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Extending ESG Frontiers: A Top-Down Framework for Integrating Sustainability into Derivatives Markets

Sun, 18 May 2025 11:16:07 +0700

ESG Considerations in Derivatives Markets

Researched and compiled by: Pham Duc Khiem, CFA® Institute Member & Dang Tran, FVMA®

Issued by: Finstock, Inc.

Environmental, Social, and Governance (ESG) investing has become ubiquitous in stocks and bonds, but its application to derivatives – especially commodity futures – is novel. Janardanan, Qiao, and Rouwenhorst (2024) explicitly highlight that “like stocks and bonds, derivatives investments have a measurable ESG impact” . They propose a top-down ESG framework for futures: rather than scoring individual firms, one scores the underlying activities/geographies of commodities and currencies. For commodities, this means assigning each contract an E (environmental) score based on its carbon footprint, an S (social) score based on the development level of its producing countries, and a G (governance) score based on corruption indices. Concretely, they measure E as kilograms of CO₂-equivalent per dollar of investment in that commodity, S as a production-weighted Human Development Index (HDI), and G as a production-weighted Corruption Perceptions Index (CPI) . For example, one portfolio selects the 50% of contracts with lowest E and finds it reduces emissions by ~81%: the benchmark’s 2.7 kg CO₂/$ falls to just 0.5 kg . A combined ESG-aware portfolio ranks contracts by the sum 2E+S+G (to weight environment more) and equally invests in the top half. This composite strategy lowers emissions by ~44% while modestly improving S and G (e.g. raising the HDI score by ~4% and the CPI by ~7% relative to the benchmark) . In each case, portfolios are fully collateralized and rebalanced annually.

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E, S, G Metrics: In Janardanan et al.’s scheme, E reflects carbon intensity (kg CO₂ per $ invested). For example, Figure 3’s note shows E as kg CO₂_eq per dollar, S as production‐weighted HDI, and G as production-weighted CPI . Intuitively, low‐E futures are those tied to less-emissions-intensive commodities (e.g. gold or soybean vs. crude oil or natural gas). The S score promotes commodities from more developed regions, and G favors those from less-corrupt jurisdictions. By construction, the authors effectively treat futures as proxies for economic activity by region: e.g. natural gas futures get a high E score because natural gas production is carbon-intensive, while futures on metals produced in Scandinavia may earn higher S and G scores. This top-down approach contrasts with the usual firm-level ESG ratings used for equities, and it is designed to respect the role of futures as risk factors rather than direct ownership stakes.
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Portfolio Construction: Using these scores, Janardanan et al. test two hypothetical screens. The “Eaware” portfolio simply selects the 50% of commodity futures with the lowest E ranks each year, equally weighted. The “ESG-aware” portfolio sorts by the composite ESG rank (2·E + S + G) and invests equally in the top half . The authors compare each to a standard equally-weighted (EW) commodity index. They find that the E-aware portfolio closely tracks the benchmark (monthly return correlation ≈0.88) but has a slightly lower 10-year return (about –1.3% per annum relative) . Most strikingly, its carbon footprint plummets: emissions per invested dollar drop from 2.7 kg to 0.5 kg (−81%) , achieved by underweighting high-emission commodities (e.g. natural gas, palm oil). However, focusing only on E has side effects: the S (HDI) and G (CPI) scores decline slightly (by ~2% and 4% respectively) because the screen ignores social/governance factors . By contrast, the ESG-aware portfolio achieves emissions roughly halfway (≈56%) between the EW and the pure-E portfolio, while modestly raising S and G. It retains a very high return correlation to the EW index (≈0.92) . In short, the ESG screens materially change the attributes of the commodity portfolio (much lower carbon intensity, slightly higher average HDI/CPI) but do not dramatically alter its risk/ return profile versus a broad commodity index.

Theoretical Rationale and Economic Channels

The above framework is grounded in how derivatives markets influence real-world outcomes. Unlike corporate bonds or equity – where buying a firm’s securities directly affects its cost of capital – futures represent insurance contracts or risk-sharing tools. Janardanan et al. emphasize that ESG in derivatives operates through the hedging channel: by influencing the cost of hedging for companies engaged in the relevant commodity or currency market . Put differently, an ESG-sensitive futures market could make insurance more expensive for “bad” commodities and cheaper for “good” ones. Because hedging decisions (rollover of futures hedges) occur frequently for producers and consumers, this could cumulatively mimic an ongoing ESG levy or subsidy. In their conclusion, the authors argue that “coordinated ESG policies in derivatives have the potential to make a significant impact on corporate decision-making” . Hedging a price risk is analogous to issuing debt: if the “price of insurance” (futures hedging) rises for carbon-intensive commodities, companies may invest less in that activity.

This logic has precedents. Auspice (Basnicki and Pickering) note that commodity futures do not finance production directly: “commodity futures investments do not require physical extraction… and they do not link to the environmental impact from resource extraction” . Instead, futures share risk. If speculative demand for low-ESG (carbon-heavy) futures shrinks, futures prices might move (e.g. become more backwardated), raising hedging costs for producers. This “implicit tax” on high-emission hedging could induce firms to cut emissions or seek alternatives. More generally, theory suggests that a futures market with an imbalance between longs and shorts (and now ESG preferences) could push prices to reflect ESG concerns. For example, if most consumers are long hedgers (they buy futures to lock in supply price) and fewer speculators are willing to take the short side in high-emission markets, futures prices would rise for those contracts, at least short-term. By contrast, “good” commodities could see lower futures prices and increased consumption. Such shifts parallel a risk-sharing argument: as Van Hemert et al. (2024) suggest, a long-short ESG fund could overweight good commodities (more risk sharing) and ignore bad ones

These channels are not uncontroversial. An AQR analysis warns that simply avoiding “bad” commodities (a no-touch approach) or overweighting “good” ones can distort prices and hedging. Janardanan et al. acknowledge this. As AQR notes, Janardanan’s proposal to go “more long ESG–‘good’ commodities and less long…’bad’ ones” does have side effects : if fewer market participants take long positions in a commodity, its price will tend to fall (and vice versa), altering quantities demanded. Over time, the futures market could even invert the intended ESG signal. In short, ESG integration via futures acts like adding supply/demand pressure to certain goods. Sustainable investors must consider these feedbacks: the price impact of screening might help ESG goals, but it could also simply change risk premia or invite speculative entry. The authors themselves stress that their ESG strategy “comes not without undesirable potential side effects”

Despite the complexity, the crucial theoretical point is that futures offer a lever on hedging costs, which is analogous to a continuous, global ESG subsidy/tax. By contrast with stock investing (which targets a single firm’s funding cost), derivative-based ESG “targets the cost of insurance to corporations engaged in a similar economic activity” . Because hedging is regular and common, its aggregate effect could rival corporate cost-of-capital signals.

