Tag Archives: M1

We’re Gonna Need A Bigger Boat

If 2018 rings in a bear market, it could look something like the Kennedy Slide of 1962.

That was my conclusion in “Riding the Slide,” published in early September, where I showed that the Kennedy Slide was unique among bear markets of the last eighty years. It was the only bear that wasn’t obviously provoked by rising inflation, tightening monetary policy, deteriorating credit markets or, less commonly, world war or depression.

Moreover, market conditions leading up to the Slide should be familiar—they’re not too far from market conditions since Donald Trump won the 2016 presidential election. In the first year after Kennedy’s election, as in the first year after Trump’s election, inflation seemed under control, interest rates were low, credit spreads were tight, and the economy was growing. And, in both cases, the stock market was booming.

Here’s an updated look at Trump’s stock rally versus the Kennedy rally and subsequent Slide:

As you can see, we’ve now reached the chart’s critical juncture—at this time of the calendar in 1962, the post-election rally was ending, and the Slide was about to begin. Our chart begs the question: Will the similarities continue and lead us into a Trump Slide in early 2018?

Or, with less drama, you might like to hear my Q1 stock market outlook.

While it’s certainly possible Trump’s rally has run its course, I’ll argue that it’s unlikely. And to make my case, I’ll rely largely on a single indicator, one that measures monetary policy. I use the indicator to help determine whether policy is behind the curve, ahead of the curve, or somewhere in between. In this article, I’ll call it VCURVE, for “versus the curve.”

Tracking VCURVE Through 16 Market Corrections

Before I explain how VCURVE is calculated, let’s look at the history. The following chart compares VCURVE to every instance since 1954 when the stock market corrected by more than 10% and for at least two months:

The upper panel shows an especially strong correlation with stock price cycles between 1954 and 1988. All ten of that period’s market corrections coincided with an upward spike in VCURVE. Despite a few instances of delay between the change in VCURVE and the market’s reaction, the indicator’s early track record was stellar—it predicted every correction with almost no head fakes. (I say “early track record” because fed funds data is only available from 1954. I’ll modify the indicator to gain a longer history at another time.)

But the historical performance didn’t persist after the 1980s at the same exceptional standard. The lower panel shows the correlation weakening, with jumps in VCURVE becoming a fifty–fifty proposition as to whether they signal a market correction.

The reason for the weaker correlation is open to debate, but I would say it’s explained mostly by the Fed’s practice of jumping to action at any hint of market turmoil. VCURVE probably hasn’t shown the same predictive power under the FOMCs chaired by Alan Greenspan, Ben Bernanke and Janet Yellen because of the respective Greenspan, Bernanke and Yellen “puts.” Whereas VCURVE before Greenspan was as reliable an indicator as you’ll find, more recently the Fed’s plunge-protection game often wins the day.

Calculating VCURVE

All that said, even the chart’s lower panel shows an excellent market indicator. The head fakes may be more frequent, but every correction still lines up with a degree of VCURVE turbulence. And just as importantly, it’s an easy indicator to calculate. Here are the two steps:

  1. From the current fed funds rate, subtract the lowest rate since the last market correction.
  2. Add the change in inflation over the past twelve months.

The first step tells us how far along the Fed is in a tightening cycle, and the second converts that figure to a measure of where the Fed stands versus “the curve.” Consider a few possible combinations:

  • If the current tightening cycle is far along but inflation is falling, VCURVE won’t be as high as it otherwise would be, because the Fed has taken enough action to dampen inflation risks. (Policy is ahead of the curve.)
  • If the current tightening cycle is young but inflation is rising, VCURVE will be higher than it otherwise would be, because the Fed may be forced to tighten more aggressively to contain inflation risks. (Policy is behind the curve.)

So VCURVE has three qualities that make it an effective indicator. It’s conceptually relevant, easy to calculate and historically proven.

And what does it tell us today?

At first glance, the latest reading is ambiguous. It’s higher than it was between 2012 and 2015, but only modestly so at 1.6%. To glean more information, we’ll take a closer look at the indicator’s history.

Testing VCURVE Against Subsequent Real Stock Returns

The next chart shows average inflation-adjusted stock returns over three-month periods following VCURVE readings in each of seven buckets:

From the pattern shown on the chart, we can make two observations:

  1. The strongest real returns tend to follow VCURVE readings of less than 2%.
  2. Real returns don’t normally fall below zero until VCURVE jumps above 3%.

