Explaining My Process
How I come up with macro trade ideas
I monitor broad money vs NGDP and demand deposits for signals
If the monetary environment is loose, I look for the market to price tightening
A lot of continuous monitoring of market prices and macro data is involved
The rates market was thrown into confusion last week by an unexpectedly hawkish federal reserve. The FOMC had been expected to re-iterate their previous dovish stance, that they wouldn’t raise rates until inflation had exceeded their target for some time and full employment was reached. Instead, they appeared to change their collective mind - with many members projecting rate hikes in 2022 and Powell sounding worried about inflation in the press conference. Over the next few days, huge speculative bets on a steeper interest rate curve unwound. Many speculators were blindsided. I didn’t see it coming.
Luckily, I didn’t have to. A simple measure of broad money growth vs NGDP, plus the observation that employment was rapidly converging on its pre crisis peak, meant I had thought that the market could price a tightening of policy in the near future. My approach to finding opportunities in rates markets doesn’t rely on figuring out what central bankers are going to say and when because I don’t think I can know that. I want to explain in this post what I think I do know and how I use it so that I can refer back to it when asked, and so the smart and capable people reading this can tell me how I might do better.
I start with a simple measure of the ‘stance’ of monetary policy. The interest rate market is, at its heart, a prediction market for how monetary policy will evolve over time. It’s therefore crucial to understand what the current policy settings are. But how can we know this? Most people believe that if interest rates are lower today than they were, policy is looser. I believe my first small bit of edge is not believing that. Central banks use interest rates as one of many tools to influence the lending decisions of banks and bank-like financial companies. Those lending decisions are what influences the stock of money and credit. All other things being equal, banks will lend more if GDP is increasing because more GDP means more valuable things for people to sell to repay loans. Therefore, I look at the growth of credit/money stock compared to nominal GDP. If money/credit is expanding much faster than the money value of all the goods and services produced in the economy, then I take that as a signal that the monetary environment is loose.
An aside here. Many people think that because MV = PT, such an environment must lead to inflation but this is theoretically questionable and empirically irrelevant. Theoretically because V is an unobservable residual, and because ‘T’ and ‘GDP’ are not the same (intermediate goods transactions aren’t included in GDP). Empirically there is simply no observable relationship. That said, there there is a much more sensible school of thought that says that changes in the money stock relative to GDP ought to be translate into some combination of increases in wealth and increases in prices, in other words asset inflation is part of the picture. I have a lot of sympathy for this approach, but have yet to work out how to use it.
To abstract away from the unique aspects of each economy and make comparisons across time and across countries, I look at the difference between growth rates in broad money and NGDP vs the 5y average difference of the two. That gives me one number that I can use to compare looseness or tightness across economies. Here’s what it looks like today:
Hungary is currently top of the list for monetary looseness, but that won’t last as NGDP numbers are from Q4 2020, whereas money numbers are more up to date. The US on the other hand really is extremely loose. Thanks to large monetised government spending which increases money balances, bank balances across the US have increased at unprecedented rates - demand deposits (current and checking accounts) have almost tripled from just under 1.5trio to just over 4trio.
Financial markets are forward looking and allegedly discount all available information - but this is not information as such. It’s a model. It’s also one that’s not popular. When I discuss my approach with new clients or colleagues it is very rare that they agree with the premise - the idea of making inferences from money growth is seen as primitive. That’s not surprising because of the history of central banks trying to manage post Bretton Woods economies in this way. Commercial banks found all sorts of ways of creating hidden leverage outside of central banks monetary growth targets, so the targets were abandoned - Goodharts law in practice. Of course, the converse of Goodhart’s law is that the less observed a variable is, the better it is as a measurement. That’s exactly why I use this approach.
So what can be done with this signal? To make money speculating one needs to identify where consensus is vulnerable to change. The purpose of my broad money vs NGDP signal is to identify potential vulnerabilities - but whether there is a consensus that can change is something one needs to work out from prices. Fortunately, interest rate markets tell us what is priced with some precision. Take the US case. Here, the Fed Funds forwards from today and before the Fed meeting last week, with changes shown.
