Sell Off Thoughts

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Here are my thoughts on the current sell-off. On overbought conditions alone we should sell off further from here. If I had to put a rough number on it, totally guessing, I would say another 3 to 5%. But there are three risks that could turbo charge this into a bigger sell-off.

The first is the return of the virus story that reverses reopening steps–not just slows down phased implementation. We all see a decent number of states now where hospitalization is still increasing and the probability of some of them going ‘epidemic’ is growing rapidly. It’s already becoming part of the market narrative, now risk markets have started to sell off. Some states have already started to slow down their phased reopening plans. If we get to the point where states have to start outright reversing elements of their reopenings, I think the PTSD from the last sell off would kick in pretty quickly and would likely turn an oversold correction into something bigger.

The second risk is that the economy is not sufficiently reopened when the fiscal assistance starts rolling off. This is a bit of a foot race. I’ve been impressed by the speed and Ingenuity with which the private sector has figured out ways to get back to work. but the fiscal assistance was pretty massive and parts of it will start rolling off fairly soon. If enough people haven’t found, or haven’t returned to, work by that time, this will start showing up in the data market will likely react to it. We’re not quite at that point yet, but it could come at any time this summer.

The third risk is if Trump. If he continues to slip in the polls and in popularity, if he increasingly feels cornered, it is likely he will do increasingly risky and desperate things. He has control over a lot of powerful levers, and I think by now it’s not too political to use as the prior that he will do as much to help himself as he thinks he can get away with. Based on what we’ve seen thus far in his presidency, it’s no longer a given that the system or GOP senators will stop him. It may still be likely, but it is not a given. In short, there’s little basis for believing Trump wouldn’t at least try, which, if dramatic enough, could roil the markets. For example if he tries to invoke the military in any way, or if he starts to lay the political predicate for rejecting the election results.

Of these three risks I think the most important and likely in the near term is the first one. If we start to see states not just delay but actually start to reverse steps towards reopening their economies, hard to see in the current set up how that wouldn’t be a market catalyst for a deeper leg down. This is the narrative evolution I will be watching for.

Asset shortages and Income Inequality

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The bare bones thesis is that asset shortages exist and are largely driven by income inequality. And by ‘assets’ I mean both safe and risky assets. I’ve been thinking and tweeting about these issues for at least 7 years. And as I have, the linkages have become increasingly clear to me. But they’re complex and hard to boil down into the simple blog posts that I prefer. It’s probably why I have held off on trying to lay this out for so long. Nonetheless, here’s the simple version:

We had a phenomenal 30-year period of global growth, much of it turbocharged by demography, deregulation (finance and trade), and a massive global credit boom. Two industries drove the boom more than the rest: finance and technology.

These industries have an unusual feature in common: massively increasing returns to scale. To oversimplify, it used to be that to expand production you had to build a factory and hire a number of people roughly proportional to the desired increase in output, and this is manifestly not the case in much of technology and finance. Companies like Facebook, Google etc. have a marginal product of 1s and 0s that costs basically zero. In finance, the due diligence you need to do on a $10 million loan and on a $100 million loan is the same, but the fees you collect are ten-fold larger. Asset management has similar scaling.

The result is huge margins and profits that accrued to a very small number of people, producing vast fortunes. This, more than tax policy or Fed policy rates, drove the income and wealth inequalities that characterized the past 30 years—starting long before we could even spell QE or before embarking on the past 20 years of tax cuts.

The other reason we know it’s not primarily policy driven is that it’s been a global phenomenon. Yes, in the US, it’s 100% true that we have had policies that favor capital over labor—including a less progressive income tax code. And these polices have no doubt exacerbated the income and wealth disparities here. But it’s hard to argue these policies have driven the global phenomenon of increased inequalities within countries and decreased inequality across countries when we haven’t had common policies. What we have had in common, though, mutatis mutandis, has been an economic transformation driven by inequality producing technology and finance.

How do global income inequalities matter to the Asset Shortage theory?

If you think about ‘asset production’ as being correlated to GDP, it is easy to recognize that if income (GDP) is evenly distributed, the aggregate marginal propensity to consume (MPC) is higher and the marginal propensity to save (MPS) is lower. If unevenly distributed, wealthier people will consume less of their income and save more. Lower MPC and higher MPS translate into higher demand for financial assets for a given level of aggregate income. And, remember, we’re talking global, not just the US.