Comparison to Other ESG Approaches

The Janardanan et al. framework is top-down and macro-driven, in contrast to the traditional bottom-up firm-based ESG screening used in equities and bonds. In commodity contexts, other approaches include:

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Exclusion or “No-Touch” Screens: An investor might simply avoid futures on certain commodities (e.g. thermal coal, food staples) due to environmental or social concerns . This is essentially treating the futures index like a “negative screen.” The drawback is that outright exclusion can forgo risk premia and may have limited impact on underlying production (the resource still trades in spot markets). AQR notes that blanket exclusion (“no-touch”) can conflict with hedging needs: for many commodities, large producers rely on futures hedges, so forbidding those markets could reduce liquidity and transparency
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Thematic / Tilting Strategies: Instead of outright ban, some investors might overweight “green” or “soft-commodity” exposures (e.g. timber, renewables) and underweight heavy emitters. The Janardanan et al. E-aware/ESG-aware portfolios are examples of tilting. Similarly, one could use futures on “sustainable” commodity indices (e.g. indices that exclude fossil fuels). For example, S&P Dow Jones and MSCI have researched sustainable commodities indices, though these are not yet mainstream.
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Vertical Integration (equity vs commodity): Investors concerned with commodities might invest in producers with strong ESG rather than in commodity futures directly. This shifts the focus to stock/ bond screens in resource companies. The drawback is a tighter correlation to equity markets and potential greenwashing (as Auspice points out). It also may not capture commodity risk exposures.
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ESG-Linked Derivatives: Outside commodities, market-makers have introduced ESG-linked swaps or options, where payouts depend on ESG KPIs (e.g. an “ESG forward” on copper that pays extra if CO₂ in copper mining falls). These are in early stages and mostly OTC. They are more experimental (see Bellanti 2023 on “ESG-Linked Derivatives”【15†】), but offer another angle: the derivative’s payoff or margin adjusts with ESG metrics, directly tying hedging costs to performance.

In summary, the top-down ESG screening for futures is complementary to these approaches. Unlike firmbased screens, it deals with activity-level externalities. It can be combined with, rather than replace, other ESG methods. For example, a fund could use an ESG index futures (described below) as a hedge and still select equities by firm ESG.

Potential Advantages and Limitations

Advantages:

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Preserved Liquidity and Diversification: Commodity futures markets are generally deep and liquid. Janardanan et al. find that even their ESG-screened portfolios closely track broad indices. This suggests investors can pursue ESG goals without sacrificing the liquidity and diversification of futures . Indeed, futures often have lower correlation with equity/bond markets than do commodity stocks , so ESG integration here retains diversification. Notably, CME Group now offers ESG versions of major equity futures (see below) because investors want “similar return profile” with ESG filtering , implying that ESG indices can track benchmarks closely.
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Inflation and Risk Hedging: All portfolios (EW, E-aware, ESG-aware) maintain positive correlation with inflation, meaning they still act as inflation hedges. This is important for institutions using futures to guard against price shocks; ESG screening does not negate that role . In fact, some ESG mandates value the “societal” role of stable commodity markets (Auspice points out that futures provide risk management and liquidity to the economy ).
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Signal to Corporations: By affecting hedging costs, this approach may influence corporate behavior. Analogous to how bond yields change with credit risk, persistent futures flow imbalances could pressure producers to decarbonize. Because hedging occurs frequently, even small premia differences can accumulate. In principle, this is a powerful signaling device: unlike a one-time green bond issue, futures hedging is ongoing.
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Ease of Implementation: The method is simple to apply: one just needs country-level ESG data. Janardanan et al. use publicly available data (UN HDI, Transparency International’s CPI, carbon emissions). Other researchers or practitioners could adopt similar top-down scores for any derivatives whose underlying economics tie to geographies (currencies, stock index regions, etc.). This simplicity makes the approach transparent and replicable.

Limitations and Risks:

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Data and Proxy Issues: The framework relies on coarse proxies. For example, E uses broad national emission averages and production weights; this ignores efficiency differences or corporate mitigation efforts. Similarly, S and G use country averages, which may misstate the actual producers of a commodity (e.g. global oil companies are not evenly spread by country GDP). These proxies may also be stale or lack corporate detail. Thus the ESG “score” attached to a futures contract is approximate, and might sometimes mis-rank contracts.
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Market Impact and Risk Premia: If many investors pursue identical ESG screens, the flows could distort futures pricing. This may either erode expected returns or create volatility. For example, if speculative (non-hedger) demand shifts away from high-emission futures, the price may rise (or the roll yield fall), reducing the reward for whoever does take the short side. In effect, the classic commodity risk premium could be altered. Moreover, an abundance of ESG-driven demand could amplify momentum/technical trading and detach prices from fundamentals.
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Traded Volume and Contract Design: Some futures already have relatively low open interest. If an ESG fund avoids or underweights a thinly traded contract, it could make that market even less liquid, which paradoxically might hurt other hedgers. Conversely, concentrating on a subset (the “top 50%” by ESG) could heighten positions in a few contracts, potentially creating concentration risk.
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Fragmented ESG Goals: Investors have varied ESG objectives. Janardanan et al. show one can reweight E vs S/G in the composite score . But different goals (e.g. climate vs human rights vs corruption) may conflict, and a single ESG portfolio cannot maximize all. The authors note that the ESG-aware fund only achieves about 60–70% of the maximal possible gains in HDI and CPI scores (compared to dedicated S- or G- portfolios) . Thus it is inherently a compromise approach.
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Regulatory and Reporting Challenges: ESG disclosure standards are evolving. Unlike stocks, commodity futures have no standard ESG reporting. Investors must interpret top-down metrics themselves, and regulators do not yet provide guidance (as noted, OECD/UNPRI have nothing for futures ). Without standard metrics, there is a risk of “greenwashing” (claiming ESG alignment without meaningful impact).

