Citi Research25 September 2026

Global Quantitative Insights

Style Leadership Remains Elusive as Momentum Reasserts

September style performance reinforced a key theme of recent months, Value's rotation has been meaningful, but not yet durable. Despite supportive macro conditions earlier in the year, Value has struggled to extend leadership globally, while Momentum and Growth have recovered some lost ground and factor leadership has become increasingly inconsistent. Narrowing valuation spreads, elevated crowding and weakening breadth argue against extrapolating the first-half Value trade too aggressively. While higher energy prices and further yield normalisation could continue to provide intermittent support for Value, we view the upside as increasingly region-specific rather than global in nature. As such, we retain a selective preference for Growth in our global style recommendations.

David T Chew · Richard W Schlatter · Anju Bhandari · Yue Hin Pong · Kuldeep M Tilara下载原始 PDF

Citi's Take

Style Performance – Rotation Without a Durable Leader — September's style performance highlights a market struggling to establish a durable leadership regime. While Value benefited from Q1 strength and the July momentum unwind, subsequent performance has lacked persistence. Momentum factors have rebounded, Value performance has become increasingly fragmented across regions, and both Pure Value and Pure Growth have generated weak returns. The result is a more volatile factor backdrop with declining persistence and growing uncertainty around the durability of Value leadership.

Valuations and Crowding: Diminishing Asymmetry for Value — While the macro backdrop remains supportive of Value, the trade no longer benefits from the same degree of valuation and positioning asymmetry. Crowding has increased within Value, and the valuation gap. More importantly, the valuation gap between the cheapest Value stocks and the highest-growth companies has narrowed. As the relative valuation discount has compressed, the forward-looking valuation tailwind that previously supported Value has diminished.

Elevated Dispersion Amplifies Active Positioning — Cross-sectional return dispersion across styles and industries has risen considerably while correlations have fallen, increasing the opportunity but equally the risk associated with active positioning. Current estimates suggest style exposures carry roughly 70% greater return sensitivity than normal, with industry exposures even higher. With Value-Growth leadership unresolved, crowding risks persistent and mixed macro signals, elevated dispersion argues for disciplined risk management rather than aggressive factor concentration.

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Style Review

September's month-to-date (Figure 1) style returns reveal a factor landscape in which clean style leadership has become notably elusive. Quintile long short style performance was led by a recovery for Price Momentum, up +3.7% globally, and positive across all regions except Japan, representing a meaningful recovery from the sharp unwind witnessed in late June/early July. Earnings Momentum improved with the global long/short spreads generating +2.7% MTD and have been particularly strong +5.4% in the US. Beyond the Momentum complex, however, the picture is one of compressed and directionless returns.

Value is fractionally negative at the global level (-0.7%), weak in the US (-5.2%) while posting modest gains in Japan (+4.0%) and Europe (+1.5%) — a divergence that speaks to genuinely distinct macroeconomic backdrops rather than a coherent global style regime.

Quality (-0.6%) and Low Risk (-0.8%) continue to underperform, with the latter suffering a particularly acute -5.2% decline in the US. Critically, with sector and country (and other style) effects stripped away via a pure style framework (Figure 2), both Pure Value and Growth performance remain weak, suggesting that latent sector or country beta have had little influence on returns. The sole exception remains Momentum, which retains a positive pure return of +2.2% globally and +3.6% in Europe, indicating genuine factor alpha rather than a sector-driven artefact.

Figure 1. Figure 1. MTD – Global Styles Performance - Momentum

Figure 2. Figure 2. MTD - Pure Styles - Leadership Patterns

Figure 3. Figure 3. MSCI World - Style Leadership Remains Unclear

Figure 4. Figure 4. 2026 YTD Style Performance by Region

Year-to-date returns (Figure 32,Figure 4) paints a more complicated picture. At the global level, Price Momentum still leads all styles with a +6.9% return (despite the momentum crash), though this masks extreme regional heterogeneity: EM Price

Momentum has recovered a large degree of losses and is up+22.5% YTD while the US momentum retains a modest +3.6%. Value presents the most striking regional divergence: European Value has delivered a robust +11.8% YTD, while US Value is deeply negative at -6.1% YTD.

Japan Value (+6.6%) continues to benefit from the Bank of Japan's rate normalisation backdrop and improving corporate governance. The secular underperformers YTD are unambiguous: Low Risk (-8.0%) and Quality (-6.2%) are the worst-performing styles globally as the risk-on environment and steepening yield curves have actively penalised defensives.

The long-run MSCI World style chart (Figure 3) contextualises the year's journey so far: Q1 was characterised by unambiguous Value leadership, April-May saw a violent Momentum unwind driven by the AI-capex re-rating, and the subsequent recovery has left the factor landscape in a volatile but near directionless consolidation into September — and consistent with a late-cycle environment in which macro cross-currents are increasingly difficult for clean systematic factor allocation.

Figure 5. Figure 5. Long/Short Decomposition: Short Book Carrying the Load (MTD)

Equally interesting for September has been the long/short quintile performance breakdown (Figure 5). Across almost every style and region, long-side returns are conspicuously absent, with the short side providing the primary source of factor return.

