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The Internet Has Not Killed, and Will Not Kill, Inflation

June 21, 2017 3 comments

Every few years or so, this story goes around to great acclaim: inflation is dead, killed by the internet. Recently, we have been hearing this story again, quite loudly. The purchase of Whole Foods by Amazon helped bring commentaries like these to the fore:

Credit Suisse’s Varnholt Says Internet Killed Inflation” (Bloomberg)

Low U.S. Inflation? It’s Your Phone: BlackRock Bond Manager” (New York Times)

Amazon Deal for Whole Foods Casts Doubt on Fed’s 2% Inflation Goal” (Barron’s)

And the list goes on and on. These are some of the more-reputable outlets, and they simply misunderstand the whole phenomenon. This isn’t unusual; almost no one really understands inflation, partly because almost no one these days actually studies something that most people presume isn’t worth understanding. (But pardon my ranting digression.)

The internet has not killed, and will not kill, inflation.

In the late 1990s, the internet was having a much greater relative impact. We went from having essentially zero internet in 1995, to a vast array of businesses in 1999 – most of whom were busy transferring money from capital markets to consumers, by raising equity investments which were then use to subsidize money-losing businesses (see especially: Amazon). And inflation? Core CPI in 1999 was 1.9% (Median CPI was 2.03%).

“But there’s more internet now than there was then!” runs the natural objection. Yes, and the internet was dramatically more impactful in 2001 than it was in 1999. Indeed, as the penetration of the internet economy exploded further despite the recession of 2000-2001, core inflation rose to 2.8% (Median CPI topped out at 3.33%) by late 2001.

There is always more innovation happening, whether it’s the 1940s or the 2010s. Innovation is a relatively steady process on the economy as a whole, but very dramatic on parts of the economy – and we tend to fixate on these parts. But there is no evidence that Uber is any more transformative now than Amazon was in the late 1990s. No evidence that Amazon now is any more transformative than just-in-time manufacturing was in the 1980s (in the US). And so on.

“But the internet and mobile technology pervades more of society!” Really? More of society than the J-I-T manufacturing innovation? More of society than airlines and telephones, both of which were de-regulated/de-monopolized in the 1980s? More of society than personal computers did in the 1990s? We all like to think we are living in unique times full of wonder and groundbreaking innovation. But here’s the thing: we always are.

“But Amazon bought Whole Foods and disrupted the whole food industry! How can you be more pervasive than food?” It remains to be seen whether Amazon is able to do what Webvan and FreshDirect and other food delivery services have been unable to do, and that is to remake the entire delivery chain for food at home. But let’s suppose this is true. Food at home is only 7.9% of the consumption basket, which is arguably less than the part of society that Amazon has already reorganized. Moreover, it’s a highly competitive part of society, with margins that are already pretty thin. How much fat is there to be cut out by Amazon’s efficiency? Some, presumably. But after Amazon makes some kind of profit on this improvement, how much of a decline in food prices could we see? Five percent, over five years? 10%? If Amazon’s “internetification” of the food-at-home industry resulted in a 10% decline in prices of everything we buy at the grocery store, over five years, that 2% per year would knock a whopping 0.16% off of headline inflation. Be still, my heart.

“In any event, this signals that competition is getting ever-more-aggressive.” No doubt, though it is ever so. But here is the big confusion that goes beyond all of the objections I’ve previously enumerated: microeconomic effects cause changes in relative prices; macroeconomics is responsible for changes in the overall price level. Competitive pressures in grocery may keep food prices down 10% relative to price increases in the rest of the economy. But suppose the money supply doubles, and all prices rise 100%, but food prices only rise 90%. Then you have your 10% relative deflation but prices overall still rose by a lot. If the governments of the world flood economies with money, no amount of competition will keep prices from rising. This is why there wasn’t deflation in 2010, despite a massive economic contraction in the global financial crisis and concomitant cutthroat competition for scarce customers in many industries.

So inflation isn’t dead, and neither is this myth. It will come back again in a few years – I am sure of it.

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

Profits and Health Care: A Beneficial Connection

March 17, 2017 4 comments

I usually try to avoid political commentary in this space, because it has become so personal to so many people. If I point out that a particular program of the “left” is smart, or cleverly put together, then half of my readership is annoyed; if I point out the same about the right, then half of my readership is angry. It doesn’t really make sense to waste article space except on those occasions when a policy has a clear effect on inflation over time, such as when the structure of the ACA made it clear that it would put upward pressure on inflation (as I pointed out in 2013) or in response to someone else’s flawed analysis of a policy, as I did last year when I tackled the San Francisco Fed for their weak argument about how the ACA would hold down inflation because the government would demand lower prices. Actually, there is no policy I have written about more than the ACA over the years – but again, this was economic commentary and not political commentary.

