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All You Need To Know About Warsh

2026-02-10 18:04:53

Hi, this is Alf - welcome back to The Macro Compass!

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And now, to today’s macro research piece.

Warsh is the new Fed Chair.

I find it hilarious that people are calling him a hawk based on what he did 10-15 years ago. This is like looking at old pics of me with a head full of hair (still have one of these on LinkedIn!). These times are long gone.

Jokes aside, I spent hours listening to the most recent interviews Warsh did (here and here, for example).

Here are the most consequential thoughts he shared:

1) Inflation is a choice, money creation drives it (as per Friedman, and that’s true in most cases);

2) Fed is backstop for banking system, but needs to get out when job is done (fair);

3) He believes inflationary money is created equally by Fed and the government (absolutely false: fiscal deficits create inflationary money, Fed monetary operations such as QE only create bank reserves);

4) He believes the Fed balance sheet being so big ‘’crowds out the private sector’‘ (absolutely false: the private sector uses another form of money i.e. bank deposits, there is no ‘’crowding out’’ of anything);

5) Need to get out of the ‘’fiscal and monetary’‘ mess inherited: a lot of talk about interest expense > defense spending (ok, so hear the plan now...);

6) ‘’If we would run the printing press a little quieter, we would have lower interest rates’’;

7) The plan is to shrink the Fed balance sheet, so we get lower rates, so private sector can thrive = higher economic growth (he is looking at the wrong printing press – Bessent has the key one, not the Fed);

8) Rates should also be lower because we are on the verge of a productivity boom (his opinion, fair enough);

9) The Fed should not be data dependent, data is revised all the time, why be? Best to have an opinion and stick to it. Also the Fed should not share economic forecasts, no dots. No forward guidance (uncertainty up).

It’s pretty clear Warsh – as many others, by the way – gets money creation wrong. But it still leads to some key consequences for markets:

Shrinking the balance sheet and following the AI productivity fairytale will be two convenient covers Warsh will use to push for cutting Fed Funds further;
Cutting front-end rates further will help the US Dollar remain weak;
Injecting instability in the repo market through a reduction of the balance sheet, cutting front-end rates proactively and slashing forward guidance tools = uncertainty up = steeper curves.

One key thing to consider is that if Warsh would manage to shrink the Fed balance sheet, this could have an impact on the repo market.

The chart below shows how bank reserves as % of total US nominal GDP have been shrinking and approaching the potentially dangerous 8-9% level – last time it breached that, we had a repo blowup in 2019.

This is because bank reserves are very unevenly distributed across US banks, and once they start becoming scarce the odds of interbank liquidity drying up for smaller banks increase:

Do you know who put forward this ratio, by the way?

Waller! (Read his speech here).

Shrinking the balance sheet further here would put the stability of the repo market at risk, and by the way in December 2025 the Fed resumed purchases of T-Bills with the very intent to avoid such risks (here).

The odds of Warsh convincing his colleagues to shrink the balance sheet further here are low.

But now let’s assume that Warsh still manages to put through his lower balance sheet + AI productivity fairytale to convince his colleagues to cut rates a bit faster. And that forward guidance disappears.

Then the US yield curve would steepen, and that goes against what the US administration wants:

Wait, why am I showing you a chart of 30-year JGBs against 30-year US yields for Japanese investors?

Because this is a key chart for Bessent to push in the right direction, as it would achieve a great deal of positive outcomes for the US administration.

For years, Japanese investors have bought foreign bonds WITHOUT hedging the FX risk at all - and why?

Because the assumption was that the Yen was going to get weaker, and hence Japanese investors could:

1) Get a higher yield in foreign bond markets;

2) Benefit from the JPY weakness on top.

And it’s in the clear interest of the US administration to reverse this mechanism.

Getting Japanese investors to buy more JGBs would encourage repatriation of capital and help JPY appreciate, plus support the Japanese bond market and help global long-end bond yields stabilize.

