Markets

Here is Why the Dollar is Weak

The so-called ‘Trump reflation trade’ started unraveling before Christmas Day, 2016. As expectations of inflation eroded and the expected path of the Fed’s policy rate became shallower, the dollar began to weaken. By the summer, all concerned agreed that the whole reflation trade had been priced out and then some. The dollar rebounded. But then…it started falling all over again.

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Figure 1. Euro/dollar spot rate. (Source: Federal Reserve.)

This appeared to be a great mystery and generated considerable talk about whether the US Treasury Secretary was talking down the dollar. The FT‘s well-respected market commentator, John Authers, noted,

Has the US dollar stopped making sense? US rates are rising, and a long-run bull market in Treasury bonds seems to be over. This is not happening elsewhere, so the differential between US and European yields has risen to its highest since the euro came into being in 1999. That should mean a rising US dollar and falling US stocks. But US stocks are shooting for the moon and the dollar is tumbling — down 13 per cent from last year’s high on a trade-weighted basis. One retort is that the US has just passed a big tax cut. Of course that raises earnings this year — so buy stocks and sell Treasuries. But it should also be a reason to buy dollars. And stocks that benefit most from the tax cut are doing no better than anyone else this year. This renders the weak dollar the more mystifying.

Matt Klein, one of the sharpest knives in the FT‘s drawer, offered that there was no mystery, it had to do with growth expectations in Europe/RoW rising relative to the United States. This would be the standard (nonfinancial) macro explanation of dollar weakness. As we shall see, he is not entirely wrong. Although the mechanism is not as straightforward as he implies.

In a followup, Authers later shared a comment from a trader that pointed to a massive carry trade underway whereby you borrow dollars to fund euro forwards. According to this trader, there was supermassive 100 basis point (ie, 1 percent) carry in the trade. This, he/she alleged, was the cause of dollar weakness.

I realized that there is a very easy way to check this. Such a carry would only exist if fwd rates deep in the curve were much higher on the continent than in America. Gavyn Davies had already noted the empirical case for this. We can do much, much better than suggestive visual evidence. Indeed, we will see how this can be nailed down mathematically.

We appeal to what’s called uncovered interest rate parity, which says that the home interest rate equals the foreign interest rate plus the expected rate of depreciation of the home currency. It imposes a consistency condition on the euro/dollar spot exchange rate on the one hand and the yield curves prevailing in America and on the continent on the other. Matt is right about changes in expectations about relative growth rates in the United States and the eurozone. What this means is that the US yield curve has become flatter than the German yield curve and that has opened up the carry that Authers’ trader gushed about.

TermSpreads

Figure 2. Term spreads in the United States (30yr minus 2yr) and Germany (15-30yr minus 2-5yr).

The proof is a straightforward calculation of uncovered interest rate parity. Figure 3 displays the expected future exchange rate implied by the yield curves displayed in Figure 2 via the UIP equation as well as the spot rate.

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Figure 3. Uncovered interest rate parity for the euro/dollar exchange rate.

The interpretation is straightforward. Change is relative growth expectations between the United States and Europe led to relative movements in the yield curves which opened up a huge carry trade opportunity. And that massive carry trade put downward pressure on the spot rate for the dollar. Here’s a graph of the carry implied by the yield curves together with the spot exchange rate.

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Figure 4. Carry implied by the yield curves on the continent and in America.

An interesting question that arises then is whether the carry trade consumed so much dealer balance sheet capacity that it precipitated the risk-off that began last week and culminated on Vol Monday. Perhaps that is why US bank stocks (but not European) did so well on Monday despite the volatility shock. If that is the case, we would’ve nailed two birds with one stone.

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Markets

Capital Formation in US Firms

Brenner suggested on these pages that US capital formation has slowed drastically. Is that true? In trying to answer that question, I found the god of modern economics. More precisely, I figured a way to nail down the correct metric for capital formation and that allowed me to measure residual productivity growth; the great unexplained of modern economics. The reason why economics does not have a theory of innovation is that it falls in the social realm; in the specific sense of Matthew Crawford’s ‘ecologies of attention’. I figure that total factor productivity is a function of the vitality of situated communities of knowledge. People engaging together with the machine face incentives endogenous to the situated community centered on the machine. Firms become more productive when—as situated communities of knowledge—they learn better ways of solving their problems and increasing productivity.