This article will be short, but different in that I am writing it to express frustration with the absolute lack of intellectual clarity on the part of the Republicans in making a particular argument that immediately impacts the debate over health care but also extends far into other policies. And, because the argument is simple, direct, and has tremendous empirical support, I couldn’t restrain myself. I expect this article will not be picked up and syndicated in its usual channels since it isn’t directly about economics or markets, but it needed to be said.

I’ve been stewing about this topic since Tuesday (March 14th), when I happened to catch part of the daily White House press briefing. Press Secretary Sean Spicer was asked a question about the President’s health care proposal, and tap danced away from the question:

Q    Thanks, Sean.  You mentioned the call with the CEO of Anthem Health.  Can you tell me what this proposal of the President means for health insurance companies?  Will their profits go up or down under the President’s proposal?

SPICER:  Well, I don’t think that’s been the focus of the President’s proposal.  It’s not about them, it’s about patients.  But I think what it means for them is that they finally get to create more choice and more plans and allow people to choose a plan that fits them.  Right now, they don’t have that choice.  And, frankly, in more and more markets, companies like Anthem, UnitedHealth, Signa are pulling out — Aetna — because they don’t have the choice and because of the government mandate.  I think what we want to do is allow competition and choice to exist so that they can offer more options for the American people.

Q    But will those companies make more money under the President’s plan or less?

SPICER:  I don’t know the answer to that.  That’s not been the focus of what we’re doing now.  And at the end of the day, right now they’re pulling out of market after market, leaving the American people with fewer and fewer choices.  So right now it’s not a question of — from the last I checked, I think many of them were doing pretty well, but it’s the American people and its patients that are losing under the current system.  So I think that there’s a way you can do a little of both.

Spicer’s response was the usual drivel that the Republicans have adopted when they run in fear from any question that includes the word “profits.” To summarize, the question was basically, “you’re doing this to throw a sop to fat-cat insurance companies, aren’t you?” and the answer was “we don’t think about that. No idea. Profits? Who said anything about profits? It’s about patients and choice. And, if anyone gets more profits, it wasn’t on purpose and we didn’t have anything to do with it.”

But this was actually a softball question, and the answer ought to have been something like this:

Q    But will those companies make more money under the President’s plan or less?

BIZARRO SPICER: Well, I hope so. After all, the insurance companies want every person in America to have health care – which is the same thing that we want – because the more people they sell their product to, the more money they can make. The insurance companies want to sell insurance to every person in the U.S. The insurance companies also want costs to be lower, and constantly strive to lower the cost of care, because the lower that costs are, the more profit they can make in the short run. But they don’t want lower costs at the expense of health – clearly, the best outcome for their profits is that most people covered by insurance are healthy and so don’t require the insurance they’ve paid for. So, if we just get out of the way and let companies strive for better profits, we are likely to get more coverage, lower costs, and a healthier population, and that is the goal of the President’s plan.

The reason we don’t already have these things is that laws we have previously passed don’t allow insurance companies to offer certain plans, to certain people, which both sides want but which politicians think are “unfair” for one reason or another. Trying to create a certain preconceived Utopian outcome while limiting profits of insurance companies is what caused this mess in the first place.

If you want to beautify gardens in this city, does it make sense to limit the amount of money that gardeners can make? If you did, you would find fewer gardens got tended, and gardeners would not strive to make improvements that they didn’t get paid for. We can see this clearly with gardeners. Why is it so hard to understand with the companies that tend to the nation’s health? Next question.

For some reason, Republicans think that saying “profits are good” is the same thing as saying “greed is good” and leads to caricatures of conservatives as cigar-smoking industrialists. But while at some level it is the desire for a better material outcome – which I suppose is greed, but aren’t there degrees of greed? – that drives the desire for profit, we cannot dismiss the power of self-interest as a motive force that has the effect of improving societal outcomes. “It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest,” after all.