While this may be the most important driver of the Asset Shortage, to be more complete, it isn’t the only one. Central banks have contributed to the demand for assets—especially safe assets. Corporate buybacks have contributed at the margin too. More significantly, global financialization has produced insurance companies, pension funds and asset managers in countries where they didn’t exist 30 years ago, and they have grown massively in countries where they already did, increasing the demand for assets to match future liabilities and mobilizing the excess savings of the wealthy. If you take all these factors together, it makes—at least to me–a pretty compelling case for the Asset Shortage thesis.

All Look Same

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A while back there was a Japanese-American who, to amuse his Polish-American girlfriend IIRC, made a tongue-in-cheek website game named All Look Same. The game was to correctly identify the nationalities of random Japanese, Koreans and Chinese from pictures of their faces. Turned out to be pretty hard.

Right now the game is analyzing charts that, at first glance, might all look the same. Scanning them this weekend led me to believe more strongly that the weight of the evidence is leaning hard in the direction of a risk sell off. I don’t have a view as to how long it might last or how far things like the S&P index might fall, I just see that it is, on balance, a terrible risk reward right now to try and make money on the long side. It also looks like the dollar could breakout against almost all currencies. The only ambiguity I see is with respect to gold and the euro. But if a selloff gains enough speed or length, I would be shocked if these two didn’t get dragged down too.

It’s true that investors are still negative and for the most part under-invested. It’s also true that in my view there’s an asset shortage that goes well beyond high quality bonds. These factors make it difficult to get aggressively short for any length of time. But I do think, at a minimum, there has been a decent amount of FOMO buying in the last few weeks that could be shaken out. If the data and/or earnings in the aggregate turn out to worse than expected (not a low bar with the current level of negativity), or investors come to believe the economic hold down will last longer than now thought, a sell off could go beyond a simple FOMO shakeout.

Here are the charts I’m looking at for leading clues about a selloff. Most of them have a link to commodities, often a leading vehicle of expression for global growth pessimism.

One year chart of SPX futs
One year chart of EEM
One year chart of Copper futs
One year chart of Silver futs
One year chart of Platinum futs
One year Palladium futs
One year chart of spot AUD
One year chart of spot USDCAD
One year chart of spot USDCNH (offshore USDCNY)

All of these charts (with the exception of USDCNH) show variants of a WAB (weak-ass bounce) that’s rolling over. USDCAD is obviously inverted, and USDCNH doesn’t quite fit the pattern because it’s managed against a basket. But even the USDCNH pattern shows a clear likelihood IMO of a continuation up move.

It’s also worth pointing out that the angle and magnitude of the bounce can be used to interpret relative strength. For example, the bounces in EEM and Copper aren’t as steep and didn’t retrace as much as, say SPX and AUD. This might not matter much if you are looking at short term trading. For that, it’s probably more important to try to identify where the recent buying has been heaviest and which assets feel most crowed. This criterion tends to work best in the initial phase of a sell off. However, if you are thinking about longer term positioning, shorting the weaker asset tends to be the best way to go.

Good luck.

More Bounce or More Bust?

I’ve found over the years that not only does market analysis not need to be complex, but complexity often makes things worse. Our propensity to overthink is as dangerous as it is hard to resist.

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The joke at my old firm when I would get something wrong, or lament about having to stop out of a trade, was “you’ve got to get a lot dumber if you want to be a really good trader.” And, like most effective humor, there was more than a kernel of truth to it. My fundamental background made it very hard for me to avoid getting lost in the weeds. I’d typically either be putting too much weight on what were really second/third order effects or applying longer-term analysis to a much shorter-term trade horizon.

These days, I feel like I’m finally dumb enough to be a decent trader. As many of you have probably noticed, I try hard to keep things really, really simple. I focus on sentiment swings, positioning, and plausible narratives. I then match these thoughts up against chart and correlation patterns. What follows is what I’m seeing now.

One, negativity is still high. The Fear Greed index is only up to at 43, the VIX is above 40, and most investors missed this trade up and are seething. Currencies that sold off hard in the risk off move in March have not sufficiently reverted, in my view, based on the chart patterns, and a weak dollar narrative seems to still be building. All of these factors are positives. As a max, I could see the $ES_F (SPX front contract) get up into the red area on the chart below. I don’t think we can go beyond that in the near term without a burst of optimism from some sort of scientific breakthrough.

