HomeMacro TrendsMacro trends and equity allocation: a brief introduction

Macro trends and equity allocation: a brief introduction


Jupyter Notebook

Macroeconomic trends affect stocks differently, depending on their lines of business and their home markets. Hence, point-in-time macro trend indicators can support two types of investment decisions: allocation across sectors within the same country and allocation across countries within the same sector. Panel analysis for 11 sectors and 12 countries over the last 25 years reveals examples for both. Across sectors, export growth, services business sentiment, and consumer confidence have predicted the outperformance of energy stocks, services stocks, and real estate stocks, respectively. Across countries, relative export growth, manufacturing sentiment changes, and financial conditions have predicted the outperformance of local stocks versus foreign ones for the overall market and within sectors.

The below post is based on proprietary research of Macrosynergy.

A Jupyter notebook for audit and replication of the research results can be downloaded here. The notebook operation requires access to J.P. Morgan DataQuery to download data from JPMaQS, a premium service of quantamental indicators. J.P. Morgan offers free trials for institutional clients.
Also, there is an academic research support program that sponsors data sets for relevant projects.

This post ties in with this site’s summary of macro information inefficiency in financial markets.

Macro trends and equity allocation

Macroeconomic trends influence expected earnings and discount factors of all stocks. However, the impact of specific macro trends can be different or even divergent across types of stocks. For example, some businesses are more sensitive to the business cycle than others. Also, each business is particularly sensitive to economic conditions in its dominant markets. This implies that in the equity space, macro trends can support allocations across two dimensions.

  • The first is allocation across sectors within the same currency area. For example, some sectors are naturally more sensitive to growth, while others are more susceptible to financial conditions or inflation headwinds.
  • The second is allocation across currency areas, particularly within the same sector. For real money investors, such allocations typically involve both currency and pure equity risk. Relative economic trends, such as growth differentials, matter if they have significant predictive power for the joint currency or equity portion of the return and if cross-country divergences are common and sizable.

The data

In this brief example post, we consider 11 sectors according to Global Industry Classification Standard (GICS) classification and 12 currency areas. We further consider a small array of information states of macro trends of the J.P. Morgan Macrosynergy Quantamental System (JPMaQS).

  • The main targets of the cross-sector analysis are daily local-currency cash equity returns for both country equity indices and country-specific sector indices. The sectors considered are consumer discretionary goods, consumer staples, communication services, energy, financials, health care, industrials, information technology, materials, real estate, and utilities. The focus is on relative cross-sector returns, i.e., returns of one sector minus the average return of all sectors.
    The data is sourced from the J.P. Morgan SIFT database, a DataQuery dataset consisting of pricing and fundamental data for 20,000+ indices. The small subset of return data used in this post is also available on JPMaQS (view documentation here). Local-currency returns are used for Australia (AUD), Canada (CAD), Switzerland (CHF), the euro area (EUR), the UK (GBP), Israel (ILS), Japan (JPY), Norway (NOK), New Zealand (NZD), Sweden (SEK), Singapore (SGD), and the United States (USD).
  • The main targets of cross-country analysis are USD-denominated equity index returns for the 11 sectors and the 12 currency areas. These have been approximated as the sum of (1) generic FX forward returns of a short USD long local currency position, (2) excess cash equity returns over and above the local funding rate, and (3) the USD funding rate. The focus is on relative cross-country returns, i.e., returns of one country for one specific sector versus the average return of all countries for the same sector.

For a meaningful analysis of the impact of economic trends on equity sector returns, we use JPMaQS’ real-time information states of the market and the public with respect to an economic concept. These states are suitable for testing relations with subsequent returns and backtesting related trading strategies.

The types of macro trends considered include JPMaQS information states of GDP growth, industrial production growth, export growth, private credit growth, producer price inflation, consumer price inflation, and real bond yields. as well as survey-based sentiment scores for manufacturing businesses, services businesses, and consumers. The exact definitions of the indicators are explained in the sections below.