Other Derivatives Markets

The top-down scoring logic extends naturally beyond commodities. Foreign Exchange (FX) Futures: Multinational firms use FX futures to hedge currency risk, so one can score each currency by its country ESG profile. For example, currencies can be graded by per-capita CO₂ (environment), national HDI (social), and CPI (governance). Janardanan et al. tabulate ESG scores for major FX futures (Table 3) using UN and Transparency data. As a sample, high-emitting countries like Australia (E≈15, S≈0.946, G≈75) get a high E score (bad), while New Zealand (E≈7, S≈0.939, G≈87) looks better . These scores vary widely: e.g. the Indian Rupee has low HDI (0.644) and high CO₂ (E=1.9) versus Canadian Dollar (S=0.935, E="14.1)." One could then tilt an FX futures portfolio toward “greener” currencies. In fact, asset managers have begun explicitly linking ESG to currencies: J.P. Morgan, for instance, builds ESG frameworks that use environmental factors to capture commodity exposure and social/governance factors to forecast currency value .

Stock Index Futures: In equities, ESG integration is already common at the index level. For example, CME Group now trades an E-mini S&P 500 ESG futures contract. This contract tracks the S&P 500 Scored & Screened index, which filters out companies based on ESG criteria . The futures’ pitch is clear: “align your financial goals with ESG values” while maintaining a “similar return profile to the S&P 500” . In other words, investors can hedge equity exposure via an ESG-labeled index. Similar products exist for Europe (e.g. S&P Europe 350 ESG futures). Extending the commodity approach, one could also make regional stock index futures ESG-aware by weighting each country or sector by its aggregate ESG footprint (analogous to the country-weighted HDI/CPI method).

Commodity Swaps and Options: Beyond futures, OTC derivatives can incorporate ESG triggers. For instance, a commodity swap might have a carbon price adjustment: if a carbon index rises, the swap payoff shifts. Some banks now offer ESG swaps where collateral or payout depends on an issuer’s ESG rating 【15†】. While nascent, these instruments show that derivatives markets are beginning to blend risk management with sustainability goals.

Conclusion

Janardanan, Qiao, and Rouwenhorst (2024) make the case that ESG need not be confined to equities and bonds. Their ESG-scored futures framework shows investors can tilt commodity and FX portfolios toward preferred sustainability profiles with minimal disruption to returns. The empirical results – large reductions in carbon footprint and modest gains in social/governance metrics with little drag on performance – suggest feasibility. Crucially, the economics differ from firm-based ESG: it is about hedging costs and risk sharing, not capital funding.

Nonetheless, ESG derivatives integration has trade-offs. The liquidity and diversification benefits of futures tend to remain intact , but market distortions and data limitations are real concerns. The effectiveness of such strategies in changing real-world emissions is an open question, hinging on investor scale and market dynamics. Still, as regulators and investors push for broader ESG adoption, these derivatives approaches (top-down scoring, ESG-labeled futures) offer a plausible path. They complement existing ESG methods and can be applied to other markets – from FX to equity indices – with the same principle of scoring economic activity.

Table 2. Example Currency ESG Scores (Janardanan et al., 2024). Each FX future is assigned: E = national CO₂ per capita (higher = worse), S = country HDI (higher = better), G = Corruption Perception Index (higher = less corrupt).

Source & Reference

1. Janardanan et al. (2024) “ESG and Derivatives” (Financial Analysts Journal) along with CFA Institute and industry publications . The Janardanan framework is compared with other ESG approaches (Ausrice, AQR ), and additional data (currency ESG, index futures) are drawn from UNDP, Transparency Int’l, and exchange sources. All cited insights are drawn from the references above.

2. ESG and Derivatives | Financial Analysts Journal https://rpc.cfainstitute.org/research/financial-analysts-journal/2024/esg-and-derivatives

3. Commodity Futures and ESG | Portfolio for the Future | CAIA https://caia.org/blog/2022/01/11/commodity-futures-and-esg

4. aqr.com https://www.aqr.com/-/media/AQR/Documents/Insights/White-Papers/AQR-Sustainable-Commodities-Investing.pdf?sc_lang=en

5. Commodity Investing in the Age of ESG and Inflation by Brennan Basnicki, Tim Pickering :: SSRN https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID3961718_code4879775.pdf?abstractid=3947377&mirid=1

6. E-mini S&P 500 ESG Index Overview - CME Group https://www.cmegroup.com/markets/equities/sp/e-mini-sandp-500-esg-index.html

7. Currencies through an ESG lens | J.P. Morgan Asset Management https://am.jpmorgan.com/se/en/asset-management/liq/insights/portfolio-insights/currency/currencies-through-an-esg-lens/

Rethinking Equity Valuation: The Enduring Impact of “Earnings per Share Don’t Count”

Sat, 26 Apr 2025 15:22:13 +0700

Author: Michael Carter, Reseacher - Finstock, Inc.

Introduction

For decades, earnings per share (EPS) has occupied a central role in equity valuation, often viewed by analysts and investors alike as the definitive measure of a company’s profitability and performance. The EPS–P/E multiple framework became the standard valuation tool taught in business schools and practiced across Wall Street. Yet, in 1974, Joel M. Stern’s article “Earnings per Share Don’t Count” shook the foundations of this orthodoxy.

In what is now regarded as a seminal piece in financial thought, Stern argued that EPS was a flawed and often misleading indicator, and proposed free cash flow (FCF) as a superior alternative. His arguments not only challenged existing assumptions but also catalyzed a broader reevaluation of valuation methodologies. This article revisits Stern’s ideas, examines their reception, analyzes the EPS vs. FCF debate through empirical and theoretical lenses, and considers the long-term legacy of his work on modern valuation practices.

Stern’s Radical Proposition: The Flaws of EPS

Stern’s central thesis was that EPS, though intuitive and easy to calculate, often fails to capture the true economic reality of a business. Through logical examples, he illustrated how two companies with identical EPS could have vastly different valuations due to differences in capital requirements. One company may sustain growth internally, while the other must raise external capital—leading to either earnings dilution (if funded by equity) or increased risk (if funded by debt).

Stern’s key contribution was the concept of free cash flow (FCF)—defined as net operating profit after taxes minus the capital expenditures required to maintain or grow the business. By focusing on the actual cash left over for shareholders, FCF addressed what EPS ignored: the opportunity cost of capital, risk, and investment efficiency.

The Six-Variable Model

To formalize his theory, Stern proposed a six-variable model that emphasized FCF drivers:

Net operating profit after taxes (NOPAT)
New capital investment
Expected return on that capital
Duration of the return
Business risk
Tax benefits of debt

This comprehensive model shifted focus from accounting profits to economic value creation. It not only provided a richer basis for valuation but also introduced the idea that valuation is inherently forward-looking and sensitive to both financial strategy and risk perception.

Reception and Immediate Influence

Despite its radical tone, Stern’s article quickly gained attention in both academic and practitioner circles. The Harvard Business School’s student performance lampooned EPS obsession, and authors like Alfred Rappaport cited Stern in challenging EPS as a basis for evaluating acquisitions. The shift Stern proposed fed directly into the development of Value-Based Management (VBM), a philosophy that aligns corporate decision-making with shareholder value.

