persisted as the dominant strategy for pensions.  As rates began their secular decline, funding levels continued to deteriorate and while liability-driven investing became popular again in the beginning of the new millennium, significant levels of underfunding prevented most pensions from fully matching their assets and liabilities.  A variety of partial risk mitigation solutions began to emerge as the lower rate environment of the past 20 years forced institutional investors to be exposed to higher levels of market risk.  New asset classes were introduced into pension plan portfolios in order to achieve higher returns and higher levels of diversification.  Adverse market volatility was further reduced through creative solutions that incorporated smart beta and risk allocation strategies that delivered lower-volatility at similar levels of long term return.  Other strategies sold liquidity back to the market in order to generate additional return in a low yielding environment.  Some risk-based approaches also introduced interest rate derivative overlay programs to extend interest rate duration of total assets along with equity risk reduction programs to reduce equity market risk.  Finally, de-risking glide paths – and ultimately liability risk transfer to insurance companies – became in vogue as companies continued to struggle with their asset-liability risk and found it expedient to pay insurance companies to assume the problem for them.

In recent years, much has been written about whether pension funds have sufficient assets to support their liabilities, and clearly the source of much of this angst is that…many of them don’t.  One thing that is clear is that after decades of chasing new and creative solutions, the problem of underfunded pension plans is still here and the debate about who should manage the assets, and how they should be managed, continues with ever-increasing urgency.

This article represents our contribution to this debate, with a special focus on the asset allocation requirements for cost effective pension plan de-risking.

 

Two Shortcomings of Traditional LDI and De-risking Strategies, as Combined

Type of risk

At this point it is important to differentiate the assets that function as liability hedges and those assets that better assist with the process of de-risking as the plan glides towards a fully hedged status.  Long duration bonds function as the best hedge for the liabilities, and as the plan’s funded status improves and the de-risking process proceeds, the allocation to bonds increases.  While bonds and bond-like derivatives are a core staple of liability-driven investing (LDI) strategies, for most underfunded plans that have a goal of full funding with some help from asset performance it is economically infeasible to allocate 100% of the assets to the liability-matching portfolio.  A gradual increase in bond assets over time as funding status increases is part of the de-risking asset allocation process.  This is an important distinction between LDI and the process of de-risking.  If the liability-matching assets allow the plan to better lock in the current funded status level, then it is only the remaining assets that allow that plan to reach the next funded status threshold in order for the plan to de-risk further.  Traditionally, these non-LDI assets are exposed to a significant amount of equity beta, as the long-term expected compensation from taking equity risk is positive.  While it is thought to be true that, in the long term, equity beta risk is well compensated, the trouble is that in the shorter time horizon of de-risking process the equity beta is very much dependent on market valuations that are not related to the valuation of the pension liabilities. Therefore, it becomes a tactical rather than a strategic decision to hold equities for a de-risking plan.

While all pension models focus on longer-term horizons, pensions in a de-risking mode have a much lower risk tolerance in the short term.  This has caused many pensions to allocate assets to a variety of alternative investments in order to diversify away from equity beta risk.  However, this practice also introduces other risks to the plan, some of which are illiquidity, currency, and/or additional credit default risk.  So there is an inconsistency: while pension funds are known for taking the very long view when it comes to illiquidity, if the sponsors are pursuing an LDI/de-risking strategy the additional illiquidity is counterintuitive, given the objective to be dynamic and nimble in the de-risking process.

But assuming that potential illiquidity is at least somewhat of a concern to a pension fund manager, then the Hobson’s choice between equity risk or illiquidity likely means that underfunded pension plans that are pursuing joint LDI/de-risking strategies are still carrying too much equity beta risk, or are slowing down the de-risking process while equity risk is mitigated through other less liquid investments, or both. Pension fund managers and their advisors sense this, but tend to reach a type of asset allocation compromise where pension returns may be less optimal and de-risking results are less effective.

So if equity beta isn’t desirable as unrelated to the liability, and illiquidity of many other alternatives make them less-desirable for dynamic rebalancing into LDI assets, what is the most effective way to replace the equity beta for a de-risking plan?  What other forms of beta and/or alpha are appropriate in aiding in the process of de-risking?  From the standpoint of Markowitz efficient frontier generation, risk is a function of return variance and the covariance of the returns of the eligible portfolio elements. Beyond that, to the optimization routine risk is risk. That is, it doesn’t matter whether the risk comes from beta or from alpha.  From the standpoint of the de-risking process, when it comes to the non-LDI assets or return generating assets, alpha is preferred to most beta since alpha is more process-dependent as opposed to market-dependent.   In the shorter-term horizon of de-risking, non-LDI beta introduces more risk.  So our only choice seems to be some combination of liquid alpha and/or well compensated liquid beta that has some correlation to liabilities.  This particular beta may be different from how the liability matching or LDI assets are invested and doesn’t need to match the performance of the liabilities, but should have a positive correlation with liability performance.  That’s a tall order.