The fed funds rate on 15jun was 0.06%, rate hikes come in 25bp increments. So since the meeting, the market has taken most of a hike out of the back end of the curve and put the same amount into the 2-3y part. The consensus, it turned out, was vulnerable because the pricing was for hikes to be delayed. Instead, the Fed has now admitted they may come sooner. My broad money vs NGDP signal was indicating this.
On top of this basic approach, I have a few filters. Broad money includes lots of different kinds of money so it’s a good measure for the overall economy but sometimes it could be that only some parts of the money stock are changing. I therefore look at various measures of demand deposits. If broad money vs NGDP is flagging a loose monetary environment, but the actual balances on peoples checking accounts are not rising, I consider the signal cancelled. This chart is a little messy so apologies, but to illustrate how that works in practice:
White here is the level of demand deposits. Green is the difference between 2y1y and 3m fed funds rates, and yellow is broad money growth minus NGDP growth
In 2018, I started to notice broad money - NGDP (yellow) was dropping. In mid 2018, it dipped below the 5y average. Demand deposits (white) then dipped also. The green line, showing how many rate hikes were priced, declined sharply in the latter part of 2018 into 2019. In 2019, broad money vs NGDP started rising again and quickly got back to the 5y average, but we still had rate cuts priced until demand deposits started rising again in Q3. After that, the rate cuts were unwound - though of course we never got to see how far the move went thanks to Covid. In both cases, broad money vs NGDP flagged the move but the timing was indicated by deposits.
Another filter is capacity utilisation and prime age employment. The signal given by US demand deposits and broad money growth vs NGDP has obviously been strongly loose for some time as the US responds to the pandemic. However, with 10% of the entire 25-54 year old population newly out of work in the pandemic, there was clearly no danger of the market pricing policy tightening. Only once a return to pre-pandemic levels of employment was within a reasonable horizon - on the current pace we’ll get there by next Feb - was it possible for markets to reprice and include a likelihood of policy tightening in the future. Capacity utilisation, a survey measure of business’s idle plant and equipment, fulfills the same function. The pandemic case is a very obvious one but employment was similarly useful in 2019 - it fell throughout 2019 even as the monetary environment loosened. That was a useful counter signal to my monetary measures, it started to rise again in late 2019, followed by tightening expectations priced by the market.
Trading this approach without following markets closely wouldn’t work, the limited success I’ve had with this approach has relied on closely following the narratives that underlie prices - as well as monitoring derivatives of prices that give information about positioning. The implied volatility of options on different parts of the interest rate curve can tell us something about beliefs. In advance of the Fed meeting last week, options on interest rates in 2024 were trading at 4x the implied volatility of those in 2022 - that indicated to me that there was a consensus that rates could not moved. I was able to cross confirm that by asking my clients whether they would consider selling one to buy the other, and being told they wouldn’t even consider it. That is normally a good indication that something is overly consensus and vulnerable to a shift.
This approach is definitely not scientific in the sense of being a formally testable model of how the world works. Such a model could not exist to answer the question of ‘what will everyone believe next’ that speculators aim to answer. The main contribution I think it makes is applying a kind of structured agnosticism about the world - by looking directly at the quantity of credit relative to the total value supporting it I am looking through the intentions and beliefs of central bankers, and the market. There are much more ‘sophisticated’ measures of financial conditions, but these are invariably using data that reflects expectations. I prefer a blunt and simple instrument that only reflects beliefs and expectations insofar as they affect behaviour.
This is a difficult approach to simply look at occasionally and take signals from. A static picture of the world through this lens tells you little. Instead, I monitor continuously how the signals I’m getting from the monetary environment and real economy stack up with the pricing of interest rates in the market and make recommendations when the two diverge. It ain’t much, but it’s honest work.
NB: This post is not investment advice and is not a trade recommendation. The views expressed here are my own and do not reflect those of my employer.