The analysis

We analyze predictive relations by means of panel regression, i.e., the joint consideration of macro trends and relative equity returns over a set of cross-sections (currency areas or sectors). Country experiences are not independent and are subject to common factors. Simply stacking data can lead to “pseudo-replication” and overestimated significance of correlation. A better method is to check significance through panel regression models with period-specific random effects. This technique adjusts targets and features of the predictive regression for common (global) influences. The stronger these global effects, the greater the weight of deviations from the period-mean in the regression. (view post here).

The below examples of predictive relations between macro trends and either cross-sector or cross-country returns are an intersection of plausible hypotheses and empirical evidence. However, the presentation of the example has a selection bias, as the post does not show hypotheses without empirical backing.

Examples of cross-sector predictions

Export growth and energy sector outperformance

The idea is that strong export growth in local-currency terms, over and above the medium-term trend in nominal GDP growth, positively predicts the relative performance of energy sector returns. The energy sector contains companies that extract, store, market, and ship oil, gas, coal, and other fuels. High nominal export growth is related to strong energy exports themselves or to strength in other parts of the industry sector that will benefit demand for energy indirectly.

Export growth is calculated as the difference between information states of local currency exports, % over a year ago, 3-month moving average (view documentation here) minus the sum of the past five years of GDP growth (view documentation here) and the currency area’s effective inflation target (view documentation here).

Panel regression analysis for 2000 to 2024 (Apr), for ten countries excluding Switzerland (which does not have an energy sector index), shows a significant predictive relation between end-of-period information states of export growth and relative energy sector returns one month or one-quarter ahead. Also, the monthly balanced accuracy of predicting positive and negative relative energy returns has been respectable at 53%.

The supported strategy is to bias equity allocations towards energy sector stops in countries and times of high export growth and away from the energy sector in countries and periods of low export growth.

Related indicators, such as industrial production growth and industrial producer price inflation, have displayed similar predictive power for the outperformance of energy sectors.

Services business confidence and health care and utilities outperformance

The idea is that positive business sentiment in services surveys predicts good subsequent business results in key services sectors, i.e., health care and utilities. Services business surveys are released for 10 of the 11 countries with very short time lags and are plausibly related to recent business performance. Services survey scores are point-in-time business confidence index values that are normalized based on past expanding data samples in order to replicate the market’s information state (view documentation here). A positive value of 1 means that the survey has one standard deviation on the positive side based on data up to that date.

Panel regression since 2000 for ten countries excluding Norway (which does not have a services survey) and excluding extreme survey outliers (over ten standard deviations) shows a significant positive predictive relation between end-of-period information states of services business sentiment scores and relative health care sector returns one month or one quarter ahead.

Panel regression also reveals a significant positive predictive relation between end-of-period information states of services business sentiment scores and subsequent relative utilities sector returns at the quarterly frequency. The relation is also positive at a monthly frequency, but the probability of significance is a bit below 90%. Nevertheless, the balanced accuracy of predicting the direction of relative returns with services score has been over 53% at a monthly level.

The evidence supports a strategy that biases allocations to the health care and utilities sectors (and maybe other services sectors) across countries during times of high local services business sentiment and allocations away from these sectors during times of low local services confidence.

Consumer confidence and real estate outperformance

Here, the idea is that strong household sentiment fosters particular demand for residential real estate. Moreover, circumstances that support household confidence, such as low inflation and low real interest rates, also support demand for housing,

JPMaQS’ consumer confidence scores capture information states of consumer confidence. For these point-in-time surveys, confidence values are transformed into z-scores based on past expanding data samples in order to replicate the market’s information state on survey readings relative to what is considered “normal” (view documentation here).

Empirical analysis confirms a positive predictive relation between consumer confidence scores and subsequent relative real estate returns across ten countries (Singapore has no survey score). According to the Macrosynergy panel test, the probability of significance has been on the low side, around 85% at both a monthly and quarterly frequency. However, the balanced accuracy of directional return predictions has been around 52% monthly, and the non-parametric Kendall coefficient of the predictive relation has been significant with 99% probability.