Firms increase productivity in two ways: (1) deploying capital in productive assets (machinery and so on) and (2) figuring out better ways of working said assets. In order to estimate the contribution of (2) we have to nail down (1). For (2) is the residual; the portion of variation in productivity unexplained by (1). The best way to nail down (1) is to consider the net (of depreciation) stock of corporate fixed assets since firms can let capital stock erode by not replacing or repairing equipment and structures et cetera. That is, corporate investment spending may not be enough given the variation in depreciation. Instead, what we really need a handle on is capital accumulation. Figure 1 presents the raw series for growth in the net stock of fixed assets of US firms. It suggests a secular decline in US capital formation since 1966.

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Figure 1. Growth in net capital stock. (Source: BEA)

The problem is that much of the variation observed in Figure 1 is an artifact of demographics. Figure 2 shows the scatter plot and series for growth in net stock of corporate fixed assets and growth in prime age population for the United States.

We must control for demographics. There are two ways to factor out the contribution of demographics. (1) Linearly project the variation of one on the other and use the OLS residuals. (2) Look instead at net capital stock per prime age adult. Happily, both yield very much the same dynamical behavior and predict labor productivity equally well. We use (2) because it admits a straightforward interpretation as capital intensity—capital stock per prime age adult. Figure 3 displays our metric for capital formation. We don’t control for real output growth or capacity utilization because of issues concerning endogeniety: Sure, capitalists are investing less because growth is slow but growth is also slow because capitalists are investing less. But assuming that demographics is exogenous is a useful fiction.

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Figure 3. Growth in US capital intensity. (Source: BEA, author’s calculations.)

You can think of the graph displayed in Figure 3 in two ways: as detrended capital formation, or more precisely, as growth in capital intensity defined as the natural log of the ratio of the net stock of corporate fixed assets (chained quantity index) to prime age population. We can see that there were investment booms in the nineties, the fifties and the sixties. The two booms in the late sixties and the late nineties stand out. This observation is strengthened by the dynamical behavior of the stock of equipment and the average age of the equipment. Figure 4 shows the growth of the net stock of equipment per prime age adult. We see that average age falls in investment spurts and rises otherwise.

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Figure 4. Equipment growth and ave age of equipment. (Source: BEA.)

Capital intensity and the average age of equipment are good predictors of labor productivity growth. Together they predict two-fifths of the variation in labor productivity growth. Figure 5 displays the scatter plot.

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Figure 5. Capital formation and age of equipment predict labor productivity. (Source: BEA, author’s calculations.)

Figure 6 displays the 5-year moving average of labor productivity growth orthogonal to lagged growth in capital formation and average age of equipment. That is, we project labor productivity growth on lagged capital formation and change in average age of equipment, and report the OLS residuals. The origin of the Y axis has been moved to 1 for ease of interpretation. (Excel and area graphs; don’t ask.) Labor productivity orthogonal to capital formation and technological shocks (captured by age of equipment) is residual productivity attributable to gains in knowhow. The portion of growth not explained by capital formation (including the technology embedded in new equipment/machinery). It is thus a finer measure than TFP.

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Figure 6. An alternate measure of total factor productivity: US labor productivity growth orthogonal to capital formation and age of equipment.

There have been two spurts in underlying productivity by our metric. A big one in the early sixties and a medium-scale one in the late-90s and early-2000s. The Sixties’ Boom stands out prominently. What explains the productivity miracle of the 1960s? That’s the big explanandum thrown up by the present study.

To gather our findings together: Looking at investment ignores depreciation. Looking at growth in net capital stock suggests a secular decline since the mid-1960s. But that is in fact an artifact of demographic shocks. Looking at the ratio of net capital stock to prime age population controls for these demographic shocks. We find two prominent investment booms in the sixties and nineties. Perhaps not coincidentally, these two periods are also times of significantly positive multi-year pure productivity shocks.

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Figure 7. The balance between the three main blocs of US corporations.

