How to Spot Outperformers Using WACC
How to use WACC to screen for outperformers + see which stocks are killing your diversification
In this week’s report:
An integrated method for calculating WACC and using it to identify stocks likely to outperform
Why high-volatility but low-correlation stocks are the best diversifiers for your portfolio
1. An integrated method for calculating WACC and using it to identify stocks likely to outperform
From Analyst Forecasts to the Q-Theory of Corporate Investments: A Novel Methodology for Directly Estimating WACC (October 13, 2025) - Link to paper
TLDR
Instead of making assumptions that stack errors in order to calculate WACC, set the WACC equal to the IRR that matches analysts’ forecasts to a stock’s market price
This WACC methodology can screen for companies with low vs high WACC: high-WACC companies that keep investing heavily in CAPX underperform the market, whereas companies investing heavily with cheap capital outperform (the market rewards them over time)
The methodology passes two validation tests: risky industries like Materials show a higher WACC of 9.69% versus 4.42% for safer Utilities, and firms with higher WACC invest less in future quarters, which is in-line with what Q-theory predicts
What the authors did
Anyone who's built a DCF model knows the frustration: estimating WACC requires calculating cost of equity via CAPM, determining cost of debt, measuring market values, and making assumptions about capital structure. Each component introduces error, and those errors compound. The result is a discount rate that demands precision but delivers guesswork.
The authors' approach uses analyst forecasts embedded in a Q-theory framework to derive WACC directly. The elegance lies in its simplicity: if analysts are already forecasting operating profits and the market prices securities based on those expectations, the implied cost of capital reveals itself through the mathematics of discounted cash flows.
Why this new methodology is superior
The traditional approach suffers from compounding measurement errors:
The traditional WACC formula requires:
Cost of Equity → Estimate using CAPM with historical returns
Cost of Debt → Estimate from current yields or interest expense ratios
Market values → Calculate market value of equity and debt
Tax rates and capital structure → Make assumptions about optimal leverage
Each component introduces error, and those errors compound when combined.
Why the new approach wins:
Forward-looking, not backward-looking: Uses what analysts expect about future operating profits, not what happened in the past. Markets price securities based on expectations, so this captures what's actually embedded in current valuations.
Fewer heroic assumptions: You don't need to estimate a cost of equity, then separately estimate cost of debt, then weight them. The market is already telling you the firm-wide hurdle rate through its valuation.
Empirically validated: The cross-sectional patterns (Materials 9.69%, Utilities 4.42%) match intuitive risk rankings. The measure correlates appropriately with volatility, beta, size, and predicts investment behavior consistent with Q-theory. Traditional WACC often produces nonsensical results.
The key insight: Analysts are already doing the hard work of forecasting future profitability. If the market prices securities efficiently relative to those forecasts, the discount rate is revealed through the math. No need to estimate it indirectly through noisy historical returns.
Cross-sectional results validate the methodology
The estimated WACC figures pass basic sanity checks with impressive precision. Materials companies face the highest costs at 9.69%, followed by Information Technology at 8.62%. Meanwhile, Utilities enjoy the lowest at 4.42%, with Consumer Staples at 6.23%. These numbers align perfectly with intuitive risk assessments.
More importantly, the methodology produces WACC estimates that correlate appropriately with traditional risk measures: positively with volatility, market beta, and factor exposures, while moving inversely with market cap, leverage, and debt maturity. This isn't a statistical curiosity; it's validation that the measure captures genuine economic risk.
Practical applications for institutional investors
The Circularity Problem
If you calculate WACC as: Enterprise Value (today) = PV of forecasted cash flows discounted at WACC
Then by definition, the WACC makes the stock "fairly valued" at today's price. You cannot then turn around and use that same WACC to run a DCF and conclude the stock is mispriced. That's circular logic.
But you CAN use this new WACC for:
1. Portfolio Construction Based on WACC Patterns: The paper's key insight is that High WACC + High Investment firms underperform. You're not saying "this stock is cheap." You're saying "this combination of characteristics predicts poor returns." That works because you're using WACC as a signal about future behavior, not as an input to value the same stock.
2. Corporate Investment Decisions (Not Stock Picking): If you're a CFO, you can use this methodology to estimate YOUR OWN hurdle rate for capital budgeting. You're not trying to find mispriced stocks. You're trying to understand what return your investors require.
3. Relative Risk Assessment: "Company A has 12% WACC, Company B has 6% WACC" tells you about relative riskiness and required returns, even if both stocks are fairly priced.