Very soon, it might be that Japanese investors wouldn’t really have any advantage in buying 30-year US Treasuries (after FX hedging costs) rather than keeping money in Japanese bonds.

A steeper US curve would bring Japanese investors into the old habits, and that’s not what the US wants.

So, here we are trying to solve the puzzle.

We have Warsh likely using Fed balance sheet reductions and the AI productivity story to cut rates.

Unlikely to really succeed, but the bias will be for a dovish Fed (what a surprise!).

That helps the USD to weaken and equities to go up (two clear US admin objectives), but Warsh is a curve steepener – and if long-end bonds get out of hand, Bessent and Japan could be ready to intervene.

Bessent can tweak issuance further to the front-end, and Japan can backstop its own long-end bonds too.

This leaves us in a scenario where in the medium term:

We keep running the global economies hot via major fiscal spending programs worldwide;
The Fed maintains a dovish inclination;
Bond yields are flat or down;
The USD trades flat or down;
Commodities and equities maintain the most upside convexity

I believe the asset classes poised to deliver the best risk-adjusted returns over the next 3-6 months are:

International, value-oriented stock markets (e.g. Europe, Japan, Canada, specific Emerging Markets);
Precious metals and industrial commodities (e.g. Copper).

This was it for today. Thanks for reading.

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May The Odds Be Ever In Your Favor

2026-01-19 16:46:05

Hi everyone, and welcome back to The Macro Compass!

‘’I compile statistics on my traders. My best trader makes money only 63 percent of the time. Most traders make money only in the 50 to 55 percent range. That means you’re going to be wrong a lot. If that’s the case, you better be sure your losses are as small as they can be, and that your winners are bigger.’’ – Steve Cohen.

This is a hard truth to accept for many macro investors: we will be right only about 50-55% of the times.

If your win rate is much higher than this, I suggest you extend the sample of trades you are analyzing or assess whether you are not trading macro but rather just selling optionality – short vol/option strategies have win rates as high as 90%+, but they wipe you out completely when you are wrong.

In the last 10 years, I scored a 53% long-term win rate on my directional macro trades.

Once I realized that and given that the year-end P&L formula can be written as follows:

I knew I’d better make sure the size of my losses doesn’t get out of control.

This can be achieved in two ways: sizing trades correctly and designing a system that lets your winners run.

Here is what I go through for every tactical trade I set up:

Points 1-3 keep my emotions in check and ground me to a more rational assessment of the trade.

Points 4-6 are about implementation.

A warning: short carry trades (and long options) are expensive to hold over time if nothing happens.

A reminder: in very choppy markets, you can get quickly stopped out with linear trades even if your thesis proves to be correct – consider whether the market regime favors linear or option implementations.

Don’t be stupid: check whether the trade you are about to add is not just another expression of a trade you already have on – I have seen people blow up as the 10 trades they were running were just…the same trade.

But it’s point 7 that sticks out: sizing and risk management define most of your P&L at year-end.

Here is how I approach them through a practical example.

Say you think that the S&P500 will keep marching higher over the next month: how many SPYs do you buy?

You could be in the right or left 50% of that distribution: when you pull the trigger, you don’t know that.

And because you don’t know that, you want to standardize your ex-ante sizing.

One effective way to standardize the sizing of each tactical trade so that they don’t excessively weigh on your year-end P&L is to do volatility-adjusted sizing: let’s go through the SPY example.

Let’s set our stop at 1.5 standard deviations, and our defined time horizon in this example will be 1 month.

For the SPY, using a 5-year lookback the typical 1.5x monthly negative sigma event would be a -7.6% decline.

You can play around with the lookback period if you want more history and/or assign different weight to more recent periods if you think today’s vol regime is more relevant (grey boxes).

If returns are normally distributed, we will be stopped out 6.7% of the times in our defined time horizon.

But as returns often follow other distributions, it’s good practice to check the actual empirical probability of being stopped out against the theoretical 6.7% probability (orange boxes).