Of course, Republicans must also remember that profit without competition is a different animal. If an insurance company creates an innovation that lowers medical care costs, but does not face competitive pressure, then the benefit of the innovation accrues to the company alone. There is no pressure in such circumstances for the company to lower the price to the customer. But consider what happened to air fares after the deregulation of 1978, or to the cost of telephone service when the AT&T monopoly was broken up in 1984, as competition was allowed and even encouraged. Competition, and the more brutal the better, is what causes companies to strive for an edge through innovation, and it’s also what causes the benefit of that edge to eventually be accrued by the end customer. The government didn’t invent cell phones. Motorola did, in order to try and gain an edge against AT&T,[1] but until the telephone monopoly was broken up there were no commercial versions of the cell phone. The first cell phones cost $10,000 in 1983, about $25,000 in today’s dollars, but now they are ubiquitous and cost about 2% as much in real terms. But this didn’t happen because of a government program to drive down the cost of cell phones. It was the profit motive, combined with competition. All that government did was create the conditions that allowed innovation and competition to happen. And wouldn’t we like health care to be as ubiquitous and cheap as cell phones are?

This is not a hard thing to get right. It isn’t hard for people to understand. But for some reason, it seems incredibly hard for politicians to believe.

Note that nothing I have written here should be construed as an opinion about the President’s health care plan, which I have not read. My remarks are only meant to reflect on the utter inability of Republicans to properly convey the reasons that a different approach – one where the government’s involvement is lessened, rather than increased – would make more sense.

[1] The first cell phone call was made by the inventor, Martin Cooper at Motorola, who called his competition with it: the head of the cellular program at AT&T. According to him, he said “Joel, I’m calling you from a cellular phone, a real cellular phone, a handheld, portable, real cellular phone” and he said it got really quiet on the other end of the line.

Good Models and Bad Models

I have recently begun to spend a fair amount of time explaining the difference between a “good model” and a “bad model;” it seemed to me that this was a reasonable topic to put on the blog.

The difference between a good model and a bad model isn’t as obvious as it seems. Many people think that a “good model” is one that makes correct predictions, and a “bad model” is one that makes bad predictions. But that is not the case, and understanding why it isn’t the case is important for economists and econometricians. Frankly, I suspect that many economists can’t articulate the difference between a good model and a bad model…and that’s why we have so many bad models floating around.

The definition is simple. A good model is one which makes good predictions if high-quality inputs are given to the model; a bad model is one in which even the correct inputs doesn’t result in good predictions. At the limit, a model that produces predictions that are insensitive to the quality of the inputs – that is, whose predictions are just as accurate no matter what the inputs are – is pure superstition.

For example, a model of the weather that depends on casting chicken bones and rat entrails is a pretty bad model since the arrangement of such articles is not likely to bear upon the likelihood of rain. On the other hand, a model used to forecast the price of oil in five years as a function of the supply and demand of oil in five years is probably an excellent model, even though it isn’t likely to be accurate because those are difficult inputs to know. One feature of a good model, then, is that the forecaster’s attention should shift to the forecasting of the inputs.

This distinction is relevant to the current state of practical economics because of the enormous difference in the quality of “Keynesian” models (such as the expectations-augmented Phillips curve approach) and of monetarist models. The simplest such monetarist model is shown below. It relates the GDP-adjusted quantity of money to the level of prices.

This chart does not incorporate changes in money velocity (which show up as deviations between the two lines), and yet you can see the quality of the model: if you had known in 1948 the size of the economy in 2008, and the quantity of M2 money there would be in 2008, then you would have had a very accurate prediction of the cumulative rate of inflation over that 60-year period. We can improve further on this model by noting that velocity is not random, but rather is causally related to interest rates. And so we can state the following: if we had known in 2007 that the Fed was going to vastly expand its balance sheet, causing money supply to grow at nearly a 10% rate y/y in mid-2009, but at the same time 5-year interest rates would be forced from 5% to 1.2% in late 2010, then we would have forecast inflation to decline sharply over that period. The chart below shows a forecast of the GDP deflator, based on a simple model of money velocity that was calibrated on 1977-1997 (so that this is all out-of-sample).

That’s a good model. Now, even solid monetarists didn’t forecast that inflation would fall as far as it did – but that’s not a failure of the model but a failure of imagination. In 2007, no one suspected that 5-year interest rates would be scraping 1% before long!

