Combining macro and micro the Mill Street way

One of the distinguishing features of Mill Street’s research process is that it can give both “top down” views on the macroeconomic and asset allocation outlook, or “bottom up” quantitative stock selection based on our long-standing tools like the MAER ranking model.

But it is often most interesting and compelling when we do both: start with top-down analysis to identify broader trends, and then use the tools to find the strongest stocks within the strongest regions/sectors/industries/styles.

We realize that not every investor can do this: some clients have specific mandates that say what they can invest in and what risks they can and cannot take, or have decided they want to focus on one area (e.g. stock picking) and not the other (macro views). Those clients can easily pick the tools that work for them and leave the rest.

My own view is that even investors who focus on one side of the macro vs micro picture can benefit from seeing what the other side is saying. More importantly, many of Mill Street’s macro views are based in part on the aggregated indicators from the stock-level data: the sum of the “micro” can drive the “macro” view. This is particularly helpful when traditional macro data (GDP, inflation, etc.) are unavailable, lagged, unreliable, or do not measure what you really want to know.

More broadly, investors often like the idea of finding the “best house in the best neighborhood”. That is, the odds of success in a highly unpredictable environment like the stock market tend to increase when multiple indicators align in the same direction.

Choose your metaphor: sailing, fishing, and finding a house

I often describe the “top down” market and asset allocation view using a sailing metaphor: you need a good way to determine which way the wind is blowing, and how hard. Without favorable winds, even the best sailor will struggle to make much headway, while strong tailwinds can allow even an inexperienced sailor to get where he or she is going quickly. The market equivalent is that a solid bull market will carry a lot of stocks and other risk assets with it, while a bear market environment is going to be a struggle for owning risk assets even if you are a good stock picker. The housing metaphor equivalent would be finding a good city or town to live in.

Some of our ways to measure the “wind”

I have written previously about, and sometimes comment on, our primary “risk on/risk off” timing tool: the Global Equity Risk Model. The key idea is that it is a multi-indicator composite with a diverse set of inputs, each of which has been studied and can help forecast future market risk and return.

You can read more about it here: Our anchor for the equity risk outlook.

I then check other indicators like our longer-term stock/bond relative valuation measure called the Implied Growth Model. I also wrote about that recently here: What is the market pricing in?

I also look at how corporate earnings and forward estimates are developing in aggregate, taking into account how analysts normally behave and what I would expect based on the macro backdrop.

Ideally, I’d like to see solidly bullish readings on the Global Equity Risk Model to tell me the near-term (1-6 month) outlook is positive for risk assets: higher expected returns and lower than average volatility. Along with that, I’d like to see the Implied Growth Model (which is based on US data) showing low or moderate long-run real growth expectations. Equity markets tend to do better when growth expectations are low and potentially improving than when expectations are very high and more vulnerable to disappointment.

I would also like to see signs that earnings estimates are at least stable or rising in aggregate, knowing that the pattern of estimate revisions is not the same as the level of earnings growth. Because of the way their job incentives push them, analysts tend to start with high longer-term earnings estimates and then trim them down to reality as time goes on, even as earnings grow over time. Ideally, the earnings estimate data will show leadership in cyclical and/or growth sectors of the market rather than defensive sectors, as cyclical/growth leadership also tends to be a positive sign for the macro backdrop.

Of course, the reverse of all these conditions would be reasons for concern about the outlook for risk assets and would argue for a cautious approach that favored fixed income over equities and more defensive holdings.

From sailing to fishing: looking for the right ponds to fish in

If we determine that the equity market outlook overall is generally positive, that is, we see “tailwinds” in our sailing metaphor that should help all boats sail faster, then we can shift to the fishing metaphor: fishing in the right ponds to catch the best fish. In this metaphor, the weather for fishing is good, but different ponds/lakes/rivers/seas that share the same general weather can yield very different numbers and types of fish to be caught.

Looking for the “right pond” in our case means looking for the regions of the world and the sectors and industries that have the best water conditions and the greatest concentration of likely fish that we want to catch. This means looking for regions or sectors that can benefit from the macro environment of interest rates, fiscal policies, commodity prices, currency trends, etc. We then combine those top-down views with bottom-up aggregates of the relative level of earnings estimate revisions, stock price momentum, and equity valuations.

Sectors or regions that have larger proportions of analysts raising forward earnings estimates (i.e., “revisions breadth”) tend to do better than those with lower levels of revisions breadth, just as we find with individual stocks. Price momentum also applies to regions and sectors much as it does for individual assets, so we look for corroborating price action in the areas with improving fundamentals. And ideally we would seek out the regions or sectors with improving fundamentals and returns where valuations are moderate relative to historical norms, and thus all the potential good news is less likely to already be priced in.

