The Rising Relevance of Risk-Adjusted Returns in Portfolio Construction

In an industry where noise often masquerades as insight, one of the most persistent illusions has been that all positive returns are created equal. For too long, portfolios have been judged on nominal performance, as if riding the tide of a bull market were a testament to skill. But in today’s environment—defined by tighter risk budgets, rising macro uncertainty, and institutional scrutiny—the question is no longer “Did we outperform?” but rather “How did we outperform?”

At Resonanz Capital, we believe this distinction is more than semantic—it’s foundational to responsible portfolio construction. That belief is precisely why we built Ensemble—our proprietary performance attribution engine that helps allocators separate genuine alpha from market noise. Ensemble automates the kind of risk-adjusted analysis this article explores, bringing clarity and accountability into every allocation decision.

As asset owners shift from naïve benchmarks to outcome-oriented frameworks, beta-adjusted and factor-adjusted returns are moving from theoretical constructs to essential tools. This article unpacks the why, what, and how of this evolution. It’s not about chasing alpha in abstraction; it’s about measuring what truly matters in a world where every basis point must justify its risk.

What are beta-adjusted and factor-adjusted returns, and how do they differ from traditional metrics?

Beta-adjusted returns evaluate an investment’s performance relative to the market risk it took on. Rather than simply looking at raw returns, this approach adjusts for the asset’s beta—its sensitivity to market movements—to isolate how much return was generated per unit of market risk. In essence, it captures alpha in the CAPM framework: the part of the return not explained by market exposure. For example, if a fund earned 10% while the market rose 8%, but had a beta of 1.5 (implying an expected return of 12%), the beta-adjusted return would be –2%—indicating underperformance after accounting for risk.

Factor-adjusted returns extend this concept by controlling for multiple risk factors—not just market beta, but also size, value, momentum, quality, and others. The idea is to strip out returns attributable to known factor exposures and reveal the residual, skill-based return. A fund might outperform a benchmark, but if most of its return came from a tilt toward high-beta or growth stocks, it’s not true alpha—it’s a replicable factor exposure.

Source: bfinance

Traditional metrics like raw or benchmark-relative returns miss these nuances. They treat all outperformance as skill, even when it's driven by common risk factors. Beta- and factor-adjusted returns offer a clearer view by asking: How much return came from genuine skill after adjusting for the risks taken?

This distinction is critical for allocators. Beta and factor exposures can be obtained cheaply through index products. True alpha is rare and expensive. Evaluating performance through a risk-adjusted lens ensures investors are paying for skill—not for exposure they could replicate more efficiently.

Why is this becoming important now for allocators ?

Risk-adjusted performance has always mattered—but today, it's essential. The rise of passive investing and smart beta strategies has made it easier than ever to access market and factor exposures cheaply. Allocators are now focused on avoiding “active fees for passive bets.” Beta-adjusted returns help distinguish skill-based alpha from returns driven by high beta or popular factor tilts.

This scrutiny is amplified in the current market regime. With higher inflation, rising rates, and cross-asset volatility, traditional benchmarks no longer tell the full story. When both equities and bonds fall—as they did in 2022—understanding why a portfolio moved matters more than how much. Boards and investment committees increasingly demand clarity: was outperformance due to thoughtful positioning or excess risk?

Risk budgeting adds further urgency. Institutions now allocate not just capital, but risk. Every unit of volatility must earn its keep. If a return can be replicated via low-cost beta, there’s no justification for paying for active management. As a result, allocators deconstruct performance into beta, factor, and true alpha components—separating the replicable from the rare.

Source: Goldman Sachs Investment Research

Lastly, regulatory and fiduciary pressure is catching up. GIPS standards, value-for-money frameworks, and institutional due diligence now often require clear attribution of performance. Simply delivering returns is no longer enough. Demonstrating how those returns were earned—through genuine alpha, not disguised beta—is becoming the new standard.

What models and assumptions go into calculating risk-adjusted returns?

Beta-adjusted returns are most commonly derived from regression models. The simplest is CAPM, where an asset’s excess return is regressed against a market index. The slope is beta (market sensitivity), and the intercept is alpha—i.e., return unexplained by market exposure. If a portfolio has a beta of 1.2 and the market returns 5%, the expected return is 6%; anything above or below that is alpha.

Factor-adjusted returns expand this by including multiple systematic risk factors—size, value, momentum, etc.—often using models like Fama-French. Here, alpha reflects what’s left after accounting for all modeled exposures. The choice of factors and time horizon matters: different models can yield different alpha estimates, which is why robustness checks are key.

In public markets, calculating these metrics is relatively straightforward given the availability of return and benchmark data. Tools like Bloomberg PORT or MSCI Barra offer real-time factor analysis. The main assumption is that relationships are stable and linear—though this often breaks down in volatile markets.

In private markets, it’s trickier. Illiquidity and smoothed valuations distort risk estimates. Techniques like the Public Market Equivalent (PME) and Direct Alpha compare private asset returns to hypothetical public benchmarks, adjusting for timing and opportunity cost. Others apply regressions against proxy indices, though these require unsmoothing to reflect true volatility.

Ultimately, all these models aim to isolate what couldn’t be explained by taking risk. The output isn’t perfect—but it’s far better than raw returns alone.

How are beta- and factor-adjusted metrics used in portfolio construction and risk budgeting?

eta- and factor-adjusted returns are central to modern portfolio construction. They shift the focus from chasing raw returns to allocating capital based on risk-adjusted outcomes.

