Research — Feb 28, 2026

Using PMI® data to optimize small and mid-cap portfolio exposure

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This whitepaper has been created by S&P Global Market Intelligence. S&P Global Market Intelligence's opinions, quotes, credit-related and other analyses are statements of opinion of S&P Global Market Intelligence (and not any other S&P Global division) as of the date they are expressed and not statements of fact or recommendations to purchase, hold, or sell any securities or to make any investment decisions, and do not address the suitability of any security.

S&P Global’s Purchasing Manager’s IndexTM (PMI®) data is a high-frequency, leading economic indicator that is widely viewed by investors, policy makers and business leaders as a benchmark that provides early indications of changes in the business cycle.

Building on previous research and considering the recent increase in scrutiny toward the valuation of mega cap stocks, we explore if signals may be extracted from the PMI data to optimize country exposure in the small- and mid-cap universe. A systematic selection strategy using historical PMI data from October 2007 to January 2026 yielded hypothetical excess returns of 523% against the MSCI World Index, demonstrating the potential value of PMI-derived signals in this setting.

Since December last year, small and mid-cap (SMID) stocks have outperformed large caps.

The ascent of market-cap weighted indices to fresh record highs has yielded strong returns for passive investment strategies. However, this has led benchmark indices to becoming more concentrated in select countries and names. For example, at the time of writing, the top 10 constituents in the MSCI World Index account for 26.6% of the index weight, while 71.2% of the index are US equities1.

Stretched valuations across the “Magnificent 7” and the tech sector, and increased scrutiny as to whether companies’ AI expenditures will eventually translate to greater earnings has led market participants to rotate to other countries and sectors, and broaden out their risk appetite in search of returns.

Since December last year, small and mid- (SMID) cap stocks have outperformed large caps, marking a significant shift when put into the context of their performance relative to blue chips over the past five years (see Figure 1). However, as investors look to the small and medium-sized universe, the importance of robust allocation strategies that are backed by fundamentals rises.

Figure 1: Small and mid-caps have outperformed large caps since back-end of 2025

Figure 1: Small and mid-caps have outperformed large caps since back-end of 2025

Previous analysis has highlighted how investors can use the PMI data — a high-frequency, leading indicator of the business cycle — to create macroeconomic signals that can support country (here) and sector (here and here) allocation in a systematic manner. Crucially, this provides investors with a means to build rules-based strategies for geographical and sectoral selections that are driven by economic forces.

Our previous research has applied PMI signals to market-cap weighted indices. Now, as appetite to rotate away from blue chips and mega-cap stocks grows, we test a systematic selection strategy to the SMID cap universe, yielding hypothetical excess returns of 523% against the MSCI World Index over our back-test period.

Why use PMIs?

The timely monthly publication cadence, as well as the use of a consistent methodology across all surveys compiled by S&P Global, make them an excellent candidate for informing country- or sector-specific views.

We use signals derived from the country-level PMI data to determine the allocation of hypothetical funds into the SMID cap universe in 12 different countries. The case for using PMI data to determine country exposure in the SMID cap universe is compelling. First, constituents are less globalized than their large cap counterparts, meaning they are more sensitive to changes in the domestic business cycle. Second, cyclical sectors carry a much larger weight in the SMID cap universe. For example, at the time of writing, industrials stocks are 21% of the MSCI World SMID Index2, compared to 11.6% in the MSCI World Index.

Using a credible, high-frequency and leading business cycle indicator like the PMI may help investors assess markets where economic conditions are the most supportive of growth for SMID caps, providing an allocation framework that is grounded in macroeconomic fundamentals.

Many economic indicators are limited in their applicability to investment allocation decisions as financial markets will typically have already “priced in” the information they contain. This is commonly seen with official datasets such as GDP. Not only are they typically released with a considerable lag, but these measures are subject to revisions, published infrequently and are also difficult to accurately compare across countries owing to different statistical methodologies. The PMI surveys are not susceptible to these weaknesses, and crucially, they contain new information. It is typical to see PMI releases impact currency, equity and bond markets upon release.

How an investment analyst or portfolio manager could use PMI?

In the previous research, we explored monthly rebalancing in accordance with the release cadence of the PMI. In this paper, we test quarterly rebalancing, which may help investors to reduce the impact of trading costs and portfolio turnover.