Stern’s ideas also influenced the creation of new performance measures such as Economic Value Added (EVA), later popularized by the consulting firm he co-founded, Stern Stewart & Co. EVA adjusts accounting earnings to better reflect economic reality, further distancing modern valuation practices from EPS.

Yet, the transition wasn’t immediate. Analysts, asset managers, and the broader investing public retained their reliance on EPS for years, largely due to its familiarity, regulatory backing (via GAAP), and presence in financial media. However, beneath the surface, FCF and value-based models steadily gained ground.

The EPS vs. FCF Debate: Evidence and Interpretation

Empirical Support for FCF

Proponents of FCF often point to its superior predictive power for long-term stock performance. Research by Foerster, Tsagarelis, and Wang (2017) finds that direct cash flow measures outperform traditional accounting metrics in explaining stock returns, especially when adjusted for risk and nonrecurring items. This makes FCF particularly useful in assessing companies with complex capital structures or significant intangible investments.

The direct cash flow method, encouraged by both IFRS and US GAAP, clusters cash flows into operating, investing, and financing activities—improving transparency and isolating true value-creating operations. Firms ranked highest in direct FCF metrics have consistently outperformed those in the lowest decile by significant margins.

Caveats and Limits of FCF

However, FCF is not a panacea. Critics argue that cash flows can be highly volatile and sensitive to timing differences or management discretion. Additionally, not all industries or business models lend themselves easily to FCF analysis—for example, financial institutions or early-stage startups.

Studies by Liu, Nissim, and Thomas (2007) suggest that in certain cases—particularly when focusing on forecasts rather than reported numbers—EPS may correlate more strongly with stock prices than cash flow. Furthermore, EPS has the advantage of reflecting non-cash obligations like pension liabilities or share-based compensation that cash flow might ignore.

Beyond the Numbers: Psychological and Practical Barriers

Another reason EPS retains its dominance is behavioral. Investors and analysts are prone to cognitive shortcuts, and EPS is easily digestible and comparable across companies and time. Corporate managers, aware of this bias, often manage earnings to meet consensus EPS forecasts—a phenomenon that has led to “earnings smoothing” and quarterly guidance strategies.

Stern himself criticized this behavior, arguing that it incentivized short-termism and distorted long-term value creation. The use of FCF, in contrast, is harder to manipulate and encourages a more strategic view of performance.

A Changing Landscape: The Rise of Intangibles

One of the most compelling validations of Stern’s critique comes from the modern corporate landscape. According to Clarivate (2023), intangible assets account for over 80% of S&P 500 market capitalization—up from just 12% four decades ago. Companies now derive value from intellectual property, brand equity, and data—assets poorly captured by GAAP accounting.

These companies often report low or negative EPS due to expensing R&D, despite having strong economic fundamentals. In such cases, traditional P/E multiples become meaningless, necessitating valuation based on sales, EBITDA, or discounted FCF.

Valuation platforms like Credit Suisse HOLT and Valens Research now use adjusted cash flow models or Uniform Adjusted Financial Reporting Standards (UAFRS) to bridge the gap between accounting limitations and market reality.

Educational Influence and Institutional Adoption

The CFA Institute has incorporated FCF analysis into its core curriculum, highlighting its centrality in modern valuation. As Pinto et al. (2024) noted, most analysts use multiple valuation methods, but free cash flow is now in near-universal use, especially for DCF modeling.

This evolution reflects a broader pedagogical shift. Business schools, once reliant on accounting-centric tools, now emphasize economic drivers of value and encourage students to examine capital intensity, reinvestment needs, and risk-adjusted returns.

Conclusion: A Lasting Legacy of Critical Thinking

Joel Stern’s 1974 article did more than critique EPS; it challenged analysts to think more deeply about what creates value. His introduction of FCF paved the way for more dynamic, flexible, and forward-looking valuation practices. While EPS still has a role to play, especially in communication and short-term comparisons, it is no longer the only—or even the best—tool in the analyst’s kit.

In an era defined by digital transformation, intangible assets, and financial complexity, Stern’s message is more relevant than ever: valuation must reflect reality, not just accounting.

By daring to question orthodoxy, Stern empowered a generation of analysts to seek out better answers—and 50 years later, the profession continues to benefit from that courage.

References

Bhandari, Shyam B., and Mollie T. Adams. 2017.
Domash, Harry. 2019.
Foerster, Stephen R., John Tsagarelis, and Grant Wang. 2017.
Fridson, Martin. 2023.
Graham, Benjamin. 1973.
Khaksari, Esmaeil. 2023.
Lev, Baruch, and Feng Gu. 2016.
Liu, Jing, Doron Nissim, and Jacob Thomas. 2007.
Maditinos, DimitriosI, Pedro Maranha, and AzucenaPerez Alonso. 2007.
Mind Tools Content Team. 2024.
Pinto, Jerold E., Elaine Henry, Thomas R. Robinson, and John D. Stowe. 2024.
Rappaport, Alfred. 1979.
Stern, Joel M. 1974.
UAFRS.org. 2023.

Global Private Equity Performance: A Comprehensive Analysis across Geographies and Strategies

Sun, 20 Apr 2025 00:12:29 +0700

Author: David L Davis

Researcher, Finstock, Inc

Issued by: Finstock, Inc

Date: April 2025

Over the last decade, private equity (PE) has become the largest segment in private markets, with assets under management reaching USD 7.6 trillion globally as of mid-2022. Half of this capital is now allocated outside of North America, marking a significant shift in the industry’s geographic footprint. Yet, academic research has disproportionately focused on North American markets due to limited international data availability.

In this study, I leverage a novel and comprehensive dataset combining fund-level data from Preqin and Eurekahedge to analyze the performance and persistence of private equity funds across international markets, including Europe, Asia-Pacific (APAC), and the Rest of the World (ROW), along with a set of globally diversified funds typically sponsored by North American firms. This research evaluates over 3,100 funds launched between 1980 and 2018, encompassing $4.2 trillion in capital commitments.

Executive Summary

Private equity (PE) has become a cornerstone of institutional portfolios, with global assets under management (AUM) reaching USD 7.6 trillion as of H1 2022, growing at a 14% CAGR since 2012. Despite the industry's growing global footprint, most empirical studies to date focus on North America. This research aims to address this imbalance by examining the performance and persistence of private equity funds across international markets—namely Europe, Asia-Pacific (APAC), Rest of the World (ROW), and globally diversified strategies.