Risk Budgeting:
Instead of spreading risk evenly or allocating by capital, investors now allocate based on marginal contribution to risk and reward. If Asset A earns 8% mostly from beta, while Asset B earns 5% mostly from alpha, risk budgeting favors B. The goal: maximize return per unit of risk, not just return alone.

Position Sizing:
Allocators size positions to reflect intended exposures. A fund with a 0.5 equity beta might get twice the capital of a beta-1 fund to achieve the same market exposure—or be scaled back in a market-neutral portfolio. Portable alpha strategies use this logic explicitly: buy cheap beta via index funds, overlay it with true alpha from hedge funds.

Manager Selection:
True alpha is scarce. Adjusted metrics reveal whether a manager’s returns stem from genuine skill or disguised exposure (e.g., credit, duration, small-cap bias). If it’s replicable, it’s not worth the fee. Consistent factor-adjusted alpha across regimes is what allocators are paying for.

Ongoing Monitoring:
Performance attribution now routinely decomposes returns into beta, factor, and alpha. If a manager outperforms because of a credit rally or duration bet, that insight shapes reallocation and oversight. Constraints like “max 0.3 equity beta” are increasingly formalized in mandates.

Stress Testing and Scenario Planning:
Knowing factor exposures allows allocators to simulate shocks (e.g., rising rates, equity drawdowns). This informs diversification, helps avoid concentration risk, and ensures that portfolio resilience is intentional—not accidental.

In short, adjusted returns anchor every step: from selecting managers, to sizing positions, to controlling risk. They’ve become the lens through which sophisticated portfolios are built.

What are the challenges and pitfalls of using beta- and factor-adjusted returns, and how can they be addressed?

While powerful, beta- and factor-adjusted metrics come with real-world limitations.

Model Risk:
CAPM and multi-factor models are only as good as the inputs. A simple model might mislabel exposure as alpha, while an overfit model might “explain away” real skill. Solution: test multiple models, focus on economically meaningful factors, and validate over long periods.

Beta Instability:
Betas shift across regimes—what’s low risk in calm markets may spike in stress. Static models can mislead. Investors increasingly use rolling regressions or dynamic models that adjust to volatility regimes.

Illiquidity and Smoothed Returns:
Private assets often show artificially low volatility and inflated alpha due to appraisal-based pricing. Adjustments like “unsmoothing” and PME/Direct Alpha techniques help reveal the true risk and return profile.

Noise and Overfitting:
More factors ≠ better insight. Adding obscure variables can create false confidence. The solution is parsimony—stick to well-understood drivers unless there’s a clear thesis—and emphasize consistency over one-off results.

Communication Risk:
These metrics can give a false sense of precision. Saying a fund has “1.47% alpha” may mislead. Allocators should use ranges, visuals, and context to explain findings, and avoid overstating confidence in any one number.

Ultimately, these tools are guides—not verdicts. Used thoughtfully, they elevate decision-making. Misused, they obscure it.

How might the use of beta-adjusted returns evolve with AI, alternative data, and regulation?

The next evolution in performance attribution is already underway—driven by smarter models, richer data, and growing demands for transparency.

AI and Dynamic Modeling:
Machine learning can identify non-linear relationships and shifting betas that static models miss. Tools like regime detection (e.g., via Gaussian Mixture Models) allow portfolios to adapt factor exposures in real time. Expect AI to enhance both accuracy and timeliness of beta estimates.

Alternative Data:
From satellite imagery to sentiment scores, non-traditional data is becoming part of risk models. These inputs can create new factors—such as ESG sentiment or supply chain risk—improving attribution and uncovering hidden exposures. For private assets, proxies derived from alternative data may offer better beta estimation than traditional marks.

Regulatory Push:
Frameworks like GIPS 2020 already encourage risk-adjusted reporting. Expect regulators to formalize this—mandating disclosure of not just return, but how it was earned. Fiduciary duty increasingly implies demonstrating skill, not just performance.

Tech Integration:
Platforms like Aladdin and Venn are embedding factor analytics into daily workflows. Soon, portfolio managers will get natural-language insights like: “+0.3% return today—mostly value factor, no meaningful alpha.” Attribution will become a live feedback loop, not just a quarterly review.

In short, beta- and factor-adjusted returns are evolving from back-office diagnostics to real-time decision tools. The future belongs to investors who can separate signal from noise—with precision, agility, and accountability.

Conclusion

The investing world is shifting from what you earned to how you earned it. In an era of tight risk budgets, fee scrutiny, and market complexity, beta- and factor-adjusted returns are no longer optional—they’re foundational.

These metrics cut through surface-level performance, revealing whether gains came from skill or from riding market trends. For allocators, that distinction drives everything: manager selection, position sizing, risk control, and ultimately, trust in the portfolio’s design.

The tools aren’t perfect—but they’re indispensable. Used thoughtfully, they sharpen decisions, expose hidden risks, and ensure every unit of risk is working as hard as it should. The message is clear: performance attribution isn’t about academic precision—it’s about practical clarity. In the years ahead, investors who embrace this lens will be better positioned to deliver resilient, efficient, and accountable portfolios.

Want to see how these principles come to life in practice? Ensemble is Resonanz Capital’s proprietary platform for beta- and factor-adjusted performance analysis. Built for allocators, it delivers real-time insights into the true drivers of return—so you can invest with confidence, clarity, and control. Click the link below to start your free 2-week trial!