We shouldn’t bet all our chips on the exact thresholds of 2% and 3%, history not always repeating and all that, but the pattern gives us a reasonable guide to early 2018. The latest reading of 1.6% falls within a range that’s followed by real quarterly stock returns averaging over 3%—hardly a bearish signal.

Conclusions

More broadly, two particular risks pose the greatest threats in early 2018. First, the market may have run too hot since Trump’s election, leaving investors overextended and unable to push prices higher. An overbought market appears to partially explain the Kennedy Slide of 1962, and a similarly overbought market today could spark a profit-taking correction.

Second, the Fed’s determination to tighten policy should continue to push VCURVE higher, even as it’s not especially high today. To be sure, rate hikes alone are unlikely to make a difference until later in 2018 at the FOMC’s projected pace of twenty-five basis points every four months, but further hikes coupled with an unexpectedly large jump in inflation would be a different story. In a rising inflation scenario that shows the Fed falling behind the curve, a correction of at least 10% would be likely and we’d probably see a full bear.

Of course, plenty of other risks could gain traction as the year gets underway. See, for example, these thirty risks discussed by Deutsche Bank and ZeroHedge or these fifteen from Doug Kass and Real Investment Advice. Also, market valuation points to meager long-term returns, as I discussed in “2 Key Indicators” and then showed somewhat differently in “Charts that Might Define the Jerome Powell Era.”

On a three-month horizon, though, most of the best indicators favor continued strength. Credit markets aren’t nearly as threatening as they were before recent bears—delinquencies, credit spreads and bank lending standards are all either neutral or just mildly bearish at worst. Moreover, the real and financial economies appear settled into a “virtuous” loop of mutually reinforcing strength, as I discussed here, while the GOP’s tax cuts should help sustain that loop for awhile longer.

And lastly, the Fed’s inch-worming monetary tightening pace hasn’t accumulated enough force as of yet to push VCURVE into a danger zone. As possibly the most effective of all fundamental indicators, I don’t recommend betting against VCURVE.

All things considered, I expect market valuation to become even more expensive before the next correction takes hold. Comparing the Trump and Kennedy rallies—as in the first chart above—I expect Trump’s market to build an even bigger slide.

A Strong Signal From The Economic Dashboard

We’ve been seeing more and more commentaries discussing bad stuff that can happen when the Fed tightens policy and, as a result, the yield curve flattens. (See, for example, this piece from Citi Research and ZeroHedge.) No doubt, the Fed’s rate hikes will lead to mishaps as they usually do—in both markets and the economy. But most forecasters expect the economy to expand through next year, believing that the Fed and the yield curve aren’t yet restrictive enough to trigger a recession.

We won’t make a full-year 2018 forecast here, but we’ll share one of our “dashboard” charts that supports the consensus view for at least the first half of the year. With one methodological change to a chart we published in August, we’ll look at the following indicators, which together have an excellent track record predicting the business cycle:

The idea is that the economy tends to turn over when investors lose money, borrowers find it hard to obtain financing, business earnings weaken, and banks struggle with a flat or inverted yield curve. Here’s a history of all four of those indicators in the quarter before and the quarter of the last nine business cycle peaks, although with less data for lending standards, which the Fed began surveying for the first time in mid-1990:

With that history as our background (in charcoal gray), our dashboard highlights the most recent data, along with our fourth quarter estimates for asset price gains and S&P 500 earnings growth:

In our view, the above chart is the best way to judge recession risks—with a strong reminder of how current conditions compare to the conditions that shaped past business cycles. That comparison looks favorable as of mid-December, just as it did in August. Here are our takeaways, moving from right to left along the chart:

  • Although the yield curve is likely to become more recessionary as the Fed continues to tighten, it’s not yet as flat or inverted as it normally is at business cycle peaks.
  • Business earnings aren’t yet recessionary, either, although gains over the last four quarters reflect depressed earnings in 2015 and 2016, which isn’t quite as bullish a signal as it would be if earnings had risen consistently over that period.
  • Outside of the commercial real estate sector, lending conditions aren’t constraining borrowing growth, and even CRE lending conditions aren’t restrictive when compared to the last three business cycle peaks.
  • Asset gains have been stellar over the past four quarters, far above the flat or declining performance that nearly always precedes business cycle peaks.

We think the last point is the most convincing. Of all the “rules” in economics, the rule that asset prices lead the business cycle is as reliable as any, and they’re a long way from recessionary as of this writing. In fact, if Q4’s gains match the average gains over the past four quarters, real asset gains for 2017 will reach 25% of personal income. That’s three months of personal income from asset gains alone—hardly an environment where households stop spending and the economy slips into recession.