Some of the more publicized alpha alternatives are hedge funds, private investments in equity or debt of corporations, or real estate.  We don’t intend to dive into the merits and disadvantages of these or other alternative investments on a stand-alone basis but will only superficially observe their fit in a de-risking framework.  Many hedge funds return as much beta as alpha – indeed, the fact that there are successful hedge-fund replication techniques is virtual proof that many hedge funds are actually beta masquerading as alpha. The obvious visual correlation between hedge fund returns and equity returns, too, should make one suspicious that hedge funds are a pure source of alpha (see Chart, source Bloomberg, comparing the HFRI Fund of Funds Composite Index to the S&P 500).

hfriFigure 1: HFRI Fund of Funds Composite Index vs S&P 500

While those hedge funds or private investments that have a higher correlation to fixed income beta may benefit plans with a long time horizon, they suffer from varying degrees of illiquidity, which impedes the de-rising process as previously discussed.

While there may be other examples for a better alternative, we can provide one strategic example that better fits the combined LDI / de-risking criteria we have discussed in this article.

 The Better Alternative

We have addressed above the type of risk that pension funds do not want to have. But it behooves us as well to point out one type of risk that pension funds really ought to have, and yet tend to be underinvested in: inflation exposure, or more accurately real interest rates.

There is a competent literature about the importance of inflation-linked assets to the pension plan.[2] Importantly, inflation-linked assets are relevant even if the pension benefits are not themselves inflation-linked, since for most pension plans the formula which links the work history of active participants to their future retirement benefits implicitly means that pension benefit accruals for a particular employee are higher the more that employee earns. Since wages generally rise at least partly because of inflation, this implies that any pension fund with active participants still accruing benefits does in fact have some inflation exposure.

But the importance of inflation to the pension plan goes beyond that liability-side insight. Additionally, pension assets are exposed to inflation – and, especially, large changes in inflation – because on the asset side the majority of the assets of most plans are invested in equities and nominal fixed-income. Both of these asset classes are terribly exposed to increases in inflation, especially when inflation rises above 3-4%.[3]

We can go still further. While the effects just mentioned are well-established in the literature, one additional benefit from owning inflation-linked assets has not been discussed as far as we can tell, and that is this: the relative value of inflation-linked bonds, compared to nominal bonds, is related to the business cycle and/or level of interest rates level in the same way that corporate spreads are – but without default risk. The chart below (source: Bloomberg data) highlights the connection between credit spreads and 10-year breakevens.[4] This is important because for most pension funds, the relevant interest rate for discounting liabilities is not the risk-free Treasury rate, but a risky corporate rate; therefore, the liability has credit spread risk and an asset that co-moves with credit spreads – especially without actually having credit risk – is valuable.

creditvsbreaksFigure 2: Inflation spreads (“Breakevens”) vs Credit spreads

In our opinion, given a choice between equity beta and inflation/real rate beta, there is no choice: inflation-linked assets are clearly the more valuable risk for a pension fund to own.

Now, pension plans that are pursuing de-risking along with LDI are typically loathe to replace equity risk, given its advantage (over a full cycle, although not necessarily at any given point) in expected return, with real interest rate risk. But inflation-linked markets have an additional benefit, at least in 2017 – they are inefficient, and produce myriad opportunities to generate alpha along with their useful beta. Indeed, we have designed an investment strategy that addresses all of these requirements:

  • Historical return commensurate with equity returns, with slightly lower total risk
  • Beta from inflation-linked bond markets, which is relevant to pension fund liabilities
  • Risk sourced from useful beta, as well as alpha
  • Implied credit spread exposure, without actual credit risks, which is relevant to pension fund liabilities
  • Superior liquidity to “alts” such as real estate, private equity, or hedge funds – which is more consistent with the de-risking mandate

We call this strategy “Enhanced Systematic Real Return.” In a nutshell, this strategy holds the combination of inflation-linked bonds and breakevens that most efficiently adds inflation protection for a given level of interest rates, and adjusts these proportions based on the richness or cheapness of inflation-linked bonds to capture additional alpha.[5]

 

Magnitude of risk

After determining a different, if not more efficient risk vehicle for the non-LDI assets we now turn to the discussion of how much of this risk should be taken at every point of the glide path.  Should the risk allocation to return generating risk assets (i.e non-LDI assets) only depend on the dollars allocated to these investments or should the risk allocation be independent of dollars allocated and vary based on the level of leverage and/or asset composition?