The sixties were a period of unabashed hegemony of the Chandlerian firms. Manufacturing corporations accounted for more than half of all corporate profit in 1967-1969. See Figure 7. We must ask a more precise question than what’s special about the sixties. We must ask, What was going on in these Chandlerian firms that was specific to the sixties? or that was premised on conditions that prevailed only in the sixties? Did it have something to do with the passage of the reins of power to the ‘organized intelligence’ running these great corporations, pace Galbraith? seen as ‘ecologies of attention’, pace Crawford? In other words, could it be that corporate freedom from ‘shareholder value’ empowered the engineers running these corporations and allowed them to solve problems faster, thereby increasing effective knowhow? Or was it the flowering of the ‘corporate-liberal synthesis’? or maybe even the full flowering of ‘Fordism’ and ‘the Treaty of Detroit’? What the hell was going on in the sixties?

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Markets

Cross-Border Banking Flows as a Metric for the Global Financial Cycle

Perhaps Nomi Prins did not choose the title of her piece “The next financial crisis will be worse than the last.” But the idea that there is going to be another financial crisis in the center of the world economy in the near term even vaguely comparable in virulence to the GFC has, as we shall see, no basis in reality. The reason is straightforward. Financial crises are denouements of credit booms, not asset price booms—all credit booms are attended by asset price booms but not the other way around—and while there is certainly an asset price boom in global markets, there is no credit boom at the center of the world economy. The chances of a banking crisis are even more remote than credit indicators suggest because of the extraordinary surveillance of the balance sheets of global banks since the GFC. Even if all legal-regulatory innovations over the past decade—especially the tighter limits on capital ratios—are bull, the sheer fact of enhanced regulatory and independent balance sheet surveillance means that banks find it much more difficult to hide risks on and off their balance sheets.

We now have a good handle on the mechanics of financial booms and crises. Financial booms are banking expansions. Banks are special because, yes, they create money by lending. But do not let the Bank of England distract you. The issue is that the excess elasticity of bank balance sheets mechanically generates lending booms since bank assets are the liabilities of non-banks. As a rule, credit booms emerge from the mutually-reinforcing interaction of property prices and bank lending. As collateral values go up, more can be lent against the same property; in turn, greater lending pushes up property prices further. Credit booms show up in credit gap measures such as credit-to-GDP ratios. We also understand how credit booms end. The stock of outstanding debt lags behind credit gaps. Once the debt burden, which is a function of the stock of outstanding debt not credit growth, becomes intolerable, credit defaults puncture the boom and precipitate a financial crisis. That’s why the best predictors of financial crises are credit gaps and debt ratios.

Metropolitan banking is international. As the day progresses, the trading book of global banks passes from Hong Kong to London to New York. The transatlantic circuit is especially important. The mid-2000s financial boom was driven in large part by a transatlantic, European banking glut. In other words, there is good reason to believe that cross-border banking flows are a especially good barometer of the global financial cycle. I therefore decided to analyze the JEDH database on cross-border banking and debt flows.

I’ll probably have much more to report later. But here’s the basic picture. Figure 1 displays three variables; all standardized to have mean 0 and variance 1. “CoreFPC” is the first principal component of the cross-border flows of Japan, Germany, France, Italy, Netherlands, Norway, Sweden, Denmark, and Finland. Roughly speaking, it captures the common variation in the series. “G2” is the sum of the cross-border flows of the United Kingdom and the United States. “China” is the sum of the cross-border flows of China, Hong Kong, and Macau.

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Figure 1. Cross-border banking flows.

A few observations are in order. The tight coupling of Anglo-Saxon finance (“G2”) and the rest of the core (“coreFPC”) is manifest; thus allowing us to interpret either as providing a fair metric for the global financial cycle. We will use the former because (a) it is a tighter, more parsimonious definition; (b) it is applicable in more general settings in the sense that we can extend many macrofinancial variables back to the 19th century without changing our center countries. Hélène Rey’s notion of the global financial cycle—as the covariation of risk premia embedded in global asset prices—is less relevant to macrofinancial stability than our metric. Although banking expansions can be read off of asset prices, it is a noisier metric precisely because not all asset price booms are attended by real financial booms that end in tears. Cross-border banking flows provide a finer measure of banking gluts than the compression of risk premia because all lending booms are attended by cross-border banking flows and vice-versa.

This metric confirms what credit gaps and debt service ratios can tell us about the buildup of financial imbalances in the center of the world economy. Interestingly, by this metric the Chinese cycle seems to have turned. This was not clear when we looked the credit gap (Figure 2).

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Figure 2. China’s macrofinancial vitals.