But eventually, monetary tightening will have greater effects, and the outsized asset gains of recent years will become more burden than boon. That’s notwithstanding Janet Yellen’s FOMC press conference on Wednesday, where she downplayed risks posed by soaring asset prices. Yellen’s parting words are certainly welcoming of debate, and we recommend the responses here, as well as this recent report from the Office for Financial Research. But for this article, we’ll just couple our bullish economic view for H1 2018 with a chart we first shared last week:

Although the chart includes only seven business cycles to keep it readable, the full history shows asset gains, adjusted for inflation, jumping above those of any other cycle since the Fed began recording gains in 1947. It paints a bigger picture behind the “virtuous” loop that’s currently fueling the economy and thus far impervious to the Fed’s snail-pace tightening. And we think it describes the greatest challenge Yellen’s successor will face, although not an immediate challenge. In our view, the tightening we’ve seen to date is still too new and too tepid to threaten the usual damage, especially with the dashboard readings above and with fiscal policy set to loosen. But further out, we suggest trusting the history of how long-running virtuous loops normally unwind.

2-Charts That Will Define The Fed’s “Jerome Powell” Era

In September, we proposed a theory of the Fed and suggested that the FOMC will soon worry mostly about financial imbalances without much concern for recession risks. We reached that conclusion by simply weighing the reputational pitfalls faced by the economists on the committee, but now we’ll add more meat to our argument, using financial flows data released last week. We’ve created two charts, beginning with a look at cumulative, inflation-adjusted asset gains during the last seven business cycles:

According to the way that the Fed defines its policy approach, our first chart stamps a giant “Mission Accomplished” on the unconventional policies of recent years. Recall that policy makers explained their actions with reference to the portfolio balance channel, meaning they were deliberately enticing investors to buy riskier assets than they would otherwise hold. Policy makers hoped to push asset prices higher, and they seem to have succeeded, notwithstanding the usual debates about how much of the price gains should be attributed to central bankers. (See one of our contributions here and a couple of other papers here.) But whatever the impetus for assets to rise, it’s obvious that they responded. In fact, judging by the data shown in the chart, policy makers could have checked the higher-asset-prices box long ago, and with a King Size Sharpie.

Consider the measure on the vertical axis, percent of personal income. From the risky asset trough in Q1 2009 through Q3 2017, households accumulated asset gains, in real terms, equivalent to 139% of personal income. (Nominal gains were much greater, but we used the CPI to deduct the amount of purchasing power that households lost on their asset holdings. Also, we defined asset holdings as the four biggest categories that the Fed computes gains for—equities, mutual funds, real estate, and pensions.)

In other words, households are enjoying an investment windfall that amounts to nearly sixteen months of personal income, which is larger than the windfalls accrued in any other business cycle since the Fed began tracking asset gains in 1947. Not only that but the gap continues to widen—as of this writing, we’re likely approaching 145% of personal income and well clear of the previous peak of 128% from the 1991–2001 expansion.

Getting back to policy priorities, the chart seems to tell us that asset prices no longer need boosting. The Fed’s pooh-bahs proved they could boss the investment markets, and they’ve almost certainly moved on to new endeavors.

Bull, bear, or donkey?

But record asset gains are just one of the reasons the Fed’s priorities are likely to be changing. To describe another reason, we’ll first show that policy makers may wield a King Size Sharpie but that it’s not a Permanent Marker:

As you can see, our second chart looks like the first, except that we pinned the tails on the asset price donkeys. We tacked on the down halves of each cycle, showing that the portfolio balance channel has a reverse mode.

So what should we make of the result that asset price cycles, adjusted for inflation, have ended with busts that reverse a large portion and often the entirety of the prior booms?

According to our beliefs about how investment markets work, the up and down phases of asset cycles are closely connected. Also, monetary stimulus influences both phases at the same time. It helped fuel the giant gains of recent expansions, but it also helped create the imbalances that led to giant losses. And after the accelerated advances of 2016-17, it’s fair to wonder if today’s imbalances are approaching the extremes of 2000 and 2007. Even some FOMC members are gently acknowledging that risk.

But we think the committee members are even more concerned than you would know by just reading their meeting minutes. We expect financial imbalances to become their biggest worry, bigger than the risk of recession, which should matter less and less to the central bankers’ reputations as the business cycle expansion continues to lengthen. In fact, a garden variety recession would barely affect their legacies at all by mid-2019, when the expansion, if still intact, would become the longest ever. By that time, the FOMC’s greatest reputational threat would be another financial market debacle, which would suggest that manipulating asset prices maybe wasn’t such a good idea, after all. In other words, the committee’s reputational calculus will change significantly during Jerome Powell’s first few years as chairperson.