Using a unique dataset merging Preqin and Eurekahedge fund-level data, this study evaluates 3,184 funds with USD 4.2 trillion in commitments raised between 1980 and 2018. We assess performance using internal rate of return (IRR), multiple on invested capital (MOIC), and public market equivalent (PME), benchmarked against regional and U.S. public indices.

1. Introduction

The globalization of private equity has reshaped capital allocation, with 50% of new investments in 2022 occurring outside North America. However, the empirical literature on international private equity has lagged, constrained by data limitations and inconsistent fund-level reporting. This study leverages newly harmonized fund-level cash flow data, sourced through FOIA and direct GP submissions, to deliver the most geographically comprehensive analysis to date.

Key research questions:

How does private equity performance vary across geographies and strategies?
Is there return persistence across global funds comparable to the U.S. market?
Do capital inflows influence subsequent returns, as theorized in the "money chasing deals" literature?

2. Data and Methodology

2.1 Data Construction

The dataset combines:

Preqin: 4,026 funds, focusing on large institutional investors and direct GP submissions.
Eurekahedge: 2,532 funds, including hedge/private equity hybrids and investor-initiated coverage.

After rigorous filtering (removing non-PE strategies, missing cash flows, and post-2018 vintages), we form a final sample of 3,184 unique funds and 1,305 distinct PE firms, totaling USD 4.26 trillion in committed capital.

2.2 Geographic Classification

Using MSCI’s regional taxonomy, funds are grouped into:

Europe (e.g., UK, Germany, France): 476 funds, USD 784B
Asia-Pacific (e.g., China, Japan, India): 256 funds, USD 310B
ROW (Africa, Latin America, Middle East): 94 funds, USD 46B
Global (non-specific geography, usually U.S.-sponsored): 191 funds, USD 561B
North America (U.S. & Canada): 2,167 funds, USD 2.56T (for benchmarking)

3. Performance Metrics

Three standardized performance metrics are used:

IRR: Annualized investor return net of management fees and carry.
MOIC: Total distributions + NAV divided by total contributions.
PME: Comparison to public equity benchmarks (e.g., S&P 500, MSCI Europe). A PME > 1 indicates outperformance.

NAVs are converted to USD, inflation-adjusted, and standardized using spot FX rates and CPI data from FRED and BLS.

4. Results: Fund Performance by Geography and Strategy

4.1 Buyout Funds

Buyouts dominate in both volume and capital.

Europe

IRR: 13.88%, MOIC: 1.39
Outperformed both MSCI Europe and S&P 500 (PME: 1.89 in 1990s)
Realization rate: 38%, suggesting maturing vintages and reliable NAVs

Asia-Pacific

IRR: 9.54%, MOIC: 1.32, PME: declining across vintages
Strongest vintages: 1990s (PME > 1.3), recent vintages underperform

ROW

IRR: 6.54%, MOIC: 1.25, PME: < 0.5 across benchmarks
Highest underperformance observed, both absolute and relative

Global Funds (U.S.-sponsored)

IRR: 15.69%, MOIC: 1.70
PME: consistently >1.8 (pre-2010), highest persistence in dataset
High geographical diversification mitigates inflow effects

North America (Benchmark)

IRR: 13.05%, MOIC: 1.50
PME: strong pre-2000s, declining in 2010s (PME: 0.59)

4.2 Growth Equity Funds

This strategy bridges VC and buyout, targeting late-stage private firms.

Asia-Pacific leads with IRRs of 12.4% and MOICs of 1.47
PME < 1, suggesting outperformance over dollar but not public benchmarks
Europe and ROW: lower IRRs (~9–10%) and PMEs (~0.6)
Global funds (largely U.S. based): IRRs ~8.3%, PME: ~0.7

4.3 Venture Capital Funds

Returns heavily concentrated in North American and global funds.

Global VCs: IRR 14.2%, PME > 1.1, strongest VC performance
Europe/APAC/ROW: IRRs between 7–10%, PMEs < 0.8
Reflects innovation ecosystem concentration in U.S. and fund maturity in non-U.S. VC markets

5. Return Persistence

Persistence is critical for fund selection and long-term asset allocation.

Findings

Europe and Global Buyouts: Strong persistence across vintages and quartiles
Growth Funds in Europe: Moderate persistence observed
Asia-Pacific and ROW: No meaningful persistence in any strategy
High persistence correlates with low manager turnover and market segmentation

6. The Impact of Capital Inflows

We test the “money chasing deals” hypothesis (Gompers & Lerner, 2000):

Europe: High inflows lead to return compression, mirroring North American dynamics
APAC and ROW: No measurable relationship between inflows and returns
Global Funds: Benefit from diversification, inflows do not depress performance

7. Discussion and Implications

For Investors

Geographic Differentiation is Critical: Europe and global buyouts offer stable alpha; growth equity in APAC offers selective opportunities.
Persistence Can Guide GP Selection: Prior top-quartile GPs in Europe and global strategies remain reliable.
Inflows Need Monitoring: In concentrated regions like Europe, vintage diversification is advised.
VC Remains U.S.-centric: Global and U.S.-sponsored VC funds outperform international peers.

For Fund Managers

Fund Strategy and Geography Shape Outcomes: Local GPs in APAC and Europe should tailor investment theses to regional dynamics.
Access Barriers Favor Incumbents: Top-tier GPs retain advantage, especially in segmented European markets.

8. Conclusion

Private equity is no longer a North American phenomenon. This study demonstrates that investment geography and strategy materially shape fund performance, persistence, and relative attractiveness. Europe and globally diversified buyouts are among the strongest performers globally, while APAC holds promise in growth equity. In contrast, venture capital success remains largely confined to U.S.-sponsored platforms.

Performance persistence is strongest where markets are segmented and GP access is limited, such as Europe and global strategies. These findings underscore the need for data-driven allocation, GP selection rigor, and regional specialization in building a successful international private equity portfolio.

Issued by: Finstock, Inc.

Unseen Shifts: The Hidden Impact of Taxes on Asset Allocation in Retirement Portfolios

Sat, 19 Apr 2025 01:56:28 +0700

Author: Evan Michale Brown

Abstract

Portfolio asset allocation is the cornerstone of long-term investment success. However, many investors and wealth managers rely on pretax account values when determining allocations, ignoring a critical element: taxes. Tax-deferred accounts like traditional IRAs carry latent tax liabilities, and this oversight causes asset allocation to drift—potentially significantly—from intended targets. This paper explores the mechanics of tax-induced allocation drift, demonstrates how it arises, quantifies its effects under various scenarios, and argues for the inclusion of after-tax valuation in portfolio management practice.

