Revisiting the CAPM

Challenging the Risk-Return Framework

Author

gitSAM

Published

March 21, 2025

Abstract
This study revisits the Capital Asset Pricing Model (CAPM) in light of increasing market concentration and its challenges to the risk-return framework. CAPM posits that systematic risk, measured by beta, is the only relevant determinant of expected returns, as unsystematic risk can be diversified away. However, rising market concentration raises concerns about whether diversification remains effective in practice. When a small number of firms dominate market returns, the traditional assumptions of efficient risk-sharing and competitive asset pricing may no longer hold. This study examines how concentration distorts expected returns, potentially leading to risk mispricing and a breakdown of the CAPM equilibrium. The findings suggest that market structure itself has become a non-diversifiable source of risk, necessitating a reassessment of traditional asset pricing models under contemporary market conditions.
Keywords

Market Concentration, Diversification

1 Introduction

The Capital Asset Pricing Model (CAPM), developed by Sharpe (1964), Lintner (1965), and Mossin (1966), remains a cornerstone of modern finance, linking expected returns to risk. It classifies risk into two categories: systematic risk, which stems from market-wide factors and cannot be diversified away, and unsystematic risk, which is asset-specific and can be mitigated through diversification. The CAPM posits that only systematic risk, measured by beta, justifies a return premium, as investors can eliminate unsystematic risk by holding a diversified portfolio, ideally approximating the market portfolio. This principle aligns with broader linear factor models like the Arbitrage Pricing Theory (APT) (Ross 1976), which extends the CAPM by incorporating multiple systematic risk factors while similarly dismissing diversifiable risk under no-arbitrage conditions.

However, the CAPM’s empirical validity has been contested. Banz (1981) documented the size effect, where small-cap stocks outperform CAPM predictions, while Basu (1977) identified the value effect, showing excess returns for stocks with high earnings-to-price ratios. These anomalies spurred the development of multifactor models, such as the Fama-French three-factor model (Fama and French 1993), which augment beta with size and value factors. Beyond these, market concentration has emerged as a critical lens for understanding asset pricing deviations. Hou and Robinson (2006) found that firms in concentrated industries earn higher returns, attributing this to economic rents from market power. Edmans (2009) linked ownership concentration to superior performance, while Choi et al. (2017) showed that institutional investors with concentrated portfolios outperform diversified ones. Neuhann and Sockin (2024) explored how financial market concentration distorts capital allocation, and Bustamante and Donangelo (2017) tied product market concentration to industry returns.

From a theoretical perspective, Magill and Quinzii (1996) argued that incomplete markets—lacking sufficient contingent claims—prevent full risk hedging, challenging CAPM assumptions. Cochrane (1996) emphasized the role of investment-based pricing, while Campbell (1992) critiqued volatility as an incomplete risk measure. Socioeconomic analyses, such as Saez and Zucman (2016), further connect market concentration to wealth inequality, highlighting broader implications. This rich body of literature suggests that market structure and concentration significantly complicate the CAPM’s risk-return framework, necessitating a deeper examination.

2 Main

2.1 Empirical and Mathematical Foundations of Diversification

Diversification’s risk-reducing power is well-established empirically and mathematically. Elton and Gruber (1977) analyzed 3,290 securities, demonstrating that a portfolio of just four stocks markedly reduces variance compared to a single stock, underscoring diversification’s practical utility.

Mathematically, consider an equally weighted portfolio of \(n\) securities. The portfolio variance \(\sigma_p^2\) is given by:

\[ \sigma_p^2 = \frac{1}{n} \bar{\sigma}^2 + \frac{n-1}{n} \bar{\rho} \bar{\sigma}^2 \]

where:

  • \(\bar{\sigma}^2\) = average variance of individual securities
  • \(\bar{\rho}\) = average correlation between securities

As \(n\) increases, \(\sigma_p^2\) converges to \(\bar{\rho} \bar{\sigma}^2\), indicating that covariance, not individual variance, dominates portfolio risk. When \(\bar{\rho} < 1\), diversification lowers volatility, resembling how higher-order terms in Taylor polynomials diminish to smooth a function or how fractal geometry simplifies irregularities with scale.

2.2 A Critique of Risk as Volatility

The CAPM equates risk with volatility, but this assumption is narrow. Long-term investors may prioritize structural risks—e.g., economic shifts or sector obsolescence—over short-term price swings. A volatile growth stock might be less “risky” to them than a stable but declining asset. Campbell (1992) supports this critique, arguing that volatility oversimplifies the multifaceted nature of risk, a view echoed by behavioral finance perspectives (Shiller 2003).

2.3 Diversification and the Risk-Return Trade-Off

The CAPM ties diversification to the risk-return trade-off, suggesting investors can eliminate unsystematic risk while earning returns proportional to systematic risk exposure. In a stochastic setting, diverse agents (e.g., farmers, energy producers) share idiosyncratic risks, enhancing welfare (Cochrane 2009). Yet, this assumes a broad, competitive market. When the market portfolio—say, the S&P 500—is dominated by a few highly correlated stocks (e.g., tech giants), diversification falters. High \(\bar{\rho}\) reduces variance’s sensitivity to \(n\), undermining the CAPM’s benefits (Grullon, Larkin, and Michaely 2019).