Finally, as a sanity check we look at the bond market. The slope of the yield curve is the best predictor of US recessions out there. When the yield curve inverts it heralds a recession in the near term. There is good reason for this. The inversion of the yield curve destroys banks’ net interest margins; forward-looking measures of banks’ net worth fall; banks respond by shedding assets; finally, the attendant fall in bank lending pushes the macroeconomy into recession—this is Adrian and Shin‘s risk-taking channel of monetary policy. The term spread has indeed compressed, but the yield curve is still upward-sloping.

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Figure 3. The term spread.

Because the current asset price boom is unattended by a credit boom in the center of the world economy, the possibility of a financial crisis comparable to the GFC is remote. And despite the “age” of the expansion, a normal recession does not yet seem to be on the cards either.

 

 

 

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The President and the Stock Market

Authers’ Note today sounded downright exasperated with the President’s tweets. Trump claimed credit for the fastest 1,000 point gain in the Dow’s history:

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That’s too cute by half. Each 1000 percent gain gets mathematically smaller as the Dow goes up; eg, the move from 10,000 to 11,000 is a 10 percent move whereas that from 24,000 to 25,000 is just 4 percent. Properly compared, the performance of the Dow under Trump lies between equivalent periods in Obama’s two terms.

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But the Dow is a stupid aggregate of stocks for the simple reason that it is price-weighted. “Over the past year,” Auther notes, “two great industrial giants have been the alpha and omega of the Dow: Boeing has doubled, gaining $85bn in market cap, while GE has collapsed by more than 40 per cent, shedding a staggering $118bn. But due to the ridiculous way in which the Dow is calculated, Boeing accounts for a rise in the Dow of 1,064 points, while GE accounts for a fall of only 84 points.” That’s because Boeing has 600 million shares outstanding valued at $309 each for total market cap of $184 billion; while GE has 8.7 billion shares worth just $18.5 each for a total of $161 billion. The differential sensitivity of the index is an artifact of the practically irrelevant question of how many shares the firms have outstanding.

If we ignore the Dow and look, as all serious investors and analysts do, at the S&P500, even Obama’s 1st year in office comes out ahead of Trump’s.

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In a more longer term perspective, the Trump rally hardly stands out either.

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More generally, Presidents have little influence over the stock market; central banks much more so. But we should not even exaggerate the influence of the latter. Stock valuations are sky-high not just because of monetary accommodation but mostly because the near-term global macroeconomic outlook is especially positive for risk assets. Not only is global growth more robust than it has been in a decade; there is little sign of inflation, which implies that there is little incentive for central banks to take away the punch bowl any time soon. It is this benign macro environment that is compressing market-wide risk premia.

Investors are facing two main risks in the near term. The first is that growth may falter and cash flow expectations may need to be marked down, with attendant corrections in asset valuations. In the extreme, the economy may even plunge into a recession; although that scenario seems unlikely in the near term—the yield curve may be shallower than before but it is still sloping upwards. The second is that inflation might finally show up, forcing the Fed to hike much faster than anticipated, thereby precipitating a risk-off. In other words, markets have been in a sort of Goldilocks Zone. Either a significant deceleration in global growth or a significant acceleration that eliminates global slack can precipitate a sell-off.

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Source: US Treasury.

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Markets

Does Consumer Confidence Predict Output Growth?

Tip of the hat to Ted Fertik for flagging Mian, Sufi and Khoshkhou (2017). The authors examine the role that partisan bias plays in consumer expectations. One of their findings is that consumer confidence is extraordinarily high among Trump supporters. But that this has not translated so far into higher spending.

I was skeptical of the notion that consumer confidence—as opposed to CEO confidence, investor confidence, and the risk appetite of intermediaries—had discernible and predictable effects on real economic activity. So I examined the data. Turns out that it does. And that it does so through a clear channel: Consumer confidence predicts discretionary consumer spending which in turn is a strong correlate of real GDP (RGDP) growth.

The evidence can be read off Figure 1. Read clockwise from top-left. (1) University of Michigan’s Index of Consumer Sentiment (MCSI) predicts RGDP growth even after controlling for lagged RGDP. (2) MCSI predicts growth in real discretionary consumer spending. (3) MCSI does not predict RGDP growth shocks orthogonal to discretionary consumer spending. That is, the residuals obtained by projecting change in log RGDP onto change in log consumer spending are not correlated with lagged consumer spending. (4) Discretionary consumer spending is contemporaneously correlated with growth in RGDP.