Not All Risk is Bad

As we have already alluded, prudent risk has some place in the management of a pension fund on a glide path. Yet, as with the villain in the black hat, we have been conditioned to look at the word “risk” and recoil. But not all risk is bad. Certainly, with LDI approaches risk is a negative – after all, the goal of LDI is to maximize the funded status (difference between assets and liabilities), subject to a limit on the maximum volatility (risk) of the funded status. In that construction, there is no doubt that risk is bad, or anyway that less risk is better. But risk is not necessarily bad for de-risking.

This seems counter-intuitive. If we are trying to remove risk, doesn’t that imply that risk is bad? Yes – as we just said, risk is bad for the LDI-driven mandate. But the plan that takes less risk has fewer opportunities to reach de-risking thresholds. That is, the more that you de-risk the longer the next increment of de-risking takes. In this context, it is actually helpful to retain more rather than less risk in the non-LDI assets at each de-risking step.

Here is an analogy from basketball: consider the player who constantly heaves up three-point shots. He shoots a lower percentage from beyond the arc, and so the variance of his scoring is quite a bit higher than his variance shooting short jumpers or layups. Let us suppose that on average, he scores the same amount per game whether he shoots three-pointers or short jumpers. In an asset management context, we would say that this is a “non-optimized” shooter. He should aim for the same average scoring with lower volatility, right?

Now let us suppose that in a particular game, this player’s team is down by 18 points in the final quarter. The coach sends the player onto the court. If this coach is from the pension industry, he instructs his shooter to take only safe shots, because that is how he maximizes his Sharpe Ratio. But if this is actually a basketball coach, he orders his player to take as many three-pointers as he can. Why? He does this because in this situation, risk is good. A strategy of only taking safe shots is guaranteed to lose in this context; only a highly-volatile strategy has a chance of working.[6]

In the same way, prudent addition of volatility as the plan is de-risking helps to de-risk a plan that is under water. So we can see that there is a tension here, and one that is routinely ignored in most LDI/de-risking plans: more volatility is helpful for de-risking, but hurtful inasmuch as it departs from the LDI mandate to maximize the return/risk tradeoff for the funded status. This leads to the phenomenon that is common today, of “hurry up and wait.” As we noted previously: the more that a fund has been de-risked, the longer the next increment of de-risking takes. Each reduction of the proportion of return generating assets to total assets significantly increases the average time until the next de-risking point is reached, as the table below[7],[8] illustrates:

table1Table 1: Reducing return-generating assets will tend to increase time to next trigger

This is problematic. By de-risking, this plan is becoming too conservative as it approaches being fully funded. We can show that the plan reaches a fully-funded status more quickly when it prudently avoids full de-risking. What happens when we allow leverage, and maintain the total portfolio risk even as the bond allocation increases at each trigger? The following table shows the significant result:

table2Table 2: By maintaining portfolio risk to return-generating assets, de-risking proceeds apace.

 

Combining the Right Type, and the Right Magnitude, of Risk

When the pension plan pursues a strategy that focuses on risks sourced from alpha and the “right kinds” of beta sources that will tend to match the liability, and de-risks in a way that recognizes that some risk helps the de-risking task, then the combined result can be powerful. The chart below (Source: Enduring Intellectual Properties, Inc) compares this new approach with the “classic” LDI plus de-risking approach. The dashed lines represent the “classic” approach, while the solid lines represent an approach that uses our “Enhanced Systematic Real Return” strategy as a substitute for the equity risk of the traditional strategy. In each case, this imaginary pension fund starts year zero at 60% funded, and liabilities grow with the Bloomberg/Barclays/Lehman U.S. Long Government/Credit Index. Also in each case, the top line represents the 90th percentile outcome of the Monte Carlo simulation; the bottom line represents the 10th percentile, and the middle line represents the median outcome.

comparativeglidepathsFigure 3: Proper types and magnitudes of risks produce preferable pension outcomes.

There are several facets of this chart worth noting.

