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Summary

• Can the monetary policy environment be used to predict global equity market returns? Should we overweight/buy countries with expansionary monetary policy regimes and underweight/sell countries with contractionary monetary policy regimes?
• In twelve of the fourteen countries studied, both nominal and real equity returns are higher (lower) when the central banks most recent action was to cut (hike) rates. For example, nominal U.S. equity returns are 1.8% higher during expansionary environments.  Real U.S. equity returns are 3.6% higher during expansionary environments.  The gap is even larger outside the United States.
• However, the monetary policy regime explains very little of the overall variation in equity returns from a statistical standpoint.
• While many of the return differentials during expansionary vs. contractionary regimes seem large at first glance, few are statistically significant once we realistically account for the salient features of equity returns and monetary policy. In other words, we can’t be sure the return differentials didn’t arise simply due to luck.
• As a result, evidence suggests that making buy/sell decisions on the equity markets of a given country using monetary policy regime as the lone signal is overly ambitious.

Can the monetary policy environment be used to predict global equity market returns?  Should we overweight/buy countries with expansionary monetary policy and underweight/sell countries with contractionary monetary policy?

Such are the softball questions that our readers tend to send in.

Intuitively, it’s clear that monetary policy has some type of impact on equity returns.  After all, if the Fed raised rates to 10% tomorrow, that would clearly impact stocks.

The more pertinent question though is if these impacts always tend to be in one direction.  It’s relatively straightforward to build a narrative around why this could be the case.  After all, the Fed’s primary tool to manage its unemployment and inflation mandates is the discount rate.  Typically, we think about the Fed hiking interest rates when the economy gets “too hot” and cutting them when it gets “too cold.”  If hiking (cutting) rates has the goal of slowing (stimulating) the economy, it’s plausible to think that equity returns would be pushed lower (higher).

There are a number of good academic papers on the subject. Ioannadis and Kontonikas (2006) is a good place to start. The paper investigates the impact of monetary policy shifts on equity returns in thirteen OECD countries¹ from 1972 to 2002.

Their analysis can be split into two parts.  First, they explore whether there is a contemporaneous relationship between equity returns and short-term interest rates (i.e. how do equity returns respond to interest rate changes?)².  If there is a relationship, are returns likely to be higher or lower in months where rates increase?

 

In twelve of the thirteen countries, there is a negative relationship between interest rate changes and equity returns.  Equity returns tend to be lower in months where short-term rates increase.  The relationship is statistically significant at the 5% level in eight of the countries, including the United States.

While these results are interesting, they aren’t of much direct use for investors because, as mentioned earlier, they are contemporaneous.  Knowing that equity returns are lower in months where short-term interest rates rise is actionable only if we can accurately predict the interest rate movements ahead of time.

As an aside, if there is one predictive interest rate model we subscribe to, it’s that height matters.

Fortunately, this is where the authors’ second avenue of analysis comes into play.  In this section, they first classify each month as being part of either a contractionary or an expansionary monetary policy regime.  A month is part of a contractionary regime if the last change in the discount rate was positive (i.e. the last action by that country’s central bank was a hike).  Similarly, a month is part of an expansionary regime if the last central bank action was a rate cut.

We illustrate this classification for the United States below.  Orange shading indicates contractionary regimes and gray shading indicates expansionary regimes.

The authors then regress monthly equity returns on a dummy variable representing which regime a month belongs to.  Importantly, this is not a contemporaneous analysis: we know whether the last rate change was positive or negative heading into the month.  Quoting the paper:

“The estimated beta coefficients associated with the local monetary environment variable are negative and statistically significant in six countries (Finland, France, Italy, Switzerland, UK, US).  Hence, for those countries our measure of the stance of monetary policy contains significant information, which can be used to forecast expected stock returns.  Particularly, we find that restrictive (expansive) monetary policy stance decreases (increases) expected stock returns.”

Do we agree?

Partially.  When we analyze the data using a similar methodology and with data updated through 2018³, we indeed find a negative relationship between monetary policy environment and forward 1-month equity returns.  For example, annualized nominal returns in the United States were 10.6% and 8.8% in expansionary and contractionary regimes, respectively.  The gap is larger for real returns – 7.5% in expansionary environments and 3.9% in contractionary environments.

 

A similar, albeit more pronounced, pattern emerges when we go outside the United States and consider thirteen other countries.

 

 

The results are especially striking in ten of the fourteen countries examined. The effect in the U.S. was smaller compared to many of these.

 

 

That being said, we think the statistical significance (and therefore investing merit) is less obvious.  Now, it is certainly the case that many of these differences are statistically significant when measured traditionally.  In this sense, our results agree with Ioannadis and Kontonikas (2006).

However, there are two issues to consider.  First, the R² values for the regressions are very low.  For example, the highest R² in the paper is 0.037 for Finland.  In other words, the monetary regime models do not do a particularly great job explaining stock returns.

Second, it’s important to take a step back and think about how monetary regimes evolve.  Central banks, especially today, typically don’t raise rates one month, cut the next, raise the next, etc.  Instead, these regimes tend to last multiple months or years.  The traditional significance testing assumes the former type of behavior, when the latter better reflects reality.

Now, this wouldn’t be a major issue if stock returns were what statisticians call “IID” (independent and identically distributed).  The results of a coin flip are IID.  The probability of heads and tails are unchanged across trials and the result of one flip doesn’t impact the odds for the next.