All that said, Powell probably wants a recession-free economy in, say, his first year or two in the position. Moreover, he’ll certainly stress continuity with his predecessors’ policies. But once he becomes comfortable in the job, the Fed’s priorities will look nothing like they did under Janet Yellen and Ben Bernanke. Instead of fueling asset gains, Powell’s biggest challenge will be containing imbalances connected to prior gains. He and his peers will aim to avoid pinning another oversized tail on the donkey—or at least to manage the fallout from said tail—and that’s a challenge that could very well define his regime.

Learning From The 1980’s

Forget about big hair, Ray-Bans, and Donkey Kong. Don’t even think about Live-Aid, Thriller, and E.T. Above all else, the 1980s were the gravy days of the money supply aggregates.

Beginning in late 1979, the Fed built its policy approach around the aggregates—primarily M1 but occasionally M2, and policy makers also monitored M3 while experimenting with M1B and, later, MZM. But those were just the “official” figures. Economists and pundits debated the Fed’s preferred measures while concocting their own home-brewed variations.

Notably, the Fed allowed interest rates to fluctuate as much as necessary to achieve its money growth targets. Fluctuate they did—rates soared and dipped wildly as a direct result of the Fed’s policy. The world, meanwhile, watched the action as attentively as a Yorkie watches breakfast, studying every wiggle in every M. Missing one wiggle could have meant the difference between exploiting the volatility that the Fed unleashed or being sunk by that same volatility.

And to make sense of it all, the world looked to the most famous economist of his day, Milton Friedman. By converting a large swath of his profession to his strict brand of Monetarism, Friedman more than anyone else had triggered the monetary frenzy.

But then, almost as quickly as the frenzy blew in, it blew right back out. With none of the Ms living up to their billings as economic indicators, the Monetarists drifted from view. Not in five minutes but in five years, give or take a couple, their period of fame was over. Friedman’s reputation as an economics savant fell particularly hard—his highly publicized forecasts proved inaccurate in each year from 1983 to 1986. And the Fed once again redesigned its approach, first deemphasizing and eventually dropping its money growth targets.

But maybe the Monetarists came closer to explaining the economy than their critics allowed?

Maybe the best indicator—I’ll call it “MDuh”—was somehow hidden in plain sight?

Those are the arguments I’ll make in this article, and I’ll back each one with up-to-date data. I’ll propose a way of thinking that’s considered common sense in some circles even as it’s blasphemous within the mainstream core of the economics profession. And I’ll explain why MDuh was the true lesson of Friedman’s research.

Before we get to MDuh, though, there are two things you should know about Friedman and his co-researcher Anna Schwartz (if you didn’t already know them). First, they relied on data, not theory, when they shaped their version of Monetarism. They found a strong historical correlation between money growth and economic activity, and they also found that money growth predicts activity. They published those results in a groundbreaking 1963 book, A Monetary History of the United States, 1867–1960.

Second, to their credit they never claimed to understand the monetary “transmission mechanism,” meaning the reasons the historical correlations were as strong as they were. But they offered their best guess, which lined up with prevailing Monetarist thinking. They believed that “there is a fairly definite real quantity of money that people wish to hold” and that our continual efforts to adjust money holdings to those fairly definite levels are the business cycle’s driving force.  (See here for source.)

The Glaring but Rarely Acknowledged Problem with M1 and M2

The second point above explains why Monetarists defined the aggregates as they did. They defined each aggregate according to the characteristics that might influence the “fairly definite real quantity of money that people wish to hold.” But the characteristics they believed important, such as liquidity, stability, and value as a medium of exchange, led to unreliable indicators, as shown in the chart below:

The chart compares the most popular Monetarist measures, M1 and M2, to two measures that I created, MDuh and NBL. I’ll define MDuh and NBL in just a moment. I’ll first offer an explanation for why M1 and M2 lost their pre-1980s mojo as GDP correlates. And to do that, I’ll need to review a fallacy that underpins not only Monetarism but all of mainstream macro.