Front contract SPX
VIX
Dollar index BBDXY

But that also doesn’t mean we have to get up into that area at all. We’ve had a vicious rally—the kind one typically sees only in bear markets, making bullishness a much tougher call here than it was even a week ago. Yes, market participants are still very negative, but the payoff asymmetry has also become less attractive. And the dojis we saw on the charts Friday, after the surprise Fed announcement, are frankly not an encouraging sign. Moreover, there would be plenty of fundamental developments to build a plausible narrative with if risk assets started to sell off, especially as the Fed policy high of last week fades and the potential for a congressional standoff on further measures grows.

Fundamentally, as many have pointed out by now, it’s not about the amplitude of the initial economic shock, it’s about the prospect of its duration. In this regard, I don’t think the economic data we’re going to see over the next two or three weeks will shed much light on the question. But, again, there will be plenty of raw material on the data front available to feed a bearish narrative if the price action starts to turn sour.

On the virus front, the news is terrible, but terrible is now increasing at a slower pace, and we’re getting somewhat used to our unpleasant new normal. But the evidence trickling in from other countries’ experiences is that there will be subsequent waves to deal with here in the US that will persist until science and thoughtful federal planning can get us back to work safely. And we don’t have much to go on yet that tells us how effective the economic policy response is going to be once we do start getting back to work.

For me, what this meant in the Trading book is I have cut the size of my equity index long and will move up my stop on the remainder. I will be looking at other assets like silver, cooper and some of the bounce leaders for signs the rally is starting to die.

By late last week I had already shifted the bulk of my risk exposure to a dollar reversion trade. I will try and ride these currency positions as long as the weak dollar narrative remains a theme (EUR, MXN). On the Investment side, I will trim a beta position or two this coming week but will try to stick with the things like the homebuilders I added recently for the longer term. I hope the market will let me hold onto them.

The Anatomy of a Sell Off: The Three Phases

Each sell off is different and there are no magic formulas to tell you when to ‘get back in’ or if it’s ‘too late to sell’, but there are some things to look for that help improve your odds.

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Typically, selloffs start because we get too optimistic and over positioned and some kind of catalyst makes us realize that maybe we have too much risk on. Sometime, it can be a catalyst that has hidden in plain sight for a while before we belatedly overreact, and sometime they truly come out of the blue. Obviously, the more extreme the optimism and positioning, the smaller the trigger (or set of triggers) it takes to unleash the selling.

Once the selling starts, it almost invariably shows up in the sectors and assets where the optimism was most vigorously expressed. In this case, that would be large cap tech. The selling can be sudden and severe. These names and sectors underperform (vol adjusted) pretty much everything else. This is what I call Phase I.

As the selling intensifies and the P&L pain starts to mount, the selling spreads into other areas and starts to slow in the sectors/names that got hit so hard in Phase I. The names and sectors that outperformed in Phase One effectively ‘catch down’ with the names that were at the center of the storm. In this case, you would start to see the S&P underperform the NASDAQ 100 (as we saw yesterday). This would be Phase II.

In Phase III, you typically see the hedges that you hastily slapped on late in Phase I stop working, even as the names/sectors/assets you held on to continue to get liquidated and go against you. Also, the indices start to get squeezy and jumpy, as more people finish their de-risking and their anxiety shifts to being caught out in or left out of a market rebound. But because in this phase there are still people who haven’t finished se risking, and because some of us get greedier/more confident on the short side (just like on the upside, success breeds overconfidence), investors and traders sell hard into those jumps and squeezes, making the intraday tape more two-way volatile.

We don’t always get the full three Phases in every selloff, and the elements above aren’t set in stone. But for the larger selloffs, and using a ‘weight of the evidence’ approach, this little roadmap can help identify where we are in the process and how hard we should be pushing our tactical bets. And, at a bare minimum, it’s a great check on our overconfidence.



When Is A Basing Pattern Buyable? Three Live Examples

I get asked often about how and when to buy assets that have taken a beating. My usual response is “buy after you see some kind of basing pattern.” Below I give three current examples of what I mean.

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GE

Here are the five-year and one-year charts of GE. We all know the story: balance sheet overreach, fall from grace, flirt with bankruptcy, change in management, multiple asset sales, and now slow repair of the balance sheet.

The five-year chart pattern matches up nicely. We see progressively higher lows since late 2018, and a break out last October that appears to be consolidating into some kind of bullish flag.

Five-year weekly chart of GE

GE will never be the company it once was, but one could easily imagine a narrative that takes it back to 20, once it gets through the residual overhead resistance between 12-14 we can see back in mid-2018. Some who caught this knife too early back then may be looking to get out once ‘back to even’.