In the US, the Value long/short spread of approximately -3.0% is almost entirely attributable to long-side weakness. Low Risk tells a similar story in the US, where long-side exposure to low-volatility stocks has been a significant drag as the market has rotated toward higher-beta names. This dynamic — in which factors are generating returns predominantly through short legs rather than long legs suggests that systematic strategies are profiting from the underperformance of stocks that should in theory be unattractive, rather than from the appreciation of fundamentally sound positions. The partial exception is Size, where long-side exposure to large-cap names is contributing positively globally (+2.8% in the US long-short). For Momentum, the long/short decomposition is more balanced in Europe and EM — lending structural credibility to the factor's current rebound — though in the US the recovery is also partially short-driven, warranting caution over its durability.

Figure 6. Figure 6. Choppy Value/Growth Leadership but Volatility is easing

Figure 7. Figure 7. Crowding in Growth remains a concern

The weekly Value-Growth spread (Figure 6) tells the story of Value rotation that has merged twice but has yet to produce a meaningful sustained regime in 2026. Q1 2026 saw an unambiguous bid for Value. April then delivered a sharp reversal: risk-on sentiment and an AI-capex re-rating drove the V-G spread down to a YTD trough. The subsequent correction saw July's spread exploded to +9.1%, as crowded Momentum and Growth longs were forcibly unwound.

Yet what is most instructive about the post-July period is precisely what has not happened. Rather than consolidating at elevated levels consistent with prior durable Value rotation episodes, the spread has since oscillated either side of zero in a choppy, alternating pattern (Figure 6).

This pattern is consistent with previous rotation episodes in which Value failed to sustain leadership: the structural conditions for a durable regime shift — simultaneous broad earnings delivery, a sustained re-steepening of yield curves, and a meaningful reduction in Growth crowding — have not cohesively aligned.

The European market is a notable exception to this global equivocation. European Value's +11.8% YTD return, underpinned by a more value-oriented market composition in Financials and Energy, a hawkish PMI backdrop, and a structurally lower exposure to the AI-capex theme, has produced a regime that looks more durable than the global picture. For European quant investors, the signal is cleaner; for global allocators, the take-away from Figure 6 is one of caution: the rotation has been meaningful but incomplete.

Leveraging our style crowding model, we assess how crowding levels in Value and Growth have evolved through the recent rotation period (Searching for Alpha: Style Crowding in Asia - Getting Ahead of the Crowd). The late June/July rotation led to significant step down for crowding within Growth, but levels have since recovered, while Value has seen a significant rebound towards the highest levels seen in almost 5 years but has since marginally eased.

Style crowding (Figure 7) provides an important forward-looking dimension to the style rotation debate. Our style crowding measures confirm that the June/July unwind delivered a genuine, if partial, de-crowding of Growth positioning — but the subsequent recovery has been swift. Growth crowding has retraced but recovered and remains elevated, suggesting that the structural overhang of concentrated positioning in Growth has not been resolved by a single month of spread widening. This is not without precedent: in prior rotation episodes — most notably the Value recovery of Q4 2022 — crowding in Growth remained sticky for extended periods even as the factor underperformed. What is more striking, however, is the behaviour of Value crowding. Rather than declining — as one might expect if investors were actively rotating out of Value following its relative underperformance from April-May — Value crowding has rebounded sharply and is now approaching its highest levels in nearly five years, having only marginally eased in recent weeks.

This dynamic is a double-edged signal: on one hand, it reflects genuine investor conviction in the Value thesis and validates the narrative of a structural rotation; on the other, it introduces a meaningful crowding risk to Value itself. Taken together, the crowding data implies that the rotation has been substantive but remains unfinished — Growth has not been sufficiently unwound, and Value's elevated crowding introduces its own fragility.

Figure 8. Figure 8. Valuation convergence leaves fewer mispriced opportunities between Cheap and Expensive names

Figure 9. Figure 9. Valuations - Cheapest Value stocks close the gap on top Growth names

While the macro narrative has been supportive of Value in 2026, the valuation evidence (Figure 8,Figure 9) paints a more cautionary picture for Value investors. Figure 8 illustrates the long-run market-relative valuations of the cheapest and most expensive quintiles for Value. While the absolute valuation gap between cheap and expensive stocks remains wide by historical standards, spreads are narrowing from long run peaks, leaving meaningfully fewer mispriced opportunities.

Figure 9 sharpens this argument considerably. Plotting the 5-year normalised valuation for the highest-growth stocks against the cheapest value stocks, the zscore differential — which peaked in 2021 has since collapsed. In other words, the valuation premium that Growth commands over the cheapest Value names is a fraction of what it once was.

The cheapest value stocks have closed a substantial portion of the relative valuation gap with the top growth cohort, reducing the forward-looking valuation tailwind that may would sustain Value outperformance. The current environment, on a pure valuation basis, is therefore less supportive of a renewed Value bid and arguably more amenable to a selective return to Growth, particularly in segments where the quality of earnings growth justifies the now-narrower valuation premium.