Finally, define what’s the fixed % of AuM you are willing to lose on any given macro trade.

A fictitious $1 million trading account willing to lose max $20k per trade which is bullish on SPY with a 1-month horizon would buy 571 SPY shares at $437 and be stopped out at $402 (-7.6% = 1.5x sigma event) hence losing $20k (= 2% of AuM).

Congratulations, you just applied volatility-adjusted position sizing!

What are the advantages of this approach?

1) You remain agnostic to ‘’volatility luck’’: if you size all positions the same, being right/wrong on the most volatile assets will make/break your P&L at year-end and that’s all about luck. Don’t gamble.

2) You remain agnostic to your ‘’conviction’’ level: the truth is that ex-ante you don’t know when you’ll be in the right or wrong 50%, so why would you over or under size a trade based on your ex-ante conviction levels? You shouldn’t.

3) This approach is really flexible: you can use it as a day trader or as a tactical macro investor, you can set stops looser/tighter depending on your approach etc.

Ok so now you have a data-driven macro approach, tools to help you digest action in global markets, and a ‘’don’t be stupid checklist’’ to help you push that 50% win rate higher plus a vol-adjusted position sizing system to avoid big losses by design.

How do you actually make money?

First, you place profit targets in an asymmetric way to your stop losses.

If your stop loss is at -1 standard deviation, your profit targets should be at over 1 sigma: if you are able to preserve a 50% win rate, that will help you making more money on winners than you lose on bad trades.

Most importantly though, you must have a system in place to let your winners run: the best hedge fund traders I know only score 2-3 outstanding trades per year which account for 80% of their yearly excellent P&L.

To improve the odds of achieving that, I use a trailing profit target strategy:

Say you set a stop at -1 sigma and first profit target at +1.5 sigma: when you hit the first target, you do not take profits – rather, you enter a trailing strategy.

Your new profit target becomes +2.5 sigma, and your new stop becomes 0.

You hit 2.5 sigma? Great!

Extend again: +3.5 sigma target, +1.5 sigma stop. And so on and so forth.In short, here is a sensible approach to tactical macro trading:

- You want to recognize you’ll be right only about 50-55% of the times;

- To skew the odds towards 55% you want to have a data-driven macro process, quantitative tools to screen markets and a ‘’don’t be stupid’’ checklist before pulling the trigger;

- You want to follow a volatility-adjusted sizing process as explained above;

- You want to religiously respect your stop losses and have a system in place to let your profits run.

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This was it for today, thanks for reading.

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The Best Assets You Don't (But Should) Own in 2026

2025-12-01 02:31:20

I am not long enough. You are not long enough.

The global real-economy money printer is going to run very hot in 2026.

The more spendable money global economies print, the higher the nominal growth impulse we are going to get.

Central Banks around the world are not going to fight it.
In some cases, they are going to apply loose monetary policy even in the face of sustained money printing.

The question is: what asset classes benefit the most in this macro setup?
We’ll cover that in a second, but first an important message.

Let’s take a step back together and look at the macro big picture ahead of us:

1) Money creation in 2026 will be through the roof: fiscal from US (OBBB), Germany, Japan, Korea etc + AI Capex will contribute to money printing;

2) Central Banks around the world will not be fighting this at all – if anything, they are net loose;

3) Housing disinflation will help US core inflation sit in the 2.5-3.0% area for a while longer;

4) The setup is: nominal growth at 5%+, Central Banks neutral or loose, ongoing global money printing;

In 2025, net fiscal deficits and private sector leverage (read: money printing) around the world added $8.1 trillion (!) of new inflationary money to global economies.

If we look at 2026, more fiscal stimulus and more debt-funded AI capex will take place.
The global real-economy money printer will continue to go BRRR.

For your reference, here is our projection for the 2026 German money printing activities based on their large fiscal stimulus:

Global money printing correlates with nominal growth: the more aggressively we create new real-economy money, the more likely it is that nominal growth will pick up.