Contrariwise, the E-A-Phillips Curve model has a truly disastrous forecasting history. I wrote an article in 2012 in which I highlighted Goldman Sachs’ massive miss from such a model, and their attempts to resuscitate it. In that article, I quoted these ivory tower economists as saying:

“Economic principles suggest that core inflation is driven by two main factors. First, actual inflation depends on inflation expectations, which might have both a forward-looking and a backward-looking component. Second, inflation depends on the extent of slack (or spare capacity) in the economy. This is most intuitive in the labor market: high unemployment means that many workers are looking for jobs, which in turn tends to weigh on wages and prices. This relationship between inflation, expectations of inflation and slack is called the “Phillips curve.”

You may recognize these two “main factors” as being the two that were thoroughly debunked by the five economists earlier this month, but the article I wrote is worth re-reading because it describes how the economists re-calibrated. Note that the economists were not changing the model inputs, or saying that the forecasted inputs were wrong. The problem was that even with the right inputs, they got the wrong output…and that meant in their minds that the model should be recalibrated.

But that’s the wrong conclusion. It isn’t that a good model gave bad projections; in this case the model is a bad model. Even having the actual data – knowing that the economy had massive slack and there had been sharp declines in inflation expectations – the model completely missed the upturn in inflation that actually happened because that outcome was inconsistent with the model.

It is probably unfair of me to continue to beat on this topic, because the question has been settled. However, I suspect that many economists will continue to resist the conclusion, and will continue to rely on bad, and indeed discredited, models. And that takes the “bad model” issue one step deeper. If the production of bad predictions even given good inputs means the model is bad, then perhaps relying on bad models when better ones are available means the economist is bad?

That Smell in the Fed’s Elevator

March 7, 2017 5 comments

A new paper that was presented last week at the 2017 U.S. Monetary Policy Forum has garnered, rightly, a lot of attention. The paper, entitled “Deflating Inflation Expectations: The Implications of Inflation’s Simple Dynamics,” has spawned news articles such as “Research undercuts Fed’s two favorite U.S. inflation tools”(Reuters) and “Everything the Market Thinks About Inflation Might Be Wrong,”(Wall Street Journal) the titles of which are a pretty decent summary of the impact of the article. I should note, because the WSJ didn’t, that the “five top economists” are Stephen Cecchetti, Michael Feroli, Peter Hooper, Anil Kashyap, and Kermit Schoenholtz, and the authors themselves summarize their work on the FiveThirtyEight blog here.

The main conclusion – but read the FiveThirtyEight summary to get it in their own words – is that the momentum of the inflation process is the most important variable (last year’s core inflation is the best predictor of this year’s core inflation), which is generally known, but after that they say that the exchange rate, M2 money supply growth, total nonfinancial credit growth, and U.S. financial conditions more broadly all matter more than labor market slack and inflation expectations.

Whoops! Who farted in the Fed’s elevator?

The Fed and other central banks have, for many years, relied predominantly on an understanding that inflation was caused by an economy running “too hot,” in that capacity utilization was too high and/or the unemployment rate too low. And, at least since the financial crisis, this understanding has been (like Lehman, actually) utterly bankrupt and obviously so. The chart below is a plain refutation of the notion that slack matters – although much less robust than the argument from the top economists. If slack matters, then why didn’t the greatest slack in a hundred years cause deflation in core prices? Or even get us at least close to deflation?

I’ve been talking about this for a long time. If you’ve been reading this blog for a while, you know that! Chapters 7-10 of my book “What’s Wrong With Money?: The Biggest Bubble of All” concerns the disconnect between models that work and the models the Fed (and most Wall Street economists) insist on using. In fact, the chart above is from page 91. I have talked about this at conferences and in front of clients until I am blue in the face, and have become accustomed to people in the audience staring at me like I have two heads. But the evidence is, and has long been, incontrovertible: the standard “expectations-augmented-Phillips-Curve” makes crappy predictions.[1] And that means that it is a stupid way to manage monetary policy.

I am not alone in having this view, but until this paper came out there weren’t too many reputable people who agreed.

Now, I don’t agree with everything in this paper, and the authors acknowledge that since their analysis covers 1984-present, a period of mostly quiescent inflation, it may essentially overstate the persistence of inflation. I think that’s very likely; inflation seems to have long tails in that once it starts to rise, it tends to rise for some time. This isn’t mysterious if you use a monetary model that incorporates the feedback loop from interest rates to velocity, but the authors of this paper didn’t go that far. However, they went far enough. Hopefully, this stink bomb will at last cause some reflection in the halls of the Eccles building – reflection that has been resisted institutionally for a very long time.

[1] And that, my friends, is the first time I have ever used “crap” and “fart” in the same article – and hopefully the last. But my blood pressure is up, so cut me some slack.

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
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