While market continue to perform strongly, the macroeconomic backdrop is turning more contentious. In Europe and parts of Asia (Japan), PMIs indicators point towards economies that continuing to see a healthy expansion (European Economics - Eurozone: All-Around Hawkish PMIs), elsewhere the narrative is less clear-cut. US composite PMI momentum though positive has been falling and is flat across China and EM (Figure 10). Taken in combination with a rising and higher rate environment, the economic backdrop suggests one of a late-cycle backdrop, where restrictive financial conditions could outweigh growth momentum.

Figure 10. Figure 10. PMIs – still expanding but losing steam

Figure 11. Figure 11. US - the broadening window closes

Global PMIs remain within expansionary range, however, an PMI momentum has been decelerating in some regions, while China and Emerging Markets been flat. This pattern expansion persisting but losing velocity. Taken in combination with a rising and higher rate environment in which restrictive financial conditions may exert structural drag, the macro backdrop is increasingly one in which cyclical Value factors could struggle. Figure 11 outlines a composite breadth signal which had been firmly positive as recently as August, has turned negative at mid-September, with every component now pointing in the same direction: narrowing.

The top-down late-cycle macro thesis finds compelling bottom-up corroboration in our analysis of US rate sensitivity using our proprietary RAM risk model. With the 10-year US Treasury yield crossing the 5% threshold and the Federal Reserve delivering a hawkish 25bps rate hike, interest rate risk has become another defining axis of stock-level return dispersion.

Equally this raises questions on the impact of the rate cycle on Growth stock valuations and what it means for forward looking returns.

To better understand the possible implications, we simulate the performance of a strategy ranking constituents within quintiles by their exposure to rates (using 10year rate sensitivity), where the High Rate-Beta quintile captures stocks that historically outperform in a rising yield environment with the low beta being those potentially negatively exposed and backtesting in a monthly rebalanced portfolio from December 1999 to August 2026 across classified rate regimes1.

Figure 12. Figure 12. Positive Rate-Beta stocks outperform, but not complete capitulation of shorts.

High Rate-Beta equities have surged, generating average returns that approximately double the broader market, with outperformance almost exclusively from the long leg (Figure 12) In the prevailing rising rate regime, positive rate-beta stocks outperform on the long leg, while long-duration Growth names experience headwinds consistent with their sensitivity to yield moves.

The underperformance is measured rather than extreme or complete capitulation, and perhaps not so severe as to eliminate the structural earnings premium that quality Growth companies could command.

1 Regimes are dynamically classified using a 2-year rolling standard deviation of month-over-month rate changes, defining periods as "Rising" or "Falling" when monthly rate changes exceed +1.0 SD or fall below -1.0 SD, respectively.

Figure 13. Figure 13. Cross Sectional Dispersion rises – opportunity or trap

Figure 14. Figure 14. Active style and industry exposures carry substantially greater performance implications than normal.

The final and operationally consequential observation for active investors’ concerns the level of cross-sectional return dispersion2 observed across stocks, styles and industries in recent weeks. Figure 13 presents the dispersion characteristics as a percentile rank relative to its own history.

The spread of returns between winners and losers within each category is also a determinant of how much active factor tilts can contribute to, or detract from, relative portfolio performance. At present, both style and industry dispersion stand at approximately 1.7x and 1.8x their respective historical median levels, placing them near the upper end of the percentile distribution observed since 1999.

Figure 14 translates this elevated dispersion estimate towards portfolio risk terms. A 10% active exposure to a style factor currently generates approximately 79bps of relative performance for a one standard deviation factor move, compared with only 47bps under median historical conditions — a ~70% increase in factor return sensitivity.

Industry exposures exhibit a similarly elevated profile, with current dispersion implying ~82% greater return contribution relative to historical norms. While this environment could well be regarded as profitable for accurate style/industry selection. The same rule applies symmetrically on the downside: for an incorrectly positioned style or industry exposure — whether intentional or an unintended byproduct of stock selection — carries materially greater performance consequences than in a typical environment.

In the context of the style uncertainty documented throughout this report — a Value-Growth spread that has twice failed to establish a sustained regime, crowding risks that cut in both directions, and a macro backdrop that is transitioning rather than decisively turning, the elevated dispersion environment argues powerfully against concentrated or aggressive factor tilts.

The confluence of evidence across style performance, factor dynamics, valuation, macro, breadth and dispersion presents a coherent and mutually reinforcing case

2 Cross-sectional dispersion is proxied by the cross-sectional standard deviation of 12-week rolling stock/style/industry return volatility, smoothed using an HP filter for retaining a pro-Growth style preference at the global level. While continued yield curve steepening could prove a headwind for Growth, we don’t think this alone is decisive enough to warrant a change of view.

While there are potential regional pockets for Value outperformance. The case for Value as a global recommendation shows greater risks. A late-cycle macro environment where market breadth is starting to narrow once again, and restrictive financial conditions are beginning to assert themselves and a weakening value narrative leave the style less favourable.