On the one-year chart, GE is basically at 52-week highs, with a good base having been built between February and July of this year in the 10-10.50 area. In the short term, however, GE could pull back as far as 10.00 without it being worrisome.

One-year daily chart of GE

HOV

Another balance sheet repair story that is showing a solid bottoming process is the home builder HOV. They re-levered and bought land banks too soon after the GFC and almost lost the company. Little by little, they too have repaired the balance sheet, bankruptcy is off the table, and they are just now returning to topline growth. The is the narrative, and it’s a pretty compelling one.

The tailwind to the sector is likely to last as well. Rates will stay some version of low for the foreseeable future, a true recession is unlikely for the next year, and supply from other home builders has been running scared for a decade.

You can see the scope for upside in the five-year chart. The basing pattern isn’t as clean on this one as it was with GE, but the pick up in bullish volume since the second half of this year is very convincing. This is one that could run to 40 pretty quickly and eventually to 50 or 60. You also would want to track the bond yield on this one (five-year CDS would do) to make sure the ‘normalization’ trend remains intact.

Five-year weekly chart of HOV
One-year daily chart of five-year CDS on HOV

On the one year-chart you see another stock not far off its 52-week high, but with near-term scope to pull back further. I actually don’t expect it to get below 20 in this pullback, but if it did, I wouldn’t be worried about the overall pattern until it got to 15.

One-year daily chart of HOV

UBS

The final example is UBS. The five-year chart shows what looks like a bottoming process over the course of 2019, with higher lows since August. It too, like virtually the entire European banking, is laboring under the presumption of a bloated balance sheet and anemic growth. The pattern, in any event, suggests it might be ready to break out of that funk.

Five-year weekly chart of UBS

The one-year chart on UBS looks less stretched than GE or HOV. Traders would probably put their stop just below the 50dma, while an investor could let it pull back as far as 11.00 without being too worried. There is less upside in this name, though, so less payoff asymmetry. Another thing to be mindful of when sizing and setting stops.

One-year daily chart of UBS

It’s easy to imagine fallen angels like these getting a bid in a world where catching-up-without-too-much-downside-exposure is active manager catnip. And whenever you can get a narrative to line up with a solid chart pattern, the odds of it being a big winner increase significantly.

Repo Made Simple

The flare up in the repo market back in September caught a lot of people off guard—including the Federal Reserve. The issue area is arcane and complex, and I’m not the best expert, but I think I understand it enough to try and lay out what happened and why.

Since there’s a non-zero chance this issue flares up again around year-end, it’s important to have an understanding of the underlying issues—if for no other reason than to see more quickly thru the bullshit.

There are two main types of change post-GFC that led the desired amount of excess reserves to be much higher than anyone had anticipated.

One, volatility in reserves increased dramatically, for two reasons:

  1. For various reasons related to the GFC, the Fed took actions that allowed the Treasury to keep its all of its money at the Fed, whereas before the Treasury kept almost all of its money outside of the Fed Funds system, in banks. And since one dollar of Fed funds in the Treasury account reduces reserves available to others by one dollar, this, as the Treasury built up and drew down on its account, led to much greater volatility in the excess reserves available to banks.
  2. The Fed during the crisis removed the cap to and restrictions on foreign banks participating in the repo market. Oscillations in their demand for repo operations also added volatility to the amount of available reserves.

If the volatility in available reserves is higher, it becomes optimal for banks to keep a larger cushion of them.

The second type of structural change was behavioral: the preference for reserves over Treasuries (as an HQLA) shifted significantly.

  1. Reserves settle t+0, while Treasuries settle t+1. You can always meet a large unexpected demand for settlement with reserves, but not always with Treasuries.
  2. Reserves now pay interest, and do so at a rate of interest roughly equal to T-Bills, so there is no monetary opportunity cost to holding reserves instead of Treasuries.
  3. The stigma of having to go to the discount window increased sharply post-GFC. (If you get caught short of reserves and need to make same day payment, the discount window is your only option.)
  4. PTSD and the conveyed expectations of the bank supervisors leads strongly to erring on the side of caution.
  5. Capital charge on lending in repo is the same as for unsecured bank lending under bank leverage rules, which further inhibited the cautious banks from trying to take advantage when the repo rate spiked from 2 to 10 percent.

This was the reserve backdrop going into September. And then the repo market got hit with a two-sided shock.