Importantly, observe how the median outcome line is linear with our approach, but flattens out with the traditional de-risking approach. This phenomenon is the visual counterpart to Tables 1 and 2; it illustrates how the closer one gets to being fully funded with a traditional glide path, the slower the funded status converges. Our approach, as highlighted in Table 2, is designed to remove that effect. The benefits of that approach aren’t only felt on the median outcome, but are apparent on every path as the funded status moves above 75%.

Also, observe that the superior “good” outcomes aren’t “paid for” by much worse “bad” outcomes. After all, we could have had even better “good” outcomes if we took lots of extra risk. But in that case, the benefit would have come at a price, and we would see it manifesting in much worse “bad” outcomes. The outcomes here are actually skewed to the positive side.

Finally, although you cannot tell this from the illustration, you should know that this simulation assumes that stocks and bonds have expected returns that are somewhere near their historical mean returns. Unfortunately, presently this seems a generous assumption for the traditional approach. It seems more likely that, going forward, pension plans which are invested heavily in equities will be drawing from a distribution with worse-than-average characteristics due to the high starting valuations. Ditto, of course, for fixed-income…but at least bonds affect both sides of the LDI equation.

Summary

LDI and de-risking glide paths can be combined under certain conditions, but current implementation practices create inconsistencies in how risks are treated and do not facilitate achievement of strategic goals.

Asset beta risks that do not match liability beta risks are useful only in a tactical setting, and then only if they are associated with exceptional returns (that is, the market is cheap tactically).

More effort is required to search out new sources of liquid alpha and beta that facilitate the de-risking process. We have produced one that we believe is useful in this context.

As the plan de-risks along the glide path, the level of risk in the non-LDI assets should be adjusted to preserve a quantum of variance that is useful in the de-risking process, as opposed to just mechanically adjusting allocation dollars in a simple glide path.


[1] Milla Krasnopolsky is an investment strategist and investment manager. Milla held previous positions as a Managing Director of Fixed Income Markets and Strategic Solutions at General Motors Asset Management and as a Principal and Senior Investment Consultant at Mercer Investments.  Michael Ashton is the Managing Principal of Enduring Investments and CEO of Enduring Intellectual Properties, Inc.

[2] For the iconic example, see Siegel and Waring, “TIPS, the Dual Duration, and the Pension Plan” (Financial Analysts Journal, September/October 2004).

[3] Remarkably, the myth that common stocks confer some inflation protection has survived decades of contrary experience, both before and after Zvi Bodie’s classic “Common Stocks as a Hedge Against Inflation” (Journal of Finance, Vol. 31, No. 2, May 1976), in which he concluded forcefully “The regression results…leads to the surprising and somewhat disturbing conclusion that to use common stocks as a hedge against inflation one must sell them short.”

[4] The 10-year simple “breakeven” is merely the yield difference between the 10-year nominal Treasury yield and the 10-year TIPS real yield; it represents roughly the amount of future inflation at which an investor would be indifferent between the two types of bonds.

[5] It would be inappropriate to discuss the fine details of this strategy in a thought piece such as this. However, we thought it important to point out that demand for a solution with these characteristics is not hopeless or uninformed. There does exist at least one such solution, and probably others!

[6] This idea isn’t exactly alien in finance: if you own an out-of-the-money option, a higher implied volatility increases your delta while if you own an in-the-money option, a higher implied volatility decreases your delta. It’s just alien in pension fund management.

[7] Both Table 1 and Table 2 represent simplified examples where LDI hedging assets and pension liabilities are proxied by the same long-duration bonds, and future pension contributions are excluded from the analysis.

[8] Table is based on a Monte Carlo simulation of a pension fund that begins with the indicated funding status and allocated as shown until it reaches the next de-risking trigger. Returns for stocks and bonds are simulated; the correlation from the last five years is used. The importance of the table isn’t derived from the precision of the assumptions, but from the illustration of the increased difficulty in reaching the next de-risking increment when the fund is already de-risked substantially.

A (Very) Long History of Real Interest Rates

December 23, 2016 3 comments

One of the problems that inflation folks have is that the historical data series for many of the assets we use in our craft are fairly short, low-quality, or difficult to obtain. Anything in real estate is difficult: farmland, timber, commercial real estate. Even many commodities futures only go back to the early 1980s. But the really frustrating absence is the lack of a good history of real interest rates (interest rates on inflation-linked bonds). The UK has had inflation-linked bonds since the early 1980s, but the US didn’t launch TIPS until 1997 and most other issuers of ILBs started well after that.