The principal caveat to this view is geographic: Europe remains a structurally distinct Value regime, where hawkish PMI dynamics, a more value-oriented sector composition in Financials and Energy, and limited AI-capex exposure have produced Value leadership that appears more durable than the global picture. We remain constructive on Growth and Neutral on Value globally, while recognising the European exception. However, elevated dispersion, persistent crowding risks and evolving market leadership reinforce a key lesson of the current regime: precision in factor selection is likely to be rewarded more consistently than conviction alone, favouring measured rather than aggressive style tilts.

Figure 15. Figure 15. Current Style Recommendations

Developed Markets

Developed Markets: Factor Performance and Volatility

Figure 16. Figure 16. Factor Performance and Volatility

Developed World: Last Month’s Style Leaders and Laggards

Figure 17. Figure 17. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, Last Month

Developed World: LTM Leaders and Laggards

Figure 18. Figure 18. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, LTM

Developed World: Macro Factor Sensitivities3: Regional

Figure 19. Figure 19. GDP Weighted Global Bond Yield (G10)

Figure 21. Figure 21. Commodities (GSCI)

Figure 24. Figure 24. Emerging Market Sovereign Bond Yield

Figure 26. Figure 26. Market (MSCI AC World)

3 The above charts illustrate predicted relative returns from our proprietary risk model (Global RAM) for a one standard deviation change in the underlying macroeconomic factor.

Developed World: Macro Factor Sensitivities4: Styles

Figure 27. Figure 27. GDP Weighted Global Bond Yield (G10)

Figure 29. Figure 29. Commodities (GSCI)

Figure 32. Figure 32. Emerging Market Sovereign Bond Yield

Figure 34. Figure 34. Market (MSCI AC World)

4 The above charts illustrate predicted relative returns from our proprietary risk model (Global RAM) for a one standard deviation change in the underlying macroeconomic factor.

Developed World: Relative Style Valuations

Figure 35. Figure 35. Estimates Momentum

Developed World: Net Sector Weights

Figure 48. Figure 48. Estimates Momentum

Emerging Markets

Emerging Markets: Factor Performance and Volatility

Figure 49. Figure 49. Factor Performance and Volatility

Emerging Markets: Last Month’s Style Leaders and Laggards

Figure 50. Figure 50. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, Last Month

Emerging Markets: LTM Leaders and Laggards

Figure 51. Figure 51. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, LTM

Emerging Markets: Macro Factor Sensitivities5: Countries/Regions

Figure 52. Figure 52. GDP Weighted Global Bond Yield (G10)

Figure 54. Figure 54. Commodities (GSCI)

Figure 57. Figure 57. Emerging Market Sovereign Bond Yield

5 The above charts illustrate predicted relative returns from our proprietary risk model (Global RAM) for a one standard deviation change in the underlying macroeconomic factor.

Figure 59. Figure 59. Market (MSCI AC World)

Emerging Markets: Macro Factor Sensitivities6: Styles

Figure 60. Figure 60. GDP Weighted Global Bond Yield (G10)

Figure 62. Figure 62. Commodities (GSCI)

6 The above charts illustrate predicted long/short style returns from our proprietary risk model (Global RAM) for a one standard deviation change in the underlying macroeconomic factor.

Figure 65. Figure 65. Emerging Market Sovereign Bond Yield

Figure 67. Figure 67. Market (MSCI AC World)

Emerging Markets: Relative Style Valuations

Figure 68. Figure 68. Estimates Momentum

Emerging Markets: Net Sector Weights

Figure 81. Figure 81. Estimates Momentum

US

US: Factor Performance and Volatility

Figure 82. Figure 82. Factor Performance and Volatility

US: Last Month’s Style Leaders and Laggard

Figure 83. Figure 83. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, Last Month

US: LTM Leaders and Laggards

Figure 84. Figure 84. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, LTM

US: Macro Factor Sensitivities7: Sectors

Figure 85. Figure 85. GDP Weighted Global Bond Yield (G10)

Figure 87. Figure 87. Commodities (GSCI)

Figure 90. Figure 90. Emerging Market Sovereign Bond Yield

7 The above charts illustrate predicted relative returns from our proprietary risk model (Global RAM) for a one standard deviation change in the underlying macroeconomic factor.

Figure 92. Figure 92. Market (MSCI AC World)

US: Macro Factor Sensitivities8: Styles

Figure 93. Figure 93. GDP Weighted Global Bond Yield (G10)

Figure 95. Figure 95. Commodities (GSCI)

8 The above charts illustrate predicted long/short style returns from our proprietary risk model (Global RAM) for a one standard deviation change in the underlying macroeconomic factor.

Figure 98. Figure 98. Emerging Market Sovereign Bond Yield

Figure 100. Figure 100. Market (MSCI AC World)

US: Style Sector Composition

Figure 101. Figure 101. Estimates Momentum

US:

Figure 108. Figure 108. Estimates Momentum

US: Net Sector Weights

Figure 121. Figure 121. Estimates Momentum

Europe

Europe: Factor Performance and Volatility

Figure 122. Figure 122. Factor Performance and Volatility

Europe: Last Month’s Style Leaders and Laggards

Figure 123. Figure 123. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, Last Month

Europe: LTM Style Leaders and Laggards

Figure 124. Figure 124. Performance Spread Between High (Top 20%) and Low (Bottom 20%), and Pure Factor Portfolios, LTM

Europe: Macro Factor Sensitivities9: Sectors

Figure 125. Figure 125. GDP Weighted Global Bond Yield (G10)

Figure 126. Figure 126. Commodities (GSCI)

Figure 127. Figure 127. Commodities (GSCI)

Figure 130. Figure 130. Emerging Market Sovereign Bond Yield

9 The above charts illustrate predicted relative returns from our proprietary risk model (Global RAM) for a one standard deviation change in the underlying macroeconomic factor.