On one side, the Treasury issued an unusually large slug of bonds, which primary dealers had to finance in the repo market. This increased the demand to borrow thru repo.

On the other side, corporations around the same date had to make large estimated tax payments, the funds for which they needed to withdrawal from the overnight market, a market which lends heavily via repos. So, we ended up with a positive demand shock and a negative supply shock. And this happened against a backdrop of cautious banks with a strong preference for reserves over treasuries.

The bottom line is there are plenty of excess reserves in the system for settlement purposes, but the banks have been extremely reluctant to part with them for the structural and behavioral reasons given above. This creates a sense of hoarding. The Fed now understands this and they are working with the banks to figure out ways to reduce the volatility in available reserves and loosen some of the de facto and de jure factors leading to reserve hoarding.

Bitcoin: FOMO, Patterns, and Where do we go from here?

Even for those not involved, it was hard to miss the dramatic reversal in bitcoin on Friday, ostensibly on news that Chinese president Xi Jinping expressed support for the development of blockchain technology. (China banned bitcoin and cryptocurrency exchanges in 2017).

Sentiment had also gotten bearish on a fairly rapid fall from 10k to 7.5k, and bitcoin trading volume on the ten top exchanges had reportedly fallen from $4 billion per day a few months ago to under $200 million a day last week. And the chart pattern looked like bitcoin was poised to fall further, continuing the “echo bubble” dynamic.

On Friday the price jumped from 7500 to 7600 at about 6:45EST and then a few hours later spiked sharply from 7700 to 8500 in less than 10 minutes. Friday evening (again, EST) prices jumped one more time from 8500 up to 10400, before trading in a range between 8800 and 10000 since. It’s roughly 3pm EST right now and bitcoin is quoted at 9660 on Coinbase.

What do the charts now say to do, based on this price action?

If you are fundamentally bullish bitcoin, I get that the FOMO here is irresistible. I don’t know how a bitcoin maximalist could remain on the sidelines or not buy more. But that doesn’t mean that you should.

Trading based on chart patterns doesn’t work like that. We use patterns precisely because they help us protect ourselves from emotions.  After a large selloff or downtrend there is typically a lot of technical damage that has to be repaired before you can get a safe entry point for a longer-term position. One large pop doesn’t do it.

There are two specific things to pay attention to: building a base and overhead resistance

An excellent example of “building a base” after a large decline is what we saw from December 2018 until March 2019. In fact, it was this base, with the requisite pattern of progressively higher lows and a quieting down/tightening up of the price action is what turned me technically bullish in late March.

I can’t get bullish on the chart until I see some kind base built and a sense that the technical/psychological damage has been repaired—even if I were fundamentally bullish the underlying asset.

The other problem is overhead resistance. From the charts above you can see that the support in the 9500-10000 area that was built from June through September has now become overhead resistance. The old saying is support that breaks becomes resistance, and resistance that breaks becomes support. This is a good example of that.

Because no base has been built and we are almost exactly at significant overhead resistance right now, the chart pattern is not giving you a buy signal. Is this a guarantee the price of bitcoin won’t continue going straight up from here? Of course not. It just tells you that the current chart pattern doesn’t put the odds in your favor. And playing the chart patterns has worked out unusually well in this asset for as long as I have been tracking it. This admittedly gets tougher and nosier now that volumes are considerably lighter and the bitcoin market is easier to push around in the near term, but typically this is noise that should influence your position sizing but not whether bitcoin ends up being a success or not.

If, however, you are a fundamental bitcoin bull and the FOMO is absolutely killing you (this happens to all of us), and you need to be involved, just be ultraconservative in your sizing. The general rules are 1) the closer you are to a natural stop on the charts, the larger the position you can afford to risk, and 2) the lower the volatility of the asset the larger position you can afford to risk. Given the magnitude of the pop, there are no natural stops for longs nearby right now, and volatility & gap risk are likely to remain quite high.

If the chart is saying bitcoin is not a good risk/reward buy here, does that mean it is a short? The basic answer there is also no.

The pattern of stair stepping down, consolidating, then breaking again to lower lows has ended. If you are bearish bitcoin and looking for an entry point to get structurally short, the odds aren’t great here either. The only thing working in your favor is that the overhead resistance offers a degree of protection.

If you are looking to trade it, you can also short price spikes up into resistance, but here too the sizing has to be more cautious because of the pick up in volatility, and you have to set a pretty tight stop and/or babysit the position closely.

Good luck.