What about global Central Banks? Are they going to stay loose in the face of strong money printing?

The table below shows the Central Bank policy stance around the world - black if around neutral, red if tight, green if loose.

Take a look at the color scale: there is only one tight CB (Brazil), some neutral CBs (+/- 50 bps around neutral), and a big bunch of loose Central Banks around the world. In many cases, the loose policy stance comes from outright ignoring your inflation target.

In the US, Canada and Japan the terminal rate (‘‘2y forward rate’’) is around the neutral rate but there is nothing ‘‘neutral’’ about core inflation - which in all cases sits well above the Central Bank target.

A Central Bank applying neutral monetary policy in the face of core inflation well above their target and ongoing money printing…

…is a very loose Central Bank.

So, what happens to markets in such a macro setup?

My TMC Asset Allocation model says we will be anywhere between ‘’The Squeeze’’ and ‘’Goldilocks’’ depending on inflation: do we sit with core around 2.5-2.8% or do we surpass 3%?

In general, these top-right quadrants are very very supportive for risk assets.

If you look back at previous episodes of sustained money printing plus loose Central Banks, the 2005-2006 period looks like a decent parallel.

Back then, the global CPI rate was around 3% (blue) and Central Banks were gradually raising rates from 4 to 5% (orange) on average around the world.
Today, we have global inflation around 3% and global CB rates around 5% as well.

If anything, today’s setup seems more sustainable because the money printer is now fueled by government deficits, not private sector debt as it was in 2005-2007:

In that period EMs, value-oriented stock markets and commodities did great.

And right now, these are the 3 most under-owned asset classes in institutional portfolios:

After a decade of US/tech dominance and scarse capital flows towards value, emerging markets and commodities the world is largley underallocated towards these asset classes.

I am not long enough these assets.
Are you?

Leave a comment

Run It Cold Now, Run It Hot Later

2025-10-08 18:19:53

Hi everyone - this is Alf.
I hope you’re having a great day.

My macro models have been suggesting a bi-modal framework to approach markets for the near future: Run It Cold Now (growth < expectations, and dovish Fed) and Run It Hot Later (growth up, inflation risk premia up, but no Fed hawkishness).

My Run It Cold Now theory has been increasingly vindicated by labor market data, and markets are often busy trading what’s in front of them rather than looking through a potential reacceleration in 2026.

This is why it’s vital to figure out:

1. How early/late do we sit within the ‘’Run It Cold Now’’ period;

2. How much has the market priced in by now;

3. Consequently: is it still worth getting engaged in Run It Cold trades, or shall we look at Run It Hot Later ideas already?

As a reminder, my macro models suggest tariffs will act as a fiscal tightening mechanism until year-end.

By early 2026, the OBBB fiscal impulse will offset and Trump’s new initiatives alongside lowered Fed Funds and private money creation should propel a Run It Hot Later period.

First, some evidence that US labor demand is really weak: extrapolating benchmark revisions from April 2025 onwards, the US has been consistently shedding jobs!

To get a broader perspective on the labor market we can rely on unrevised data which incorporates demand and supply for labor: for example, unemployment rate and its important subcomponents.

The chart below shows the number of long-term (27+ weeks) unemployed Americans as a percentage of the total labor force. At 1.14%, this number is already as high as in 2002 or summer 2008 – in both cases, a recession was already visibly hitting America:

Why is US labor demand so weak?

Due to tariffs, the US is going through a slowdown of its primary fiscal impulse: the 2025 primary fiscal deficit sits almost 20 bps below last year and markedly below the 2023 pace.

Tariffs are effectively acting as a tax on US companies and consumers:

This seems to be confirmed by ‘’the best economist Druckenmiller knows’‘: the internals of the stock market.

The chart below shows (in white) the ratio between an index of the 5 largest US payroll processors companies and the equal-weight SPX, plotted against 2-year Treasury yields (in green).

If there are no new jobs, the largest payroll processors companies in the US will suffer - and indeed, their stocks are trading very weak.