Figure 132. Figure 132. Market (MSCI AC World)

Europe: Macro Factor Sensitivities10: Styles

Figure 133. Figure 133. GDP Weighted Global Bond Yield (G10)

Figure 135. Figure 135. Commodities (GSCI)

10 The above charts illustrate predicted long/short style returns from our proprietary risk model (European RAM) for a one standard deviation change in the underlying macroeconomic factor.

Figure 138. Figure 138. Emerging Market Sovereign Bond Yield

Figure 140. Figure 140. Market (MSCI AC World)

Europe: Style Sector Composition

Figure 141. Figure 141. Estimates Momentum

Europe: Relative Style Valuations

Figure 148. Figure 148. Estimates Momentum

Europe: Net Sector Weights

Figure 161. Figure 161. Estimates Momentum

Appendix: Construction Style Indices and Calculation of Style Performance

Style Descriptors

The first step involves the identification of the most important systematic drivers of risk and return across global markets over the long term. Using results from the extensive style performance research published by the Citi Global Quantitative Research team together with empirics documented within the Academic literature we arrive at the following styles: Size, Value, Growth, Low Risk, Quality, Price Momentum and Estimates Momentum.

For each style factor, we then select a list of descriptors that best represent that specific attribute. For example, we use a range of defensive and cyclical pricebased ratios to describe the Value attribute. These descriptors are then combined to arrive at a style factor loading for each stock in a given MSCI universe.

Stock universes are based on MSCI indices, and we have a complete history of styles/factors back to 1995. Figure 159 lists the styles that we cover together with the descriptors used in the construction of each index. One notable absence from our style classification is GARP. We chose not to include this style because as a distinct style, this could be constructed from combining Value and Growth.

Figure 162. Figure 162. Style Factors and Descriptors

When building our style factor loading, we start by winsorizing the descriptor data to eliminate outliers. After this we normalise this data and combine it on an equal weighted basis to form the style composite factor.

Calculation of Raw Style Performance

Calculating the Style Betas

Y  B ' F

n = number of stocks; m = number of styles, country and sectors.

 

Y '

  1

B  F ' F F '

We calculate raw style performance using the standard factor mimicking approach. In each universe stock are ranked using each composite

Construction of Pure Style Indices

For a point in time, given the style factor loadings, country/sector membership dummies and our desired orthogonal style exposures, we then employ a simple matrix inversion to compute a set of orthogonal style factor portfolios. The last step involves a simple linear combination of the monthly style factor portfolios with the next-period returns of each constituent stock measured over a monthly or daily periodicity to generate an index of style returns. The result is that each index has a unit exposure to any one style and zero exposure to all other styles, at the time of the monthly rebalance.

In order to calculate the betas, we are effectively constructing the style portfolios. Following this, the style portfolio exposures are function of the style loadings and their corresponding betas. In matrix notation (simplified version):

Where (the equation above is contemporaneous, hence ignoring the time subscript):

Y= matrix (m,m) factor portfolio exposures; B = matrix (n,m) of style portfolio weights (betas); F = matrix (n,m) stock exposures to style/country/sectors;

Given our desire for set of linearly independent style portfolios,Ycan take the form of an identity matrix, F is our style factor loadings so we are therefore solving for B .

The result of B is a set of stock level weights (or betas) for each style that multiplied with the style factor loadings produces orthogonal style exposures11. Or in other words, each style portfolio has a unit exposure to itself and zero exposure to all other styles.

11 We also constrain the weighted sum of the sectors to be zero.

Construction of Style Return Indices

P  R ' B

t t  1

Given we know

matrices is

As part of the construction of the style portfolio exposures, at a point in time, we are left with a set of style portfolios and their corresponding long-short stock weights. Bringing this part of the style index construction to return space (i.e., the B matrix), we assume that cross-sectional stock returns over any period can be expressed with the following equation:

Where: tP = a row vector (1,m) of pure style portfolio returns from time t-1 to time t

tR = a column vector (n,1) of stock returns from time t-1 to t

= matrix (n,m) of style portfolio weights formed at time t-1  1

tR and we have calculated tB

, the product of the these two  1

tP , the return of each pure systematic factor (in total, m) at time t.