My Performance Numbers

My career path has not been a common one. Not many policy economists take a wrong turn into finance, and those who do, do so as analysts, strategists, or pundits. Then they stay there. Very few cross over into front-line risk taker/portfolio manager—and those who try tend not to survive. I was repeatedly told economists are rarely good investors and could never be good traders.

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I saw this as a great challenge—like learning to shoot a basketball with your off hand (which, back when I was playing basketball, wasn’t done). Plus, putting your money where your mouth also seemed like fun—a lot more fun than writing reports, making recommendations, and being a portfolio manager’s scapegoat. So, when given the chance to transition into actually managing money, I jumped at it.

Over the years I went from mutual fund sovereign analyst, to mutual fund portfolio manager, to senior risk taker in a trading-oriented hedge fund. Along the way I figured out why economists tend to be bad risk takers, and, most importantly, I discovered the primacy of risk management. I also got to know myself, my risk tolerances, natural biases, and how similar my cognitive shortcomings were to everyone else’s.

For the past six-plus years I have been running money for myself and a handful of “friends and family”. This has allowed me to vastly improve the quality of life for my family and me, and, almost equally importantly, to run money the way I want to, suiting my abilities and personality.

More specifically, I had had in my head a trading approach that I thought improved upon what I had been doing as a hedge fund manager, and I wanted to test it.

I now have six full years of running the Trading strategy. It’s levered, hyper-liquid, and macro and, on average, short term. When not in cash waiting for opportunities, it tends to be very aggressive. It relies a lot on pattern recognition, risk management and interpreting the pendular swings of market narratives.

This last January I had Ursa Fund Solutions compile a Schedule of Investment Performance from 2013-2019. I’m pleased to say that the results are, on the whole, good. Despite one disastrous year (actually, one disastrous quarter), the average annualized return was 24.3%. The cumulative return was 269%. I have attached that schedule here. I know six years is not a huge sample size, but, at a minimum, it’s an encouraging start.

There are some salient take-aways.

  1. The style is uncorrelated to the S&P. I haven’t run the numbers, but even a cursory glance at them (and me living them) suggest it’s true.
  2. It can also be uncorrelated to my own ideas. Anyone following me closely in 2018 knows how many things I got wrong—themes both big and small. Yet the fund returned 42 percent.
  3. Well over half of the returns were generated from only three assets: S&P futures, gold (precious metals), and the euro. (For those who are interested, bitcoin was the third largest contributor in 2018; and the outsized return in April 2013 was the collapse in gold and precious metals).

I didn’t have these numbers verified because I plan to launch a proper fund or raise money. I did it to put an official stamp on the process. And I wanted the stamp for three reasons: to show the subs on @behavioralmacro that the process works, and that hopefully can be learned from; to have hard numbers in my back pocket to shut up the occasional insistent naysayer who insists pattern recognition and TA can’t work, and, most of all, to document for myself that I had learned to shoot with my off hand.

Bonds, Pendular Swings, and Currencies

I look at sentiment in Fed analysis like a pendulum, one that swings from an excessively hawkish interpretation of the Fed’s stance at one end to an excessively dovish interpretation of the Fed’s stance at the other. Back and forth. Over and over again. The amplitude of each swing varies, as can its length. But the back and forth is always there, oscillating around the unobservable notion of a moving center of gravity. If you get good at gauging where we are in this process, you have a real leg up in bond and currency trading.

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It’s obviously not easy. But it can pay off well if you keep your losses small all the times you’re wrong, and really push your advantage when right.

Right now, I think the pendulum started swinging a few weeks ago away from an excessively dovish take on the Fed and too much fear of worsening slowdown. It had reached a local extreme and started swinging back.

The question at this point is how much farther should we expect the pendulum to swing back?

As a proxy, I’m using the 10yr UST futures. On the 5yr chart, you can see that if we continue the pattern of the last few years, this local lower high would lead to a lower low. My trading conclusion is I wouldn’t want to bet on the 10yr UST future falling quite that far, but I would expect it to fall further–maybe a lot–from here in the context of a pendular swing. After all, these pendular swings typically last many months, and this one only started a few weeks ago.


However, in the short term there is significant support right around these levels, as you can see in the 1yr chart of the 10yr UST futures.

It may turn out that the news is so bullish and/or the positioning so wrong-footed that we cut right thru the red area and proceed lower as the pendulum continues to swing back. Or maybe not.

Either way, the broad direction for precious metals and currencies in the near term is likely to depend on how this pendular swing in bonds plays out.