We set our portfolio rebalance date as the fifth working day of each quarter. The portfolio is adjusted in month t, in accordance with the data released in month t, month t-1 and month t-2, with returns realized in month t+3 after the release of new PMI data when the hypothetical portfolio is reallocated based on the newest PMI signals.

There are many PMI sub-indices which can be used to extract investment signals. Intuitively, indicators that track trends in business activity and expectations, demand, company pricing power and margins may provide incremental value to investors’ assessments of company, sector and country stock market performance.

Often, the PMI is taken at face value and interpreted accordingly. For example, if the US Composite PMI New Orders Index recorded 54.0 for a particular month, this would signal an increase in demand for US private sector goods and services compared with the month before. It is also common to compare index values against those from prior months. Assuming that previously the index registered 52.0, rising from 52.0 to 54.0 would signal an accelerated pace of growth in sales made by US private sector firms.

From these two data points alone, we have extracted two important pieces of information on the current and expected performance of the US economy. We have ascertained that demand improved on the month and at a quickened pace. These are two positive signals that are suggestive of positive macroeconomic tailwinds for US companies.

Importantly, the PMI allows us to compare these two signals like-for-like with other countries. This lends itself well to constructing country-rotation strategies, which will underpin our hypothetical portfolio as part of this test. 

Building investment signals with PMI: One approach and its rationale

It is important to preface that the signals used in this paper are by no means exhaustive. We encourage interested PMI users to experiment with different signals, data transformations and blends of sub-indices, as alternative approaches could yield stronger excess returns, lower index volatility or a more suitable risk profile.

Our research focused on countries with available Composite PMI data. To build our portfolio, we used the signals derived from the PMI surveys to select an MSCI SMID country index for investment, and we then rotated between countries on a quarterly basis depending on our PMI signals. The countries eligible for selection were Australia, Brazil, Canada, mainland China, France, Germany, India, Italy, Japan, Spain, the UK and the US. The country that exhibited the strongest "buy" signal, according to historical PMI data, was selected. We took this as the country that obtained the highest score, based on the formula below:

𝑆𝐶𝑂𝑅𝐸𝑖𝑡 = 𝑆𝐼𝐺𝑁𝐴𝐿𝑖𝑡 + 𝑃𝐸𝑁𝐴𝐿𝑇𝑌𝑖𝑡

Where  is the cross-sectional z-score of the PMI-derived signal for country i in month t, and  is cross-sectional z-score of the "penalty" for country i in month t.

Scores for each country were calculated at the start of every quarter, going back to the fourth quarter of 2007. The country with the highest score was selected for investment in that quarter, with funds reallocated should the highest-scoring country change the following quarter.

To calculate , we took the PMI New Orders Index and weighted the latest three months of data for each country where the latest month of data (month t-1) was given a weight of 50.0%, month t-2 a weight of 33.3% and month t-3 a weight of 16.7%. Because we were rebalancing quarterly, we wanted to give the most up-to-date information the highest weight in our signal, but we also wanted to account for economic performance in the other months between the rebalance date. Finally, we calculated a cross-sectional z-score to standardize the data for ranking and account for the fact that country PMIs tend to deviate around different means. The PMI New Orders Index is inherently a forward-looking indicator — it measures total sales volumes (both domestic and export) and is a critical gauge of domestic business health. Strong private sector sales growth is a leading indicator of economic expansion, providing a positive fundamental investment signal for companies that operate in that country.

We incorporated other indicators which added incremental information on company performance through . This was calculated by adding together cross-sectional z-scores of (i) the difference between the trailing three-month moving average of the PMI Output Prices Index — a pricing power gauge — and its value in month t, and (ii) the difference between the trailing three month average of a margins proxy (calculated as the PMI Output Prices Index less PMI Input Prices Index) and its value in month t. Countries were therefore penalized for having weakening pricing power or deteriorating margins, which, all else equal, was an adverse earnings indicator.

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Methodology notes

In this paper, we used historical PMI data and experimented with a sample period from October 2007 to January 2026. This sample period was selected based on the availability of MSCI data and to maximize the availability of territories with historical Composite PMI data. MSCI SMID cap country indices (US dollar, net dividend, total returns) are based on data available via S&P Global Market Intelligence.

For more information on the methodology and results of our experiments, please contact economics@spglobal.com.
 

Read the full whitepaper and results

This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.

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Research