Appendix: Style Definitions

Most of the style descriptors we use are self-explanatory. Data is primarily sourced from either Worldscope or Thomson IBES. However, there are some style descriptors that warrant further explanation:

n S&P Global BMI Growth-Value Score — this is a score between 0 (pure value) and 1 (pure growth) is assigned to each stock and is calculated by S&P. For more information on the methodology that is employed to calculate the score, please see refer to S&P on methodology.

n Earnings Stability — calculated as the R-squared from a regression of five annual EPS values (three historical and two forecast) against a time trend. (For more details, see our report Searching for Alpha: Focus on Earnings Stability, Citi Research, February 2003.

n Price Momentum — for each respective daily time period, price momentum is calculated as the t-statistic of the slope coefficient, derived from a regression of the log of daily prices (in local currency) on a time trend.

n Earnings, Sales, Cash Revisions — this is the time-weighted average of analyst upgrades less downgrades scaled by the total number of estimates for fiscal years 1 and 2.

n Earnings Certainty — measured as the inverse of the coefficient of variation in the next 12-month EPS estimates (stocks are excluded if there are less than five estimates or EPS is less than one cent). This is the same as the inverse of our EPS dispersion measure and more detail can be found in Searching for Alpha: Sell When Analysts Disagree, Citi Research, December 2003.

n Earnings Quality — calculated as the year-on-year change in net operating assets or the difference between accounting and cash earnings (accruals). For more details, see our reports. Searching for Alpha - Accruals Volatility – A New Approach to Quality Investing, Searching for Alpha: Quantifying Earnings Quality, Citi Research, October 2004.

n Balance Sheet Quality — this is the level of net operating assets. NOA incorporates all the changes in NOA and can be used as proxy for cumulated accruals. Since accumulation of earnings without cash flow is unsustainable in the long term, having high net operating assets is a sign of balance sheet bloat. For more details, Searching for Alpha: Quantifying Earnings Quality, European Quantitative Strategy, October 2004.

Previously Published Research

Figure 163. Figure 163. Research Reports (since 2009)

02-Jul-13

Factor

Searching For Alpha: Digging For Dividends: QUARI - QUality with A Reliable Income

24-Jun-13

Misc

Global Theme Machine: An Objective Way of Identifying Attractive Investment Themes

25-Mar-13

Factor

Searching for Alpha: Purifying Analyst Recommendations: Removing Beta to get to the Alpha

06-Nov-12

Factor

Searching for Alpha: Tangible Benefits of Intangibles: Brand, Respect & Intellectual Capital

09-Mar-12

Smart Beta

Low-Risk Portfolio Strategies: Sharpe Ratio Maximisation and Multi-Asset Applications

28-Feb-12

Rotation

Macro Risk and Style Rotation: A Guide Rather than a Prescription

14-Sep-11

Factor

Searching for Alpha: Accruals Volatility - A New Approach to Quality Investing

24-Aug-11

Allocation

Industry Alpha Insights: Four Approaches to Tactical Industry Selection

17-Mar-11

Misc

Industry Alpha Insights: Quantifying Industry-Specific Fundamentals

18-Nov-10

Smart Beta

Low-Risk Equity Portfolios: More than just Minimum Variance

15-Nov-10

Allocation

Under the Microscope: Measuring Systemic Risk - The Absorption Ratio

14-Jun-10

Factor

Under the Microscope: Optionality in Valuation

31-Mar-10

Event

Searching for Alpha: Earnings Surprise: Still Profiting from Surprises

29-Jan-10

Factor

Momentum in Japan: Looking at Price, Trading Values and Earnings

15-Oct-09

Rotation

Searching for Alpha: Style Rotation: Optimising Style Rotation Strategies

Source: Citi Research

Citi Quant Research Team

Figure 164. Figure 164. Citi Quantitative Research Teams (For Informational Purposes Only)

If you are visually impaired and would like to speak to a Citi representative regarding the details of the graphics in this document, please call USA 1-888-500-5008 (TTY: 711), from outside the

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Appendix A-1

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For important disclosures (including copies of historical disclosures) regarding the companies that are the subject of this Citi Research product ("the Product"), please contact Citi Research, 388 Greenwich Street, 6th Floor, New York, NY, 10013, Attention: Legal/Compliance [E6WYB6412478]. In addition, the same important disclosures, with the exception of the Valuation and Risk assessments and historical disclosures, are contained on the Firm's disclosure website at https://www.citivelocity.com/cvr/eppublic/citi_research_disclosures. Valuation and Risk assessments can be found in the text of the most recent research note/report regarding the subject company. Pursuant to the Market Abuse Regulation a history of all Citi Research recommendations published during the preceding 12-month period can be accessed via Citi Velocity (https://www.citivelocity.com/cv2) or your standard distribution portal. Historical disclosures (for up to the past three years)

will be provided upon request. Citi Research Equity Ratings Distribution

Data current as of 01 Jul 2026 Citi Research Global Fundamental Coverage (Neutral=Hold) % of companies in each rating category that are investment banking clients Citi Research Quantitative World Radar Screen Model Coverage % of companies in each rating category that are investment banking clients Citi Research Quantitative Latam Radar Screen Model Coverage % of companies in each rating category that are investment banking clients Citi Research Quantitative Asia Radar Screen Model Coverage % of companies in each rating category that are investment banking clients Citi Research Quantitative Australia Radar Screen Model Coverage

12 Month Rating

Catalyst Watch

Buy

Hold

Sell

Buy

Hold

Sell

61%

32%

36%

48%

16%

38%

42%

27%

42%

37%

36%

30%

40%

30%

24%

16%

23%

57%

21%

39%

40%

43%

20%

60%

20%

26%

10%

41%

59%

% of companies in each rating category that are investment banking clients Guide to Citi Research Fundamental Research Investment Ratings:

35%

Citi Research stock recommendations include an investment rating and an optional risk rating to highlight high risk stocks. Risk rating takes into account both price volatility and fundamental criteria. Stocks will either have no risk rating or a High risk

rating assigned.