Daily temperatures are not IID.  The distribution of temperatures is very different for a day in December than they are for a day in July, at least for most of us.  They are not identical.  Nor are they independent.  Today’s high temperature gives us some information that tomorrow’s temperature has a good chance of hitting that value as well.

Needless to say, stock returns behave more like temperatures than they do coin flips.  This combination of facts – stock returns being non-IID (exhibiting both heteroskedasticity⁴ and autocorrelation) and monetary policy regimes having the tendency to persist over the medium term – leads to false positives.  What at first glance look like statistically significant relationships are no longer up to snuff because the model was poorly constructed in the first place.

To flush out these issues, we used two different simulation-based approaches to test for the significance of return differences across regimes.⁵

The first approach works as follows for each country:

1. Compute the probability of expansionary and contractionary regimes using that country’ actual history.
2. Randomly classify each month into one of the two regimes using the probabilities from #1.
3. Compute the difference between annualized returns in expansionary vs. contractionary regimes using that country’s actual equity returns.
4. Return to #2, repeating 10,000 times total.

This approach assumes that today’s monetary policy regime says nothing about what tomorrow’s may be. We have transformed monetary policy into an IID variable.  Below, we plot the regime produced by a single iteration of the simulation. Clearly, this is not realistic.

 

The second approach is similar to the first in all ways except how the monetary policy regimes are simulated.  The algorithm is:

1. Compute the transition matrix for each country using that country’s actual history of monetary policy shifts. A transition matrix specifies the likelihood of moving to each regime state given that we were in a given regime the prior month.  For example, if last month was contractionary, we may have a 95% probability of staying contractionary and a 5% probability of moving to an expansionary state.
2. Randomly classify each month into one of the two regimes using the transition matrix from #1. We have to determine how to seed the simulation (i.e. which state do we start off in).  We do this randomly using the overall historical probability of contractionary/expansionary regimes for that country.
3. Compute the difference between annualized returns in expansionary vs. contractionary regimes using that country’s actual equity returns.
4. Return to #2, repeating 10,000 times total.

The regimes produced by this simulation look much more realistic.

 

When we compare the distribution of return differentials produced by each of the simulation approaches, we see that the second produces a wider range of outcomes.

 

 

In the table below, we present the confidence intervals for return differentials using each algorithm.  We see that the differentials are statistically significant in six of the fourteen countries when we use the first methodology that produces unrealistic monetary regimes.  Only four countries show statistically significant results with the improved second method.

 

Country	Spread Between Annualized Real Returns	95% CI First Method	P-Value First Method	95% CI Second Method	P-Value Second Method
Australia	+9.8%	-1.1% to +20.7%	7.8%	-1.5% to +21.1%	8.9%
Belgium	+14.6%	+4.1% to +25.1%	0.6%	+0.7% to +28.5%	3.9%
Canada	-0.7%	-12.2% to +10.8%	90.5%	-14.2% to +12.8%	91.9%
Finland	+29.0%	+6.5% to +51.5%	1.2%	-2.4% to +60.4%	7.1%
France	+17.3%	-0.5% to +35.1%	5.7%	-10.8% to +45.4%	22.7%
Germany	+10.8%	-1.1% to +22.7%	7.5%	-2.8% to +24.4%	12.0%
Italy	+17.3%	+3.6% to +31.0%	1.3%	-0.2% to +34.8%	5.3%
Japan	+26.5%	+12.1% to +40.9%	0.0%	+3.4% to +49.6%	2.5%
Netherlands	+16.8%	-1.8% to +35.4%	7.6%	-11.6% to +45.2%	24.7%
Spain	+23.8%	+11.3% to +36.3%	0.0%	+9.9% to +37.7%	0.1%
Sweden	+30.4%	+12.7% to +48.1%	0.1%	+4.7% to +56.1%	2.1%
Switzerland	+2.3%	-11.5% to +16.1%	74.4%	-26.3% to +30.9%	87.5%
United Kingdom	-0.6%	-11.5% to +10.3%	91.4%	-12.0% to +10.8%	91.8%
United States	+3.6%	-5.0% to +12.2%	41.1%	-6.0% to +13.2%	46.2%

Source: Bloomberg, MSCI, Newfound Research

 

Conclusion

We find that global equity returns have been more than 10% higher during expansionary regimes.  At first glance, such a large differential suggests there may be an opportunity to profitably trade stocks based on what regime a given country is in.

Unfortunately, the return differentials, while large, are generally not statistically significant when we account for the realistic features of equity returns and monetary policy regimes. In plain English, we can’t be sure that the return differentials didn’t arise simply due to randomness.

This result isn’t too surprising when we consider the complexity of the relationship between equity returns and interest rates (despite what financial commentators may have you believe).  Interest rate changes can impact both the numerator (dividends/dividend growth) and denominator (discount rate) of the dividend discount model in complex ways.  In addition, there are numerous other factors that impact equity returns and are unrelated / only loosely related to interest rates.

When such complexity reigns, it is probably a bit ambitious to rely on a standalone measure of monetary policy regime as a predictor of equity returns.

 

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1. Belgium, Canada, Finland, France, Germany, Italy, Japan, Netherlands, Spain, Sweden, Switzerland, United Kingdom, United States
2. In this part of the analysis, the authors use Treasury Bill rates as the interest rate variable.
3. Due to data availability issues, not all countries are evaluated back to 1972 as in the paper.
4. A very fancy way to say “varying volatility”
5. Both simulation approaches have hindsight bias embedded as we only know the distribution of monetary policy regimes after the fact.