4. Macro/Aggregate Analysis: Using aggregate WACC to predict future corporate investment.
5. Indirect Security Selection Approaches: You COULD use this new WACC for security selection, but indirectly:
Approach 1: Peer Group Benchmark
Calculate implied WACC for 20 similar companies
Use the median as your discount rate
Apply to individual firms to find outliers
Approach 2: Abnormal Implied Returns
If a stock's implied WACC is 15% but its risk characteristics suggest it should be 8%, that's interesting
You're not using the WACC to value; you're identifying a disconnect between implied returns and risk
Approach 3: Changes Over Time
If a company's implied WACC suddenly spikes without fundamental risk changes, maybe the stock got oversold
2. Why high-volatility but low-correlation stocks are the best diversifiers for your portfolio
Portfolio Insights from a Contribution Analysis of the Diversification Ratio (October 13, 2025) - Link to paper
TLDR
High-volatility, low-correlation assets are true diversifiers; low-volatility, high-correlation assets are concentrators that create the illusion of safety
A mathematical decomposition can reveal exactly which positions enhance vs. diminish portfolio that add risk without reward
The framework provides actionable rebalancing guidance: positions with positive contributions deserve higher weights, while negative contributors signal concentration risk
What the author did
The “diversification ratio” compares the weighted average of individual asset volatilities to portfolio volatility. A ratio of 1.85 means your portfolio's volatility is 85% lower than it would be if all assets moved in lockstep. The innovation here is decomposing this ratio to show exactly how much each position contributes to - or detracts - from diversification.
How the decomposition works
The math exploits a property called "degree-0 homogeneity": the diversification ratio depends only on relative weights, not absolute dollars. This means the contributions across all positions must sum to zero, creating a zero-sum framework.
Each asset's contribution splits into two competing forces:
The volatility effect: Higher-volatility assets mechanically increase the diversification ratio. An asset with 20% volatility in a 10% volatility portfolio contributes 2x its weight.
The correlation penalty: Assets that move with the portfolio (measured by beta) reduce diversification. An asset with a beta of 1.0 perfectly tracks the portfolio and drags down the diversification ratio.
The formula: Contribution = (Weight × Asset Vol / Portfolio Vol) - Diversification Ratio × Weight × Beta
Here’s a concrete 2-stock example
Let’s assume that you hold a 50/50 portfolio of two stocks:
Stock A (Tech): 30% volatility, beta to portfolio = 1.3
Stock B (Utility): 15% volatility, beta to portfolio = 0.7
Portfolio volatility: 18%
Diversification ratio: 1.25
Now apply the formula for each stock:
Stock A contribution:
= (50% × 30% / 18%) - 1.25 × 50% × 1.3
= 0.833 - 0.813
= +0.02 (a positive number indicates a ‘diversifier’ of risk)
Stock B contribution:
= (50% × 15% / 18%) - 1.25 × 50% × 0.7
= 0.417 - 0.438
= -0.02 (a negative number indicates a ‘concentrator’ of risk)
The volatility effect shows Stock A contributes 0.833 (its high volatility adds to the diversification ratio numerator), while Stock B only adds 0.417. But the correlation penalty reveals the hidden cost: Stock A's 1.3 beta means it amplifies portfolio moves, costing 0.813. Stock B's 0.7 beta costs just 0.438.
The result: Stock A barely helps diversification (+0.02) while Stock B slightly hurts it (-0.02). The contributions sum to zero – in any portfolio, diversifiers and concentrators must balance.
If you want better diversification, shift weight toward Stock A (the diversifier) or find a Stock C with even lower beta to the portfolio.
Why this matters for portfolio construction
Most portfolio managers focus on correlation matrices and volatility, but miss the interaction effects. A low-volatility, high-correlation asset can hurt diversification more than a high-volatility, low-correlation asset helps it.
This framework provides three practical applications:
Rebalancing guidance: Tilt toward positions with positive contributions.
Risk factor identification: Large negative contributions signal concentrated factor bets. A negative contribution to diversification reveals that the asset is essentially a leveraged portfolio position.
Due diligence: When evaluating new positions, compute their expected contribution before allocation – you need low beta to the existing portfolio.
The diversification ratio decomposition transforms a portfolio-level metric into position-level intelligence. Instead of asking "is my portfolio diversified?" you can ask "which positions are creating vs. destroying diversification?"
The zero-sum constraint is the key insight: not all holdings can simultaneously diversify. Every portfolio contains concentrators by mathematical necessity. The question is whether you're compensated for that concentration risk.
For implementation, calculate contributions quarterly. Positions with persistently negative contributions should justify themselves through alpha – otherwise they're diluting your diversification without adding value.
Disclaimer
This publication is for informational and educational purposes only. It is not investment, legal, tax, or accounting advice, and it is not an offer to buy or sell any security. Investing involves risk, including loss of principal. Past performance does not guarantee future results. Data and opinions are based on sources believed to be reliable, but accuracy and completeness are not guaranteed. You are responsible for your own investment decisions. If you need advice for your situation, consult a qualified professional.