This isn’t just a problem for asset-allocation studies, although it is that. The lack of a good history of real interest rates is problematic to economists and financial theoreticians as well. These practitioners have been forced to use sub-optimal “solutions” instead. One popular method of creating a past history of “real interest rates” is to use a nominal interest rate and adjust it by current inflation. This is obvious nonsense. A 10-year nominal interest rate consists of 10-year real interest rates and 10-year forward inflation expectations. The assumption – usually explicit in studies of this kind – is that “investors assume the next ten years of inflation will be the same as the most-recent year’s inflation.”

We now have plenty of data to prove that isn’t how expectations work – and, not to mention, a complete curve of real interest rates given by TIPS yields – but it is still a popular way for lazy economists to talk about real rates. Here is what the historical record looks like if you take 10-year Treasury rates and deflate them by trailing 1-year inflation:

dumbrealThis is ridiculously implausible volatility for 10-year real rates, and a range that is unreasonable. Sure, nominal rates were very high in the early 1980s, but 10%? And can it be that real rates – the cost of 10-year money, adjusted for forward inflation expectations – were -4.6% in 1980 and +9.6% in 1984? This hypothetical history is clearly so unlikely as to be useless.

In 2000, Jay Shanken and S.P. Kothari wrote a paper called “Asset Allocation with Conventional and Indexed Bonds.” To make this paper possible, they had to back-fill returns from hypothetical inflation-linked bonds. Their method was better than the method mentioned above, but still produced an unreasonably volatile stream. The chart below shows a series, in red, that is derived from their series of hypothetical annual real returns on 5-year inflation-indexed bonds, and backing into the real yields implied by those returns. I have narrowed the historical range to focus better on the range of dates in the Shanken/Kothari paper.

skreal

You can see the volatility of the real yield series is much more reasonable, but still produces a very high spike in the early 1980s.

The key to deriving a smarter real yield series lies in this spike in the early 1980s. We need to understand that what drives very high nominal yields, such as we had at that time in the world, is not real yields. Since the real yield is essentially the real cost of money it should not ever be much higher than real potential economic growth. Very high nominal yields are, rather, driven by high inflation expectations. If we look at the UK experience, we can see from bona fide inflation-linked bonds that in the early 1980s real yields were not 10%, but actually under 5% despite those very high nominal yields. Conversely, very low interest rates tend to be caused by very pessimistic real growth outcomes, while inflation expectations behave as if there is some kind of floor.

We at Enduring Investments developed some time ago a model that describes realistically how real yields evolve given nominal yields. We discovered that this model fits not only the UK experience, but every developed country that has inflation-linked bonds. Moreover, it accurately predicted how real yields would behave when nominal yields fell below 2% as they did in 2012…even though yields like that were entirely out-of-sample when we developed the model. I can’t describe the model in great detail because the method is proprietary and is used in some of our investment approaches. But here is a chart of the Enduring Investments real yield series, with the “classic” series in blue and the “Shanken/Kothari” series in red:

endreal

This series has a much more reasonable relationship to the interest rate cycle and to nominal interest rates specifically. Incidentally, when I sat down to write this article I hadn’t ever looked at our series calculated that far back before, and hadn’t noticed that it actually fits a sine curve very well. Here is the same series, with a sine wave overlaid. (The wave has a frequency of 38 years and an amplitude of 2.9% – I mention this for the cycle theorists.)

endrealsine

This briefly excited me, but I stress briefly. It’s interesting but merely coincidental. When we extend this back to 1871 (using Shiller data) there is still a cycle but the amplitude is different.

endreallong

So what is the implication of this chart? There is nothing predictive here; about all that we can (reasonably) say is what we already knew: real yields are not just low, but historically low. (Current 10-year TIPS yields are higher than our model expects them to be, but not by as much as they were earlier this  year thanks to a furious rally in breakevens.) Money is historically cheap – again, we knew this – in a way it hasn’t been since the War effort when nominal interest rates were fixed by the Fed even though wartime inflation caused expectations to rise. With real yields that low, how did the war effort get funded? Who in the world lent money at negative real interest rates like banks awash in cash do today?

That’s right…patriots.

1986-004-223Frankly, that makes a lot more sense than the reason we have low real interest rates today!

Categories: Good One, Investing, Theory, TIPS

Can’t Blame Trump for Everything

November 15, 2016 Leave a comment

So much has happened since the Presidential election – and almost none of it very obvious.