This is an example of how the internals of the stock market suggest the US labor market is very weak, and that the Fed will be soon called to ease more:

The US economy is ‘‘running cold’’ now, yet stock markets are roaring and risk sentiment remains very aggressive - why?

The private sector money printer is going BRRRR, led by AI.

The chart below shows the big-tech announced capex spending as a % of their EBITDA – it’s already over 65% on average, exceeding the AT&T spending of 1998. To keep up this pace next year, companies will have to resort to debt-funded AI capex:

AI Capex mechanically adds to US GDP even before we get to talk about the ROI.

But the biggest issue with AI Capex is that it doesn’t really add jobs for the median American for now, and hence we are left with two economies: a hot AI-related economy, and a broader labor market struggling under the fiscal tightening induced by tariffs.

The stock market is not the economy, and the gigantic AI capex effort coupled with large global fiscal stimulus programs continues to support risk sentiment.

Our global real-economy money printing index is very strong.

We tracked the YTD pace of inflation-adjusted $ money creation around the world, and this year we have scored an impressive 5.77% increase in real-economy money printing around the world.

This comes after 3 weak years led by the gigantic Chinese housing deleveraging, and the YTD pace in 2025 is in line with the ‘’concerted global growth’’ pace of 2017.

The global pace and acceleration is quite robust, and its mainly driven by China which has restated its credit engines after 2-3 years of robust housing deleveraging.

Despite being crippled by tariffs (e.g. a large tax), US money creation is accelerating led by the AI-related debt-funded capex spending on data centers.

And money printing is only set to accelerate going forward.

From a fiscal standpoint, we are 100% sure that from early 2026 we will see:

- Germany adding to money creation via a large increase in primary spending;

- The US OBBB kicking in with its fiscal stimulus offsetting tariffs money destruction;

- Korea, Sweden, and many other countries kickstarting deficit spending programs

We might be looking into a scenario where the Fed cuts rates but global money printing accelerates.

If we dust off my TMC Quadrant Asset Allocation Model, that puts us in the top-right quadrant:

In short, historically the best asset allocation for such an environment involves selling IOUs and paper assets, and buying tangible risk assets.

Basically:

Sell USDs and bonds,
Buy stocks and assets linked to nominal growth

The most painful outcome for institutional investors would be an equity rotation towards EM/Value + a commodity rally.

These are very underowned asset classes, and they could rally right when standard portfolio hedges such as USD and bonds would fail to deliver.

And the Market Gods enjoy inflicting the proverbial Pain Trade every now and then…

Thanks for reading!

May The Odds Be Ever In Your Favor

2025-08-25 17:10:35

Hi everyone - this is Alf. I hope you're having a great day.

‘’I compile statistics on my traders. My best trader makes money only 63 percent of the time. Most traders make money only in the 50 to 55 percent range. That means you’re going to be wrong a lot. If that’s the case, you better be sure your losses are as small as they can be, and that your winners are bigger.’’ – Steve Cohen.

This is a hard truth to accept for many macro investors: we will be right only about 50-55% of the times.

In the last 10 years, I scored a 52% long-term win rate on my directional macro trades. Once I realized that and given that the year-end P&L formula can be written as follows:

I knew I’d better make sure the size of my losses doesn’t get out of control.

This can be achieved in two ways: sizing trades correctly and designing a system that lets your winners run. We are going to talk about my approach to both angles in a second, but first another important remark.

To step up the win rate on macro trades from 50% to say 55% over a long period of time, you need to gain some edge over other macro investors.

What could that be?

- A data-driven approach with superior macro models

- The ability to assess the gigantic amount of cross-asset market signals via quantitative tools

- A particular edge in a niche market that you have learnt to navigate well over time

- Be less stupid than others

Macro models help a lot, but my ‘’don’t be stupid checklist’’ adds value too:

Points 1-3 keep my emotions in check and ground me to a more rational assessment of the trade.

Points 4-6 are about implementation.