Here at Mill Street, we have spent a lot of time building and analyzing these aggregated bottom-up indicators of earnings, price, and valuation, extending our stock selection work. We also recognize the nuances and quirks of different regions and sectors, as well as the unquantifiable aspects of government or central bank policies or other drivers like OPEC, wars/conflicts, and shock events like COVID. This is the key reason why it is very difficult to construct purely mechanical sector or regional allocation models (i.e., models with relatively few assets to compare, like 11 sectors or a dozen or two regions/countries), as there is less scope to diversify away from the impact of idiosyncratic events or drivers as can be done more readily with a large universe of individual stocks.

Best house in the best neighborhood

If we have identified a favorable market backdrop and some regions and/or sectors that look strong on our aggregate indicators, we can then go down to the security level to identify the strongest stocks in those strong areas. That is, having identified the best “pond to fish in”, or alternatively, the best “neighborhood to buy a house”, we can look for the best houses in the good neighborhoods (or best fish in the pond).

One example path from the “top” to the “bottom” of the market

Right now, for instance, we might find that the market backdrop still looks supportive, and that the US and Japan have the strongest fundamental earnings trends among major regions.

If we opt to focus on the US, our indicators have been telling us for some time that large-caps have the more favorable cyclical tailwinds and earnings trends relative to small-caps.

Among sectors, our work shows that sectors like Financials, Industrials, Technology, and Consumer Staples have more support from our bottom-up indicators than do areas like Energy, Materials, Real Estate, and Consumer Discretionary.  And since sector classifications are quite broad and heterogenous, we can look at the more granular industries to find the strongest areas within the stronger sectors.

These might include:

  • Insurance, Capital Markets, and Financial Services within the Financials sector, but not Banks or Consumer Finance
  • Software and Technology Hardware within Technology, but not Communications Equipment or Semiconductors
  • Building Products, Airlines, and Machinery in Industrials, but not Ground Transportation or Air Freight & Logistics
  • Beverages and Personal Care Products in Consumer Staples, but not Consumer Staples Retailing


From here, we can drill into those particular industries to find specific stocks that score well on our long-standing MAER stock selection model. MAER is an acronym for the product’s original name, the Monitor of Analysts’ Earnings Revisions. As the name suggests, it is indeed based on trends in earnings estimate revisions by equity analysts, but also includes our proprietary price momentum indicators and valuation inputs as part of a multi-factor approach. More information about MAER is available in the summary document here, and I have written about our historical studies of the MAER model and its drivers several times: here, here, and here.

If we identify the higher-ranking stocks within higher-ranking industries within higher-ranking sectors and regions, and have a favorable market backdrop (one in which investors are in fact focused on relative fundamentals and not simply running away from all risk), then the odds of success should be significantly higher with all those tailwinds behind us. Of course, “higher odds” is never a guarantee, and a consistent, disciplined approach is needed in order to capture those odds. If we find ourselves in unfavorable market conditions or a situation that our indicators are not designed for (e.g. a global pandemic, shock political/conflict events, etc.), the best option is often to revert to the safety of cash (or back to benchmark for those required to be invested) until the situation stabilizes and the indicators are behaving normally again.

The graphic below summarizes how the Mill Street research process operates, with tools for each of the pieces.

While Mill Street clients can access all of the models, indicators, and tools described here along with my analysis and interpretation of it (and customized analysis based on their specific mandates), I hope that the general thought process may be useful for any reader.

Have a plan, and make the plan before you make any investments

The general guidance I always give to investors of any type is that you should first “have a plan”, then try your best to stick to that plan. It certainly does not have to be my plan, and can be fairly simple or more complex. But whatever it is, the plan should be developed before any investments are made and without looking at the current environment: it is easy to say “I’ll just stay fully invested no matter what” in good times, but then panic when markets get very volatile. Or say “I will only buy quality stocks trading at a discount” but find that cheap stocks can stay cheap a long time, or that “quality” often overlaps with “low beta” and you are unhappy if your portfolio lags when the market is rising strongly.

Diversifying your market toolbox is important too

The bottom line is that it is difficult to actively manage a portfolio to outperform a benchmark (i.e. not just putting 401k money away in index funds, which is generally a good plan for many individuals), and it is more difficult if you only look at the markets from either a top-down perspective or only a bottom-up perspective. Just like portfolios should be diversified, so should the toolkit used to build the portfolios: no single indicator or approach will work all the time. Combining top-down and bottom-up inputs in a systematic way is our approach here, and hopefully we have not “over-diversified” our market analysis metaphors!

Sam Burns, CFA

Chief Strategist

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