Introduction: Pretax Statements Lie

In financial planning, a dollar is treated equally across all accounts—Roth, traditional IRA, or taxable brokerage. But in practice, these dollars have different real-world values after taxes. A Roth IRA dollar is fully spendable, while a traditional IRA dollar might only be worth $0.70–$0.60 depending on one’s tax bracket. Ignoring this discrepancy creates what I refer to as tax-induced asset allocation drift.

Consider this: two accounts each report $1 million in value. One is a taxable account. The other is a traditional IRA subject to a 30% marginal tax rate in retirement. On paper, both show the same value. But in practice, only $1 million from the taxable account can be spent; the IRA only affords $700,000 after tax.

Yet most portfolio allocation software and advisor practices rebalance using pretax numbers. This leads to a fundamental misjudgment of risk, especially when certain asset classes—like bonds—are preferentially housed in IRAs for tax efficiency.

The Problem: How Taxes Distort Allocations

Asset allocation targets (e.g., 60% equities / 40% bonds) are designed around an investor’s risk tolerance and return expectations. These are risk-calibrated targets.

However, when portfolios are split between taxable and tax-deferred accounts, and only pretax balances are used, the effective exposure to risk assets may deviate significantly due to unequal tax treatment.

A Simple Example

Portfolio value: $2,000,000
50% in traditional IRA ($1,000,000), 50% in taxable
60% stocks, 40% bonds allocation
Tax rate on IRA withdrawals: 30%

Following conventional wisdom, the investor houses all bonds in the IRA to minimize tax drag. But the after-tax value of the IRA is only $700,000. When this is factored in, the portfolio shrinks to $1.7 million in effective spendable value, and the asset allocation shifts from 60/40 to 67% stocks / 33% bonds.

Result: A 7% drift—exceeding the conventional 5% rebalancing threshold.

Why This Problem Is Systemic

Most financial planning tools and custodial platforms do not offer tax-adjusted portfolio views. Wealth managers, despite optimizing for asset location, fail to correct for this invisible drift. While tools exist for tax-loss harvesting and capital gains budgeting, few consider forward-looking after-tax value.

This is particularly troubling because wealth managers are trained to obsess over small basis-point improvements. Yet a 5–10% drift in asset mix is routinely ignored.

Mechanics of Tax-Induced Drift

Taxes cause asset classes to lose value at different rates. Traditional IRAs are subject to ordinary income tax upon withdrawal. Taxable accounts often benefit from capital gains treatment or step-up basis.

Let:

i = proportion of the portfolio in IRAs
b = target bond allocation
sr = marginal tax rate at retirement

The tax-induced bond drift is defined as:

Sensitivity Analysis: When and Where Drift Matters Most

We can break down portfolio drift by varying three key parameters:

Tax rate (12%, 24%, 35%, 43.4%)
Asset allocation (30/70, 50/50, 70/30 stock/bond)
IRA size as % of total portfolio (0% to 100%)

Findings:

Drift is highest when the IRA size matches the bond allocation.
Drift is zero when portfolio is all-taxable or all-deferred.
Drift grows non-linearly with higher tax rates.

At a 35% tax rate, a 50/50 stock/bond portfolio with 50% IRA exposure becomes 64/36 after-tax. That's nearly a 14% swing in effective asset exposure.

Risk-Reward Trade-Off Shift

Ignoring drift doesn't just affect exposure—it worsens the return profile. For example:

Pretax Portfolio:

Expected Return: 6.50%
Volatility: 11.49%
Sharpe Ratio: 0.57

After-Tax Portfolio:

Expected Return: 6.07%
Volatility: 11.87%
Sharpe Ratio: 0.51

The portfolio becomes riskier with lower efficiency.

Implications for Practice

Private Wealth Advisors should:

Use after-tax adjusted values in reporting and allocation models.
Monitor drift using a 5% threshold, just as with standard rebalancing.
Educate clients about the real value of retirement assets.

Software Developers should:

Build after-tax tracking tools
Enable visualization of spendable wealth by account
Add tax-aware rebalancing models

Regulators and Certifying Bodies (e.g., CFP Board, CFA Institute) should:

Encourage industry standards for after-tax asset reporting
Promote research on after-tax optimization

International Relevance

The challenge is global. IRAs in the U.S. have international analogs:

Canada: RRSP
UK: SIPP
France: PER
Japan: iDeCo
Poland: IKZE

Wherever retirement accounts defer taxes, this problem arises. Asset allocation drift due to taxes is a universal retirement planning hazard.

Limitations and Future Work

This analysis assumes:

Two-asset portfolios (equities and bonds)
Static tax rates
No RMDs, Roth conversions, or future tax law changes

Future research could explore:

Multi-asset class impacts
Incorporation of human capital
Dynamic withdrawal and spending strategies
Tax-aware liability-driven investing (LDI)

Conclusion: Pretax Precision, Post-Tax Deception

Taxes silently distort the risk profile of portfolios by changing the relative weight of each asset class. By ignoring the latent tax burden of IRAs and other deferred accounts, investors may find themselves misaligned with their true risk preferences.

The magnitude of this drift often exceeds common rebalancing triggers and materially alters both risk and return expectations.

For those managing retirement wealth, failing to account for tax-induced asset allocation drift is not just a missed opportunity—it is a misstep that can jeopardize long-term financial security.

The fix is simple: measure what matters. After-tax value matters.

Beyond Backtests: Real-World Lessons in Rebalancing Factor Strategies

Fri, 18 Apr 2025 01:00:30 +0700

Authors: Vincent Yip and Evan Michale Brown

Published by: Finstock.co, 2025

Executive Summary

Factor-based investing strategies like value, momentum, quality, and investment have long been backed by academic research for their ability to outperform traditional market-cap-weighted indices. However, in practice, implementation challenges cause a large gap between paper performance and live results. The most critical friction lies in portfolio rebalancing, particularly in the frequency, cost, and effectiveness of trade execution.

In this in-depth study, Vincent Yip and Evan Michale Brown introduce a refined and highly practical solution—smart rebalancing. The core philosophy is simple but powerful: trade less, but trade better. Rather than chasing every marginal signal, prioritize trades that offer the most compelling expected return per unit of cost. This approach, known as Priority-Best Rebalancing, helps investors preserve more alpha while substantially reducing trading frictions.

Through robust empirical analysis covering over five decades of market data, the authors show that smart rebalancing outperforms traditional rebalancing techniques, both gross and net of trading costs.