The plunge in equities on Donald Trump’s victory was foreseeable. The bounce was also foreseeable. The fact that the bounce completely reversed the selloff and took the market to within a whisker of new all-time highs was not, in my mind, an easy prediction. I understand that Mr. Trump intends to lower corporate tax rates (and he should, since it is human beings – owners, customers, and employees – that end up paying those taxes; taxing a company is just a way to hide the fact that more taxes are being layered on those human beings). And I understand that lowering the corporate tax rate, if it happens, is generally positive for corporate entities and the people who own them. I’m even willing to concede that, since Mr. Trump is – no matter what his faults – certainly more capitalism-friendly than his opponent, his election might be generally positive for equity values.

But the problem is that equities are already, to put it generously, “fully valued” for very good outcomes with Shiller multiples that are near the highest ever recorded.

I think that investors tend to misunderstand the role that valuation plays when investing in public equities. Consider what has happened to the economy over the last eight years under President Obama: if you had known in 2008 that growth would be anemic, debt would balloon, government regulation would increase dramatically, taxes would increase, and a new universal medical entitlement would be lashed to the backs of the American taxpayer/consumer/investor, would you have invested heavily in equities? Yet all stocks did was triple. The reason they did so was that they started from fairly low multiples and went to extremely high multiples. This was not unrelated to the fact that the Fed took trillions of dollars of safe securities out of the market, forcing investors (through the “Portfolio Balance Channel”) into risky securities. By analogy, might stocks decline over the next four years even if the business climate is more agreeable? You betcha – and, starting from these levels, that’s not terribly unlikely.

I am less surprised with the selloff in global bond markets, and not really surprised much at all with the rally in inflation breakevens. As I’ve said for a long time, fixed-income is so horribly mispriced that you should only hold bonds if you must hold bonds, and then you should only hold TIPS given how cheap they were. Because of their sharp outperformance, 10-year TIPS are now only about 40-50bps cheap compared to nominal bonds (as opposed to 110 or so earlier this year), and so it’s a much closer call. They are not relatively as cheap as they were, but they are absolutely less expensive as real rates have risen. 10-year real rates at 0.37% aren’t anything to write home about, but that is the highest yield since March.

Some analysis I have seen attributes the large increase in market-based measures of inflation expectations on Mr. Trump’s victory. For example, 10-year breakevens have risen 20bps, from about 1.70% to about 1.90%, since Mr. Trump sealed the win (see chart, source Bloomberg).

usggbe

I think we have to be careful about blaming/crediting Mr. Trump for everything. While breakevens rose in the aftermath of the election, you can see that they were rising steadily before the election as well, when everyone thought Hillary Clinton was a sure thing. Moreover, breakevens didn’t just rise in the US, but globally. That’s a very strange reaction if it is simply due to the victory of one political party in the US over another. It is not unreasonable to think that some rise in global inflation might happen, if Trump is bad for global trade…but that’s a pretty big reach, and something that wouldn’t happen for some time in any event.

In my view, the rise in global inflation markets is easy to explain without resorting to Trump. As the previous chart illustrates, it has been happening for a while already. And it has been happening because global inflation itself is rising (although a lot of that at the moment is optics, since the prior collapse of energy prices is starting to fall out of the year-over-year figures).

The bond market and the inflation market are acting, actually, like the Great Unwind was kicked off by the election of Donald Trump. We all know what the Great Unwind is, right? It’s when the imbalances created and nurtured by global central banks and fiscal authorities over the last couple of decades – but especially in the last eight years – are unwound and conditions return to normal. But if pushing those imbalances had a soothing, narcotic effect on markets, we all suspect that removing them will be the opposite. Higher rates and inflation and more volatility are the obvious outcomes.

Equity investors don’t seem to fear the Great Unwind, even though stock multiples are one of the clearest beneficiaries of government largesse over the last eight years. As mentioned above, I can see the argument for better business conditions, even though margins are still very wide. But I’m skeptical that better business conditions can overcome the headwinds posed by higher rates and inflation. Still, that’s what equity investors are believing at the moment.

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A couple of administrative announcements about upcoming (free!) webinars:

On Thursday, November 17th (aka CPI Day), I will be doing a live webinar at 9:00ET talking about the CPI report and putting it in context. You can register for that webinar, and the ensuing Q&A session, here. After the presentation, a recording will be available on TalkMarkets.

On consecutive Mondays spanning November 28, December 5, and December 12, at 11:00ET, I will be doing a series of one-hour educational seminars on inflation. The first is “How Inflation Works;” the second is “Inflation and Asset Classes;” and the third is “Inflation-aware Investing.” These webinars will also have live Q&A. After each session, a recording will be available on Investing.com.

Each of these webinars is financially sponsored by Enduring Investments.

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