Channel

Figure 1. The empirical evidence for the confidence channel. The data is at the quarterly frequency and for the period 1960Q1-2017Q3. Source: Haver Analytics.

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Markets

Was the Great Recession a Natural Calamity?

Tip of the hat to Adam Tooze for flagging Annie Lowry’s excellent round-up of the Great Recession’s impact on American society. After ‘the economy tipped into the deepest contraction of the post–World War II era’, Lowry writes, ‘the Great Recession’s scars remain’. The recession exacerbated troubling movements already underway: erosion of middle-skilled jobs, vertical polarization of the labor market, decline in labor market participation, economic insecurity, racial polarization, vertical polarization, regional polarization, and the opioid crisis. ‘A sicker, more unequal, more racially divided country: This is the legacy of the Great Recession.’ Her conclusions are worth quoting in full and bring the framing into sharp relief.

When the next recession comes the data on what to do about it will be there. Economists have pulled together plenty of studies of the dollar-for-dollar effectiveness of initiatives like extending unemployment insurance and increasing the size of the food-stamp programs, and the relative ineffectiveness of things like corporate tax cuts. Social scientists, social workers, and local officials have urged the importance of acting as quickly as possible to intervene, with efforts to stabilize financial markets, increase the deficit, and make monetary policy more accommodative. The country has now gone through three consecutive jobless recoveries, with downturns tending to amplify long-existing trend to hollow out the middle class, polarize the labor market, and hit already ailing regions hard. It seems likely that the next recession will do much the same.

The question is whether policymakers will take such evidence of the pain and scars left by the Great Recession into account. Congress is today on the verge of pushing forward a tax cut aimed at rich families and profitable corporations that will add more than a trillion dollars to the debt, with no real need for new economic stimulus at the moment. Meanwhile, it has declined to do much for the poorer families that are still feeling the worst effects of the last recession and have not yet recovered. The risk is that next time, they will get left even further behind.

In short, recessions are naturally-occurring calamities like hurricanes or earthquakes. The policy questions they raise are about the effectiveness of various measures to deal with them. Macroeconomic studies provide insights into the shock; microeconomic studies allow us to explore the impact of the shock on different markets and social groups. We have learned a lot since 2008 and these acquired knowledges should be brought to bear ‘the next time’. A rational policy framework must incorporate all these insights to fight the next one. This is not Lowry’s personal frame of reference. This is the dominant frame used by economists and laypeople to think about the Great Recession.

All frames are necessarily partial; they illuminate some aspects of reality and leave others in the dark. The recessions as natural calamities frame is especially problematic. For the scale and virulence of the Great Recession was not the result of a random draw; nor was it independent of economic policies pursued. The scale and virulence of the Great Recession was due above all to the unprecedented amplitude of the financial cycle. The recessions as natural calamities frame leaves out the most important policy lesson to be learned from the catastrophe: financial booms are extremely dangerous and must be tamped down vigorously. The principal policy failure did not occur in 2009-2010; it occurred in 2004-2006. Policymakers and regulators failed to appreciate the build-up of great financial imbalances. And that failure led directly to the catastrophe of the Great Recession.

US financial cycle

Source: Claudio Borio.

 

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Markets

Markets Celebrate the GOP’s Trillion Dollar Giveaway to the Rich

So you thought fiscal hawks were serious about the US deficit? Haha. Despite last minute snags the Republicans are expected to push through a trillion dollar tax-cut for their paymasters. One is told by Very Serious People that markets, bond vigilantes in particular, punish fiscal profligacy by demanding higher costs of borrowing. That’s poppycock. Both stocks and bonds are booming for good reason. The tax cut delivers a large, positive wealth shock to investors and the market is repricing to reflect investors’ greater appetite for risk. The bond market refuses to believe that the tax cut—a major fiscal shock when the economy is at full employment—will have much of an effect on economic activity. For the compression of the term spread means that the market expects subdued inflation and a shallow path of policy rates. Were a major pickup in economic activity around the corner, the yield curve would become steeper not shallower. So the euphoria in the stock markets is due not to expectations that animal spirits unleashed by the tax cut and regulatory “reform” would deliver higher growth. Rather it is due to the straightforward transfer of resources from the public sector to corporations and investors. What the market is celebrating in other words, is not the expected growth of the pie but redistribution of the pie in its favor.

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