Investment Ratings: Citi Research investment ratings are Buy, Neutral and Sell. Our ratings are a function of analyst expectations of expected total return ("ETR") and risk. ETR is the sum of the forecast price appreciation (or depreciation) plus the dividend yield for a stock within the next 12 months. The target price is based on a 12 month time horizon. The Investment rating definitions are: Buy (1) ETR of 15% or more or 25% or more for High risk stocks; and Sell (3) for negative ETR. Any covered stock not assigned a Buy or a Sell is a Neutral (2). For stocks rated Neutral (2), if an analyst believes that there are insufficient valuation drivers and/or investment catalysts to derive a positive or negative investment view, they may elect with the approval of Citi Research management not to assign a target price and, thus, not derive an ETR. Citi Research may suspend its rating and target price and assign “Rating Suspended” status for regulatory and/or internal policy reasons. Citi Research may also suspend its rating and target price and assign "Under Review" status for other exceptional circumstances (e.g. lack of information critical to the analyst's thesis, trading suspension) affecting the company and/or trading in the company's securities. In both such situations, the rating and target price will show as “—“ and “-“ respectively in the rating history price chart. Prior to 11 April 2022 Citi Research assigned “Under Review” status to both situations and prior to 11 Nov 2020 only in exceptional circumstances. As soon as practically possible, the analyst will publish a note re-establishing a rating and investment thesis. Investment ratings are determined by the ranges described above at the time of initiation of coverage, a change in investment and/or risk rating, or a change in target price (subject to limited management discretion). At times, the expected total returns may fall outside of these ranges because of market price movements and/or other short-term volatility or trading patterns. Such interim deviations will be permitted but will become subject to review by Research Management. Your decision to buy or sell a security should be based upon your personal investment objectives and should be made only

after evaluating the stock's expected performance and risk. Catalyst Watch/Short Term Views (“STV”) Ratings Disclosure:

Catalyst Watch and STV Upside/Downside calls: Citi Research may also include a Catalyst Watch or STV Upside or Downside call to indicate the analyst expects the share price to rise (fall) in absolute terms over a specified period of 30 or 90 days in reaction to one or more specific near-term catalysts or events impacting the company or the market. A Catalyst Watch will be published when Analyst confidence is high that an impact to share price will occur; it will be a STV when confidence level is moderate. A Catalyst Watch or STV Upside/Downside call will automatically expire at the end of the specified 30/90 day period. The Catalyst Watch will also be automatically removed if share price performance (calculated at market close) exceeds 15% against the direction of the call (unless over-ridden by the analyst). The analyst may also remove a Catalyst Watch or STV call prior to the end of the specified period in a published research note. A Catalyst Watch/STV Upside or Downside call may be different from and does not affect a stock’s fundamental equity rating, which reflects a longer-term total absolute return expectation. For purposes of FINRA ratings-distribution-disclosure rules, a Catalyst Watch/STV Upside call corresponds to a buy recommendation and a Catalyst Watch/STV Downside call corresponds to a sell recommendation. Any stock not assigned to a Catalyst Watch Upside, Catalyst Watch Downside, STV Upside, or STV Downside call is considered Catalyst Watch/STV No View. For purposes of FINRA ratings distribution-disclosure rules, we correspond Catalyst Watch/STV No View to Hold in our ratings distribution table for our Catalyst Watch/STV Upside/Downside rating system. However, we reiterate that we do not consider No View to be a recommendation. For all Catalyst Watch/STV Upside/Downside calls, risk exists that the

catalyst(s) and associated share-price movement will not materialize as expected. Privately held companies:

The legal entities through which Citi conducts its business do not permit investment research recommendations, ratings, target prices, or estimates on any privately held companies (“private companies”) referenced herein. Citi Research does not utilize a rating system for private companies. Any information on private companies in this report is based on generally available information and, although obtained from sources believed by Citi to be reliable, its accuracy and completeness are not guaranteed. Private companies are generally not subject to reporting obligations, which may result in less publicly available information. The information contained in this report may differ from information a private company may subsequently make public or otherwise share with investors. Continuation of publishing private company reports or private company sections

within reports is at Citi's discretion, in accordance with internal policies. Guide to Citi Research Quantitative Research Investment Ratings:

Citi Research Quantitative Research World Radar Screen recommendations are based on a globally consistent framework to measure relative value and momentum for a large number of stocks across global developed and emerging markets. Relative value and momentum rankings are equally weighted to produce a global attractiveness score for each stock. The scores are then ranked and put into deciles. A stock with a decile rating of 1 denotes an attractiveness score in the top 10% of the universe (most attractive). A stock with a decile rating of 10 denotes an attractiveness score in the bottom 10% of the universe (least

attractive).