The Gap Between Theory and Practice

Most academic factor research assumes:

Frictionless markets.
Costless trading.
Perfect liquidity.
Full rebalancing at every rebalance interval.

In contrast, real-world investment managers face:

Bid-ask spreads and price impact.
Delays and slippage.
Market illiquidity, particularly in small- and micro-cap stocks.
Capacity constraints.

The consequence is a large implementation shortfall—a persistent gap between simulated and actual returns, especially in strategies with high turnover or limited liquidity. This shortfall not only reduces alpha but can also reverse a strategy’s entire value proposition.

Rebalancing as a Performance Lever

Rebalancing is often seen as a mechanical function, but it has strategic importance. Ineffective rebalancing can:

Trigger unnecessary trading costs.
Diminish alpha from high-conviction ideas.
Increase exposure to noisy or reversed signals.

Yip and Brown argue that rebalancing should be treated as an optimization problem, balancing expected return, cost, and signal decay. Smart rebalancing transforms rebalancing from a routine process into a return-enhancing alpha lever.

Priority-Best Rebalancing: The Smart Method

The central innovation is Priority-Best Rebalancing:

Rank trades based on signal strength.
Execute only the most impactful buy and sell trades first.
Respect a pre-defined turnover budget to limit costs.

By doing so, the strategy targets “signal-concentrated alpha” while ignoring low-priority trades that contribute little to performance but add significant cost.

This method is particularly useful in high-turnover strategies, such as momentum, where the frequent signal changes can lead to excessive trading costs. It also helps stabilize long-only implementations, which are more sensitive to transaction drag compared to long-short portfolios.

Other Rebalancing Methods (for Comparison)

Proportional Rebalancing
- Scale every trade down uniformly.
- Simpler to implement but doesn’t differentiate between strong and weak signals.
- May over-allocate capital to low-impact trades while underweighting high-conviction ideas.
Priority-Worst Rebalancing
- Execute the weakest signals first as a control test.
- Used to highlight the critical importance of smart trade ranking.
- Serves as a reminder that poor execution can erode even the best signals.

Data, Factors, and Portfolio Construction

The study uses a long historical dataset:

U.S. equities from 1964–2020.
Sources: CRSP for pricing data, Compustat for fundamentals.
Filters: NYSE, AMEX, and NASDAQ common stocks (share codes 10 and 11).
Accounting data lagged 6 months per Fama-French standards.

Long-only factor portfolios include:

Value: High book-to-price (B/P).
Profitability: High operating profitability.
Investment: Firms with low asset growth.
Momentum: Stocks with strong recent returns (t-12 to t-2).
Composite: Combination of the above using robust z-scores.

Portfolios are constructed using NYSE breakpoints and rebalanced on either monthly or annual cycles. Turnover, CAPM alpha, and Sharpe ratios are computed. The authors also evaluate performance using rolling windows to ensure that findings are robust across market conditions.

Findings and Performance Metrics

Smart rebalancing reduces trading costs by 30–70% compared to full rebalancing.
Net alphas are frequently higher than unconstrained, full-turnover strategies.
Annual turnover limits of 20–50% capture most alpha while minimizing cost drag.
Composites using multiple signals outperform single-factor portfolios in stability and efficiency.

Detailed comparison:

Full value strategy: 39% turnover, alpha = 2.7%.
Priority-best at 20% turnover: alpha = 2.9%, with a significant drop in trading costs.
Composite strategies achieved higher Sharpe ratios and lower drawdowns when rebalanced smartly.

Dynamic vs. Calendar Rebalancing

Calendar-based rebalancing (e.g., monthly/quarterly) is widespread but ignores signal evolution. A smarter approach:

Rebalance only when the portfolio drifts meaningfully from target.
Set rebalancing triggers based on signal drift thresholds.
Avoid unnecessary trades during periods of low change.

This event-driven rebalancing is especially effective for:

Momentum, where signals decay rapidly.
Composite portfolios, where offsetting trades naturally reduce turnover.

The authors demonstrate that combining event triggers with priority-best selection leads to even greater cost efficiency and alpha retention.

Trading Costs in Real Terms

The study integrates Chen & Velikov’s trading cost model, accounting for:

Bid-ask spreads.
Price impact.
Market depth.
Trade size.

Impactful takeaway:

High-turnover momentum strategy (305% turnover) loses up to half its alpha to trading costs.
With priority-best and a 50% turnover limit: most alpha retained, trading cost sharply reduced.

The authors argue that incorporating realistic trading cost modeling is essential in live strategy design.

Small-Cap Efficiency Gains

Small-cap portfolios are often more profitable in backtests due to pricing inefficiencies but are:

More expensive to trade.
More sensitive to turnover.

Smart rebalancing in small-cap space:

Yields higher alpha-to-cost ratios.
Provides greater benefit than in large-cap universes.

Example:

Small-cap composite: unconstrained net alpha = 3.2%.
Priority-best with turnover cap = 4.7%, higher Sharpe, lower drawdowns.

The strategy’s value becomes especially evident in constrained environments such as low-liquidity markets or capacity-limited funds.

Subsample Robustness and Economic Cycles

Performance tested across major U.S. market cycles:

1964–1983: Early testing.
1983–2002: Institutionalization.
2002–2020: Dot-com bust, GFC, quant crash.

Key takeaways:

Priority-best outperforms in each period.
Post-2000 value underperformance is less severe under smart turnover constraints.
Net alpha is more stable, indicating better resilience.

The findings suggest that smart rebalancing is robust across decades of economic and market evolution.

Implementation Guide for Investors

Practical steps for applying this research:

Use ranking algorithms to prioritize trades.
Cap turnover annually (e.g., 20–50%).
Build composite signals using robust z-scores.
Prefer event-based rebalancing over time-based.
Integrate trading cost models in portfolio optimization.
Monitor and update thresholds based on market liquidity conditions.

These principles can be implemented using standard tools such as Python, R, or commercial portfolio management systems.

Conclusion

Vincent Yip and Evan Michale Brown’s work at Finstock.co demonstrates that portfolio performance isn’t just about what you trade, but how you trade. By introducing and validating smart rebalancing—especially the Priority-Best method—they offer a clear and scalable way to bridge the implementation gap in factor investing.

In a world of shrinking alpha and rising competition, execution matters more than ever.

Smarter signals. Targeted trades. Sustainable returns.

Safe Equities: A Modern Alternative to Bonds in Portfolio Allocation

Thu, 17 Apr 2025 01:41:34 +0700

Rethinking Risk: How Safe Equities Can Replace Bonds in a Modern Portfolio

By Vincent Yip

Introduction: The Changing Face of Portfolio Safety

The 60/40 portfolio—a classic combination of 60% equities and 40% bonds—has long been the bedrock of long-term investment strategies. Its appeal lies in its balance: stocks provide growth, while bonds offer stability, cushioning investors during downturns.