The data also support the related hypothesis that high consumer price inflation predicts relative real estate returns. Here, the predictor is excess core inflation, i.e., the difference between information states of annual core inflation (view documentation here) and the effective inflation target. Excess core inflation has been a highly significant negative predictor of subsequent relative real estate sector returns in a panel of all 11 countries at both a monthly and quarterly frequency.

The supported strategy is to bias equity allocations towards the real estate sector in times and countries where consumer confidence is strong and inflation low and to bias allocations away from real estate in cases where conditions for consumers are poor.

Examples of cross-country predictions

Relative export growth and country outperformance

High local-currency export growth is often related to strong economic performance and undervalued currencies. Thus, USD-denominated returns in countries with stronger export growth are likely to outperform those in countries with weaker export growth. This is particularly more plausible, as local-currency export growth is not as closely followed by financial markets as other timely high-frequency indicators.

Relative export growth here means relative information states of annual export growth rates. Otherwise, these quantamental indicators are the same that were used for cross-sector predictions above.

There is strong empirical evidence for a positive predictive relationship between relative export growth and relative cross-country equity performance across various sectors. For the overall country indices, the linear predictive relationship in a panel of 11 countries and 2000-2024 is highly significant at a monthly and quarterly frequency. Individual sectors for which this relationship is modestly significant include industrials and energy.

This evidence supports a strategy that biases allocations towards countries with higher relative export growth across sectors.

Relative manufacturing confidence changes and country outperformance

Manufacturing is a very cyclical economic sector, being a key constituent of large business cycles and smaller “mini-cycles”, often related to demand shocks and subsequent inventory dynamics. Almost all countries release timely manufacturing business surveys for this reason. Despite methodological differences in surveys, relative changes in manufacturing business confidence scores should positively predict the relative performance of manufacturing businesses across countries and, by extension, the relative performance of equity returns for the industrial sector and maybe even the overall business sector. The relative improvement in business sentiment also bodes well for the currency return portion of the USD returns.

For this analysis, we use information about changes in manufacturing business scores. As for other surveys, scores are seasonally adjusted confidence indicators that are transformed into z-scores based on past expanding data samples to replicate the market’s information state on survey readings relative to what is considered “normal” (view documentation here). In particular, the focus is on information states of the changes in the survey score taken in the last three months over the previous three months or last quarter over the previous quarter.

There has been a positive and significant predictive relation between relative changes in the business confidence score of one country versus the others and subsequent monthly and quarterly relative returns for industrial stocks. Since this would b a more short-term effect the relation is particularly significant at a monthly frequency, with a probability of significance near 100%.

A positive and significant predictive relation between relative manufacturing sentiment changes and relative USD returns across countries can also be found in overall equity indices that include all sectors as well as in the consumer durables and staples sectors in particular.

These findings support strategies that bias allocations towards countries with relative improvements in industry business sentiment, particularly within the industrial sector.

Relative real yield curve steepness and country outperformance for finance stocks

The final idea is that standard macro indicators of banking profitability predict the relative performance of dollar returns of financial stocks across currency areas. In particular, we postulate that a steeper real yield curve bodes for better returns since a large part of banking services is term transformation.

Real yield curve steepness is measured as the difference between a real 5-year interest rate swap yield and a real 2-year interest rate swap yield. Yields become real by subtracting estimates of inflation expectations from nominal yields. We should use real yields rather than nominal yields because inflation expectations can be vastly different across countries, particularly since we included emerging markets in the data panel. Here, we use out-of-the-box real yields of JPMaQS based on information states of formulaic inflation expectation estimates (view documentation here).

The evidence of a positive predictive relation between relative real yield curve steepness and subsequent relative returns on financial stocks across countries is significant at both the monthly and quarterly frequency. On a related note, there has also been a positive and modestly significant relationship between relative private credit growth and relative cross country financials returns.

These findings support strategies that bias allocations for financial stocks to countries with steeper real yield curves. A broader index of macro conditions for financials’ profitability that includes bank credit growth may be even more appropriate.

Ralph Sueppel is managing director for research and trading strategies at Macrosynergy. He has worked in economics and finance since the early 1990s for investment banks, the European Central Bank, and leading hedge funds.