A warning: short carry trades (and long options) are expensive to hold over time if nothing happens.

A reminder: in very choppy markets, you can get quickly stopped out with linear trades even if your thesis proves to be correct – consider whether the market regime favors linear or option implementations.

Don’t be stupid: check whether the trade you are about to add is not just another expression of a trade you already have on – I have seen people blow up as the 10 trades they were running were just…the same trade.

But it’s point 7 that sticks out: sizing and risk management define most of your P&L at year-end.

Here is how I approach them through a practical example. Say you think that the S&P500 will keep marching higher over the next month: how many SPYs do you buy?

You could be in the right or left 50% of that distribution: when you pull the trigger, you don’t know that. And because you don’t know that, you want to standardize your ex-ante sizing.

One effective way to standardize the sizing of each tactical trade so that they don’t excessively weigh on your year-end P&L is to do volatility-adjusted sizing: let’s go through the SPY example.

You could be in the right or left 50% of that distribution: when you pull the trigger, you don’t know that. And because you don’t know that, you want to standardize your ex-ante sizing.

Let’s set our stop at 1.5 standard deviations, and our defined time horizon in this example will be 1 month. For the SPY, using a 5-year lookback the typical 1.5x monthly negative sigma event would be a -7.6% decline.

You can play around with the lookback period if you want more history and/or assign different weight to more recent periods if you think today’s vol regime is more relevant (grey boxes).

If returns are normally distributed, we will be stopped out 6.7% of the times in our defined time horizon. But as returns often follow other distributions, it’s good practice to check the actual empirical probability of being stopped out against the theoretical 6.7% probability (orange boxes).

Finally, define what’s the fixed % of AuM you are willing to lose on any given macro trade.

A fictitious $1 million trading account willing to lose max $20k per trade which is bullish on SPY with a 1- month horizon would buy 571 SPY shares at $437 and be stopped out at $402 (-7.6% = 1.5x sigma event) hence losing $20k (= 2% of AuM).

Congratulations, you just applied volatility-adjusted position sizing!

What are the advantages of this approach?

1) You remain agnostic to ‘’volatility luck’’: if you size all positions the same, being right/wrong on the most volatile assets will make/break your P&L at year-end and that’s all about luck. Don’t gamble.

2) You remain agnostic to your ‘’conviction’’ level: the truth is that ex-ante you don’t know when you’ll be in the right or wrong 50%, so why would you over or under size a trade based on your ex-ante conviction levels? You shouldn’t.

3) This approach is really flexible: you can use it as a day trader or as a tactical macro investor, you can set stops looser/tighter depending on your approach etc.

The truth is that this volatility-adjusted position sizing approach helps you avoid your ex-ante biases: you’ll be right ~50% of the times and don’t get to know on which trades – so, size appropriately.

How do you actually make money?

First, you place profit targets in an asymmetric way to your stop losses.

Most importantly though, you must have a system in place to let your winners run: the best hedge fund traders I know only score 2-3 outstanding trades per year which account for 80% of their yearly excellent P&L.

To improve the odds of achieving that, I use a trailing profit target strategy:

Say you set a stop at -1 sigma and first profit target at +1.5 sigma: when you hit the first target, you do not take profits – rather, you enter a trailing strategy.

Your new profit target becomes +2.5 sigma, and your new stop becomes 0. You hit 2.5 sigma?Great! Extend again: +3.5 sigma target, +1.5 sigma stop. And so on and so forth.

These rare but outsized gains make the difference at year-end.

In short, here is a sensible approach to tactical macro trading:

- You want to recognize you’ll be right only about 50-55% of the times;

- To skew the odds towards 55% you want to have a data-driven macro process, quantitative tools to screen markets and a ‘’don’t be stupid’’ checklist before pulling the trigger;

- You want to follow a volatility-adjusted sizing process as explained above;

- You want to religiously respect your stop losses and have a system in place to let your profits run.

Thanks for reading! Feel free to share this piece with a colleague or friend.

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