But recent history has challenged this wisdom. In 2022, as markets reeled from rising interest rates and inflation shocks, both stocks and bonds posted deep losses. The S&P 500 fell 18%, and long-term U.S. Treasury bonds lost nearly 30%. For investors who had counted on bonds to offset equity risk, the results were alarming.

This backdrop sets the stage for a provocative and timely question: Are bonds still the best hedge in a diversified portfolio?

Vincent Yip proposes an alternative: rather than diversifying across asset classes, what if we diversified within equities by allocating to “safe” equities—stocks with lower risk to future earnings?

This idea challenges the orthodoxy of portfolio construction. And it’s backed by data, theory, and decades of financial insight.

What Are “Safe” Equities?

The term “safe equities” might sound like an oxymoron. Aren’t all stocks risky by nature?

Yes—but not equally so. Vincent Yip argues that risk should not be viewed solely through volatility or beta. Instead, risk should be tied to the vulnerability of a firm’s future earnings, because it is earnings that ultimately drive stock prices.

Through detailed fundamental analysis, he identifies companies that exhibit:

Stable, predictable earnings
Growing revenues and improving margins
Low dependency on speculative investments (like R&D with uncertain payoffs)
Strong balance sheets with low financial fragility
Historical ability to convert investments into realized income

These companies—when grouped together—form a portfolio that behaves differently than the broad market. They tend to decline less in downturns, while still participating in bull markets. In statistical terms, they have lower downside beta, positive skewness, and surprisingly strong returns.

The Historical Case for Safe Equities

Using data from 1975 to 2021, covering all U.S. publicly traded firms (excluding financials and utilities), Vincent Yip annually scored stocks based on the risk to future earnings and sorted them into deciles—Portfolio 1 being the “safest,” Portfolio 10 being the riskiest.

Here’s what he found:

🔹 Performance in Down Markets

In bear markets such as 2000–2002:

The S&P 500 fell by an average of −14.7%
Safe equities (Portfolios 1–3) dropped just −1.7%

Even during the 2008 financial crisis, while safe equities declined sharply (−43%), they rebounded faster and outperformed bonds in the recovery.

🔹 Performance in Up Markets

In bull markets like 1995–1999:

The S&P 500 returned 27.8% annually
Safe equities still returned 16.3%, giving up some upside—but not much

In the long moderate uptrend from 2009–2017, safe equities matched the market, returning 16.3%, showing they weren’t just defensive—they could also grow.

Replacing Bonds with Safe Equities: The Data Speaks

From 1975–2021:

Safe equities returned an average of 13.7% per year
10-year U.S. Treasury bonds returned 7.3%
The Sharpe ratio for safe equities was competitive with the S&P 500 and superior to bonds

Furthermore, the correlation between safe equity returns and bond returns was negative, especially post-2000. This means safe equities can offer diversification even without holding bonds.

Critically, in both positive and negative stock-bond correlation regimes, safe equities outperformed bonds on average.

A Better Hedge: Long–Short Safe Equity Strategy

Vincent Yip also explores a long–short hedge portfolio:

Long position in risky equities (Portfolios 8–10)
Short position in safe equities (Portfolios 1–3)

This zero-net-investment strategy earns an average of 9.3% annually, with low correlation to the market and negative correlation to bonds. It effectively hedges equity risk and can be used as an overlay to enhance a long-only equity strategy without requiring extra capital (aside from collateral for the short side).

In markets where bonds fail to hedge, this portfolio shines.

Safe Equities vs. Traditional Defensive Strategies

Vincent Yip compares safe equities with other low-risk strategies:

1. Low-Beta Stocks

While lower-beta portfolios have lower sensitivity to market returns, they don’t always reduce earnings risk.
His analysis shows “beta is dead” in terms of explaining excess returns.

2. Quality Stocks

Stocks with high profitability and low leverage.
Strong correlation (0.71) with safe equities.
However, “quality” lacks a clearly defined link to earnings risk in the way Vincent Yip’s safe equities do.

3. Low-Volatility Stocks

Often overlap with safe equities.
Return correlation: ~0.64.
But low volatility ≠ low earnings risk. Safe equities are rooted in fundamental financial analysis, not just return smoothness.

Accounting Behind the Safety

Vincent Yip grounds his method in accounting principles—especially:

Realization principle: Revenues and profits are booked only when they're earned, reducing future uncertainty.
Conservative accounting: High-risk investments (like R&D or brand-building) are not capitalized unless proven.

From these, he extracts fundamental indicators:

Sales growth
Margin improvement
Stable or declining SG&A and R&D as a percentage of sales
Higher accruals and reinvestment into productive assets (DNOA)
Sustainable external financing (EXTFIN)

These metrics help score stocks each year on their earnings safety.

Implications for Long-Term Investors

According to Merton’s Intertemporal Capital Asset Pricing Model (ICAPM), long-term investors need assets that hedge future risks to returns.

Traditional low-beta strategies fall short. They may reduce exposure to market movements but don’t address future income uncertainty.

Safe equities, on the other hand:

Protect against permanent loss of capital
Provide income stability
Offer some participation in growth cycles
Hedge effectively when bonds fail

They allow investors to stay in equities without taking on the full brunt of market swings.

Rethinking the 60/40: New Allocation Ideas

Vincent Yip simulates alternative allocations:

Classic:

60% S&P 500
40% 10-year U.S. Treasury bonds

Alternative:

60% S&P 500
40% safe equities

Results:

The 60/40 safe equity mix outperformed the bond mix in cumulative returns
It delivered better downside protection than the S&P alone
It maintained higher upside participation than bonds

Even when the market declined more than 5%, safe equities softened the blow (average return: −6.8%) while bonds returned +11.75%. But in up years, safe equities delivered much stronger gains than bonds (26.3% vs. 7.6%).

Conclusion: Safe Equities as a Modern Hedge

In a world of rising inflation, interest rate uncertainty, and structural shifts in markets, relying on bonds as your sole safety net may no longer suffice. Vincent Yip makes a strong case for rethinking what “safety” means.

Safe equities—identified through disciplined, fundamentals-based analysis—offer a compelling alternative. They are:

Less risky in earnings terms
Diversifying across regimes
Higher returning than bonds
Cost-efficient (especially in a long–short setup)

For investors, asset managers, and institutions seeking robust portfolio construction strategies, safe equities represent a 21st-century solution to a 20th-century problem.