2.4 Market Concentration and the Upper Frontier

In the standard arbitrage-free asset pricing framework, the upper mean-variance frontier assets correlate perfectly (negatively) with the stochastic discount factor (SDF). In concentrated markets, dominant firms with economic moats (Bustamante and Donangelo 2017) act as principal components, compressing the payoff space. For example, if the Herfindahl-Hirschman Index (HHI) measures this dominance or concentration, then a high HHI signals reliance on few assets, limiting diversification. Investors may then seek arbitrage in these stocks, amplifying concentration (Valta 2012).

2.5 Implications for Investors and Market Stability

In concentrated markets, diversification yields to capturing rents from dominant firms. These firms use primary-market capital to reinforce moats, distributing profits rather than fostering competition (Hou and Robinson 2006). Secondary-market trading becomes zero-sum, redistributing wealth without value creation. Early investors in concentrated stocks gain disproportionately, widening inequality (Saez and Zucman 2016) and challenging the CAPM’s traditional data-driven risk-return logic.

2.6 Broader Socioeconomic Consequences

Concentration reduces contingent claim diversity, impairing hedging capacity (Magill and Quinzii 1996). A shock to a dominant sector triggers systemic ripples, increasing instability. This homogeneity shifts markets from managing uncertainty to rewarding market power, exacerbating wealth gaps and contradicting the CAPM’s egalitarian risk-sharing ideal.

3 Conclusion

The CAPM’s diversification-driven risk-return framework faces significant challenges from market concentration. As diversification weakens, investors prioritize rents over risk reduction, simplifying markets into systems dominated by a few firms. This shift threatens stability, equity, and hedging capacity, urging a rethinking of the CAPM and policies to enhance market diversity.

References

Banz, Rolf W. 1981. “The Relationship Between Return and Market Value of Common Stocks.” Journal of Financial Economics 9 (1): 3–18.
Basu, Sanjoy. 1977. “Investment Performance of Common Stocks in Relation to Their Price‐earnings Ratios: A Test of the Efficient Market Hypothesis.” The Journal of Finance 32 (3): 663–82.
Bustamante, M Cecilia, and Andres Donangelo. 2017. “Product Market Competition and Industry Returns.” The Review of Financial Studies 30 (12): 4216–66.
Campbell, John Y. 1992. “Intertemporal Asset Pricing Without Consumption Data.” National Bureau of Economic Research Cambridge, Mass., USA.
Choi, Nicole, Mark Fedenia, Hilla Skiba, and Tatyana Sokolyk. 2017. “Portfolio Concentration and Performance of Institutional Investors Worldwide.” Journal of Financial Economics 123 (1): 189–208.
Cochrane, John H. 1996. “A Cross-Sectional Test of an Investment-Based Asset Pricing Model.” Journal of Political Economy 104 (3): 572–621.
———. 2009. Asset Pricing: Revised Edition. Princeton university press.
Edmans, Alex. 2009. “Blockholder Trading, Market Efficiency, and Managerial Myopia.” The Journal of Finance 64 (6): 2481–2513.
Elton, Edwin J, and Martin J Gruber. 1977. “Risk Reduction and Portfolio Size: An Analytical Solution.” The Journal of Business 50 (4): 415–37.
Fama, Eugene F, and Kenneth R French. 1993. “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33 (1): 3–56.
Grullon, Gustavo, Yelena Larkin, and Roni Michaely. 2019. “Are US Industries Becoming More Concentrated?” Review of Finance 23 (4): 697–743.
Hou, Kewei, and David T Robinson. 2006. “Industry Concentration and Average Stock Returns.” The Journal of Finance 61 (4): 1927–56.
Lintner, John. 1965. “The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets.” The Review of Economics and Statistics 47 (1): 13–37.
Magill, Michael, and Martine Quinzii. 1996. “Incomplete Markets over an Infinite Horizon: Long-Lived Securities and Speculative Bubbles.” Journal of Mathematical Economics 26 (1): 133–70.
Mossin, Jan. 1966. “Equilibrium in a Capital Asset Market.” Econometrica 34 (4): 768–83.
Neuhann, Daniel, and Michael Sockin. 2024. “Financial Market Concentration and Misallocation.” Journal of Financial Economics 159: 103875.
Ross, Stephen A. 1976. “The Arbitrage Theory of Capital Asset Pricing.” Journal of Economic Theory 13 (3): 341–60.
Saez, Emmanuel, and Gabriel Zucman. 2016. “Wealth Inequality in the United States Since 1913: Evidence from Capitalized Income Tax Data.” The Quarterly Journal of Economics 131 (2): 519–78.
Sharpe, William F. 1964. “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk.” The Journal of Finance 19 (3): 425–42.
Shiller, Robert J. 2003. “From Efficient Markets Theory to Behavioral Finance.” Journal of Economic Perspectives 17 (1): 83–104.
Valta, Philip. 2012. “Competition and the Cost of Debt.” Journal of Financial Economics 105 (3): 661–82.