Mainstream theory relies on the false premise that bank loans are no different to other loan types. It ignores the reality that bank loans are unique, because banks are the only institutions that create deposits (money) while delivering loan proceeds. Bank borrowers receive money that banks create from thin air, and that brand new money has powerful effects. It boosts spending without requiring prior saving, meaning it’s mostly additive to economic activity. That is, it doesn’t have a large “crowding out” effect on other spending—bank-created money flows directly into nominal GDP. It might affect prices, real growth, or a combination of prices and real growth, depending on how the new money is spent. But it’s important to remember that the new money connects to a bank loan. The money–GDP correlation is merely a byproduct of a lending–GDP correlation. Bank lending, not money, is the driving force.

Back to M1 and M2: Why did those highly touted measures lose their strong correlations to GDP, whereas MDuh didn’t?

I would say it’s because they lost their connections to bank lending. The economists who created them made both additions to and subtractions from bank-created money, whereas I made no such adjustments when I calculated MDuh. I didn’t bother with the differences between checking, savings, and time deposits, and I didn’t bother with money that’s not created by banks, such as money market funds. In other words, I didn’t bother with the characteristics of money that absorb the attention of mainstream economists—liquidity, stability, and value as a medium of exchange. For what it’s worth, I doubt that people maintain definite money holdings, as the Monetarists claimed.

MDuh depends on a single question: Is a potential MDuh component initiated by a private entity with the legal authority to create money, meaning either a commercial bank or a similar deposit-taking institution? If the answer is yes, I include the component in MDuh. Otherwise, I don’t. By using only that criterion, I’m estimating the amount of new money that banks pump into the economy when they make loans and buy securities. Not surprisingly, MDuh correlates almost perfectly with net bank lending—the correlation between 1959 and 2016 was 0.97. And net bank lending, as you might have guessed, is “NBL” in the chart above.

To say it again, banking realities tell us that bank lending, not money, is the business cycle’s driving force, as shown by the data in my chart.

Why Friedman and Schwartz Were Almost “On The Money”

Now for the irony.

Over the 94-year period covered in Friedman and Schwartz’s Monetary History, data only existed for a few types of money. The authors couldn’t separate different types of bank accounts as finely as statisticians do today. They couldn’t measure any non-currency, non-bank-created money that may have existed over the period of study. In other words, they couldn’t add and subtract the various components of the Ms that disconnect them from bank lending.

So MDuh is far from an original measure. It consists of currency in circulation plus bank deposits less bank reserves, which is equivalent to the measure Friedman and Schwartz used in their book for the period until the Fed’s inception in 1913 (there were no central bank–held reserves) and almost equivalent thereafter. Their monetary history could have just as accurately been called “The History of MDuh.” In effect, their study of MDuh triggered the 1980s monetary frenzy in the first place.

(The only discrepancy between MDuh and Friedman–Schwartz is my adjustment for bank reserves, which isolates private sector–supplied credit by excluding deposits that arise though the Fed’s open market operations. Without the adjustment for bank reserves, MDuh would mix apples with oranges. It would combine private sector lending, which is pro-cyclical, with the Fed’s lending, which is intended to be counter-cyclical. Private sector lending is more strongly correlated to GDP, as you would expect.)

In an ideal world, Friedman and Schwartz’s followers would have recognized that MDuh mostly demonstrates the connections between business cycles, inflation, and bank credit cycles. But that’s not what happened. They stuck to their training, which told them that bank loans are identical to other types of lending. And then they obsessed over how to define money supply, as if economic insight comes down to whether to include, say, overnight repos in your favorite M. By so doing, they moved further and further from MDuh.

Next Steps for Those Who See Things as I Do

As mentioned above, my conclusions probably sound like common sense to many of you, even as they conflict with mainstream macro. You might wonder if you can exploit that discrepancy, and I explain how in my book Economics for Independent Thinkers (website here, Amazon link here).

For now, though, I’d say the next time your favorite analyst breaks down M1 or M2, comment politely that those indicators emerged from long-standing fallacies about money and banking. Suggest that maybe people don’t fine-tune their money holdings to a “fairly definite” level as Monetarist theory requires. Or, even if they do, the desired money holdings wouldn’t propel the economy in the same way bank loans do. And then ask her to look at MDuh instead. Or, better yet, ask her to look at net bank lending and be done with it. Money, while occasionally interesting, mostly sows confusion among those who study it.

Author’s note: I plan to post a follow-up or two with more detailed statistics, including correlations with real growth, and I’ll also consider the economics profession’s response to critiques such as mine. The follow-ups are unlikely to be widely published— but they WILL BE published here at Real Investment Advice so check back frequently. Also, for more on the realities of banking and how they differ from textbook theory, see this Bank of England report.