In this note, we introduce the Cornerstone Capital Global Sector Strategy Model. The model ranks the 10 GICS in the MSCI All Country World Index. As discussed in detail below, the model employs a quantitative multi-factor methodology to generate sector recommendations based on proprietary measures of valuation and earnings. The Cornerstone Capital model also takes into account environmental, social and governance (ESG) metrics by sector. We will be updating the sector model on a monthly basis.
A Cyclical Tilt
Figure 1 illustrates that our sector equity strategy currently has a cyclical tilt. We are overweighted in Information Technology and Consumer Discretionary and underweighted or neutral in most of the “defensive” sectors: Consumer Staples, Health Care, Utilities. The exception to this cyclical tilt is that we are underweight in Materials. The sector ranks unfavorably both in terms of valuation and earnings. As we discussed in the February edition of The Cornerstone Journal of Sustainable Finance & Banking “pricing power [for Materials] seems questionable in an environment of just moderate global economic growth.
The Key Fundamental Variables: Earnings and Valuation
We start with the assumption that only two things ultimately determine the fair value of equities: earnings and valuation. In the short term, other factors may play a role – e.g., sentiment (“fear” or “greed”), politics (including geopolitical issues), macroeconomic variables (e.g., Central Bank tightening or easing) etc. – but, in the long run, we believe it all comes down to earnings and the valuation of those earnings. A number of factors drive valuation multiples at any point in time, including perceptions of ESG issues.
This is a dynamic model, with factors and factor weightings reviewed on a monthly basis for relevance. The key measures of valuation and earnings are also updated monthly; they can be updated more frequently (e.g., weekly) although a risk here is short-term “noise” in the data that does not persist for a longer period of time. A variation of this model has been in use for a number of years, and has added value in the investment decision process.
The Weighting of Regions versus Sectors
The Cornerstone Capital Global Strategy Model is comprised of a regional element and a sector element. The sectors are the 10 GICS in the MSCI All Country World Index (ACWI). Figure 2 illustrates the sector weights in the MSCI ACWI – currently and in recent years. Note that the weight of the Information Technology sector increased dramatically during the TMT “bubble” of the 1990s, while the weight of the Financials sector increased during the sub-prime bubble that burst in 2007. While our strategy model is tactical in nature, in subsequent research reports we will address optimal strategic sector allocations.
The primary difference between the regional and sector models is the weighting assigned to the valuation and earnings factors. The sector model gives a heavier weighting to earnings while, in the regional model, valuation and earnings have roughly similar weights.
The reason for this is that, in our experience, investors look for sectors that primarily offer relatively strong earnings momentum, and for regions that offer a combination of attractive valuations and earnings momentum.
So, for example, an investor may choose to overweight Japan and be underweight Latin America primarily because of the relative valuations of the two markets. To be sure, however, a region (e.g., Japan) that has a heavy weighting of a sector with strong earnings momentum (e.g., Consumer Discretionary) will likely be overweight, while a region (e.g., Latin America) with a heavy weighting of a sector with weak earnings momentum (e.g., Materials) will likely be underweight. This is indeed the case – we are overweight Consumer Discretionary and Japan, and we are underweight Materials and Latin America – so that the sector and regional models are consistent.
Sector Valuation Factors
In terms of the valuation of a sector, several factors are measured in order to come up with numerical values, which we label “positive,” “neutral,” or “negative” in Figure 1.
These factors include:
- P/E relative to other sectors;
- P/E relative to the historical average for the sector;
- P/E on a “normalized” basis i.e., excluding cyclical peaks and troughs;
- The potential for P/E expansion or contraction.
The first three factors are self-explanatory, while the fourth factor is based on a number of momentum indicators.
Sector ESG Metrics
As we noted above, a number of factors drive valuation multiples at any point in time, including perceptions of ESG issues. We also pointed out that this is a dynamic model, with factors and factor weightings being reviewed frequently.
In this first iteration of the sector equity strategy model, we utilize ESG metrics calculated by MSCI. MSCI ESG Intangible Value Assessment (IVA) provides analysis of over 5,000 global companies’ financially material risks and opportunities arising from environmental, social, and governance factors. At a security level, an Environment Score, Social Score and Governance score are calculated on a 0-10 scale, with 10 being the best in terms of companies’ opportunity or risk exposure and ability to manage that exposure.
Figure 3 illustrates that, according to a Deutsche Bank analysis, Environmental factors are particularly important in the Utilities and Materials sectors, Social factors are particularly important in the Health Care sector and Governance factors are particularly important in the Telecom and Financials sectors.
Sector Earnings Factors
Turning to the earnings of a sector, the model aggregates a number of measures under four broad headings:Earnings momentum: Relative to the MSCI All Country World Index, we calculate if the earnings momentum of a sector has been accelerating, stable or decelerating. We then look at the earnings momentum of one sector relative to another. The resulting numerical values are labeled in Figure 1 as “positive” (accelerating momentum), “neutral” (stable momentum) or “negative” (decelerating momentum).
Earnings revisions: For each of the companies in a sector, we look at the recent trend in earnings revisions by calculating the difference between the number of upward and downward estimate revisions. The data are aggregated, and the resulting numerical values are summarized. A high ratio of upward-to-downward revisions is considered “positive” for a region; conversely a high ratio of downward-to-upward revisions is considered “negative.”
Margins: We look at the margins of each of the companies in a sector – both actual and estimated – and aggregate the data. We assume that relatively high and sustainable margins are “positive” in that they should support earnings growth, while volatile margins are a “negative.” In recent years, margins in the (knowledge-intensive) Information Technology and Telecom sectors have been consistently high, while margins in the (commoditized) Energy sector have been quite volatile. Consequently, Information Technology and Telecom get a higher score for Margins in Figure 1 than Energy.
Share buybacks: Given that corporate earnings are reported on a per share basis, we take into account the amount of net share buybacks that have occurred over the past twelve months in each sector. Once again, we aggregate data from the company level. A large amount of net share buybacks is “positive” for earnings per share growth in a sector, while the opposite (i.e., share issuance) is “negative.”
Ranking Sectors by Weighting Valuation, Earnings and ESG Scores
The values derived from the various measures of valuation, earnings and ESG are weighted, and the sectors are then ranked on the basis of their total “score.” Sectors that are at the very top or very bottom of the distribution are typically ranked “overweight” or “underweight” respectively, while sectors that fall in the middle are typically ranked “neutral.”
Given the quantitative underpinnings of the model, we can look at the dispersion of the “scores” in order to decide on the relative weightings. In other words, a sector’s score might be so high relative to the others in a given month that it is the sole overweight while, in another month, the scores of a number of sectors are closely clustered and they are all assigned the same weighting (e.g., “neutral”).
Combining the Sector and Regional Models
Combining our sector and regional models, Figure 5 illustrates select sector over- and under-weights by region.
Figure 5: Regional and Sector Overview
We are overweight Information Technology in Japan, Emerging Asia and the U.S.; we are overweight Consumer Discretionary in Japan and the U.S. We are underweight Materials and Consumer Staples in the majority of regions.
Michael Geraghty is the Global Markets Strategist at Cornerstone Capital and formerly the founder of Informed Investor, LLC a consultancy specializing in thought leadership that produces bespoke research reports for institutional investors. Michael has over three decades of experience in the financial services industry including working as an investment strategist at UBS and Citi.