Citi Research Asia Quantitative Radar Screen model recommendations are based on a regionally consistent framework to measure relative value and momentum for a large number of stocks across regional developed and emerging markets. Relative value and momentum rankings are equally weighted to produce a global attractiveness score for each stock. The scores are then ranked and put into quintiles. A stock with a quintile rating of 1 denotes an attractiveness score in the top 20% of the universe (most attractive). A stock with a quintile rating of 5 denotes an attractiveness score in the bottom 20% of the universe

(least attractive).

Citi Research Quantitative Research Latam Radar Screen recommendations are based on a globally consistent framework to measure relative value and momentum for a large number of stocks across markets in Latin America. Relative value and momentum rankings are equally weighted to produce a global attractiveness score for each stock. The scores are then ranked and put into quintiles. A stock with a quintile rating of 1 denotes an attractiveness score in the top 20% of the universe (most attractive). A stock with a quintile rating of 5 denotes an attractiveness score in the bottom 20% of the universe (least

attractive).

Citi Research Australia Quantitative Radar Screen model recommendations are based on a robust framework to measure relative value and momentum for a large number of stocks across the Australian market. Stocks with a ranking of 1 denotes a stock that is above average in terms of both value and momentum relative to the stocks in the Australian market. A ranking of 10 denotes a stock that is below average in terms of both value and momentum relative to the stocks in the Australian market. For purposes of FINRA ratings-distribution-disclosure rules, a Citi Research Quantitative World Radar Screen recommendation of (1), (2) or (3) most closely corresponds to a buy recommendation; a recommendation from this product group of (4), (5), (6) or (7) most closely corresponds to a hold recommendation; and a recommendation of (8), (9) or (10) most closely corresponds to a sell recommendation. An (NR) recommendation indicates that the stock is no longer in the screen. For purposes of FINRA ratings-distribution-disclosure rules, a Citi Research Latam Radar Screen recommendation of (1) most closely corresponds to a buy recommendation; a recommendation of (2),(3) or (4) most closely corresponds to a hold recommendation; and a recommendation of (5) most closely corresponds to a sell recommendation. An (NR) recommendation

indicates that the stock is no longer in the screen.

For purposes of FINRA ratings distribution disclosure rules, a Citi Research Asia Quantitative Radar Screen recommendation of (1) most closely corresponds to a buy recommendation; a Citi Research Asia Quantitative Radar Screen recommendation of (2), (3), (4) most closely corresponds to a hold recommendation; and a recommendation of (5) most closely corresponds to a sell recommendation. An (NR) recommendation indicates that the stock is no longer in the screen. For purposes of FINRA ratings-distribution-disclosure rules, a Citi Research Quantitative Research Australia Radar Screen recommendation of "attractive" (1) most closely corresponds to a buy recommendation. All other stocks in the sector are considered to be "unattractive" (10) which most closely corresponds to a sell recommendation. An (NR)/(0) recommendation

indicates that the stock is no longer in the screen.

Recommendations are based on the relative attractiveness of a stock, thus can not be directly equated to buy, hold and sell categories. Accordingly, your decision to buy or sell a security should be based on your personal investment objectives and only

after evaluating the stock's expected relative performance. RESEARCH ANALYST AFFILIATIONS / NON-US RESEARCH ANALYST DISCLOSURES

The legal entities employing the authors of this report are listed below (and their regulators are listed further herein). Non-US research analysts who have prepared this report (i.e., all research analysts listed below other than those identified as employed by Citigroup Global Markets Inc.) are not registered/qualified as research analysts with FINRA. Such research analysts may not be associated persons of the member organization (but are employed by an affiliate of the member organization) and therefore may not be subject to the FINRA Rule 2241 restrictions on communications with a subject company, public appearances and

trading securities held by a research analyst account. Citigroup Global Markets Inc. Citigroup Global Markets India Private Limited Citigroup Global Markets Limited OTHER DISCLOSURES

Richard W Schlatter; Anju Bhandari Kuldeep M Tilara Yue Hin Pong; David T Chew

Any price(s) of instruments mentioned in recommendations are as of the prior day’s market close on the primary market for the

instrument, unless otherwise stated.

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The proportion of all Citi Research research recommendations that were the equivalent to "Buy","Hold","Sell" at the end of each quarter over the prior 12 months (with the % of these that had received investment firm services from Citi in the prior 12 months shown in brackets) is as follows; Q2 2026 Buy 33%(63%), Hold 44% (51%), Sell 23% (49%), RV 0.5% (87%): Q1 2026 Buy 33% (63%), Hold 44% (52%), Sell 23% (46%), RV 0.5% (89%); Q4 2025 Buy 33% (63%), Hold 44% (50%), Sell 23% (46%), RV 0.4% (91%); Q3 2025 Buy 33%(61%), Hold 44% (52%), Sell 23% (50%), RV 0.4% (80%). For the purposes of disclosing recommendations other than for equity (whose definitions can be found in the corresponding disclosure sections), "Buy" means a positive directional trade idea; "Sell" means a negative directional trade idea; and "Relative Value" means any trade idea which does not have a clear direction to the investment strategy. 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