CAPM (Capital Asset Pricing Model)
El modelo fundacional que relaciona el retorno esperado de un asset con su riesgo sistemático medido por beta —pillar de la teoría de valoración moderna aunque con limitaciones empíricas significativas.
¿Qué es CAPM?
El Capital Asset Pricing Model (CAPM) fue desarrollado por William Sharpe (1964), John Lintner (1965) y Jan Mossin (1966) como extensión de Modern Portfolio Theory. Sharpe won Nobel en 1990 por el CAPM. La fórmula central: E(Ri) = Rf + βi × (E(Rm) − Rf), donde: E(Ri) = expected return of asset i; Rf = risk-free rate; βi = asset's beta (sensitivity to market); E(Rm) = expected market return; (E(Rm) − Rf) = market risk premium. Interpretation: expected return equals risk-free rate plus asset's share of market risk premium, scaled by beta. Higher beta = higher expected return (compensation for higher systematic risk). CAPM implies: (1) only systematic risk (market risk, captured by beta) is rewarded; (2) idiosyncratic risk (company-specific) can be diversified away, so not compensated; (3) in efficient markets, all assets line up along "Security Market Line" (SML) connecting Rf to market portfolio. Es widely-used framework para cost-of-capital calculations, asset valuation, performance attribution. Despite empirical limitations discussed below, CAPM influences nearly every investment professional's mental models.
Beta: El Coeficiente Central
Beta (β) es measure of asset's sensitivity to market movements. Formally: β = Cov(Ri, Rm) / Var(Rm). Interpretation: β = 1 asset moves exactly con market (SPY has β = 1 by definition); β = 1.5 asset moves 1.5× market (amplified); β = 0.5 moves half as much (defensive); β = 0 uncorrelated with market; β < 0 inversely correlated (rare). Typical betas: tech stocks 1.2-1.8 (high sensitivity), utilities 0.4-0.7 (defensive), financials 1.1-1.4 (cyclical), consumer staples 0.6-0.8 (defensive). Gold stocks often -0.2 to 0.3 (defensive/negatively correlated). Calculation: typically regressing stock returns vs. market returns over 36-60 months of monthly data. Services like Yahoo Finance, Bloomberg provide beta estimates. Interpretation caveats: (1) Historical beta may not persist: company's business changes, beta changes. (2) Time-varying: beta shifts across market regimes —beta can be 1 during normal times, 2 during crisis (leverage effect). (3) Timeframe sensitivity: 1-year beta vs 5-year beta differ. (4) Calculation varies: weekly vs. monthly returns, different benchmarks, different time periods produce different numbers. Beta is useful approximation, not precise value.
Security Market Line
La Security Market Line (SML) es la visualización geométrica de CAPM. Plot beta en X-axis vs. expected return en Y-axis. The line passes through (0, Rf) and (1, E(Rm)). All assets, in equilibrium, should lie on this line. Slope = market risk premium (Rm - Rf). Interpretation: (1) Assets above SML are undervalued (offering more return than CAPM predicts for their beta); (2) Assets below SML are overvalued (too little return for their beta); (3) Assets on SML are fairly priced. Theoretical insight: if markets are efficient, all assets should converge to SML through buying/selling pressure. Undervalued assets get bought (driving price up, return down); overvalued assets sold (price down, return up). Reality: SML is theoretical ideal; actual market shows persistent deviations. Empirical tests have found systematic deviations (low-beta stocks outperform their CAPM predictions, high-beta underperform). Challenges: (a) difficult to measure true market portfolio (all risky assets globally); (b) historical betas unstable; (c) empirical anomalies (size effect, value effect) not captured. Still, SML framework informs thinking: higher systematic risk should command higher expected returns.
Empirical Challenges y Extensions
CAPM has faced empirical challenges. Fama-French studies (1990s) found: (1) Value stocks outperform CAPM predictions —stocks con low P/B ratios earn higher returns than beta predicts. (2) Size effect: small-cap stocks outperform CAPM predictions. (3) Low-beta anomaly: low-beta stocks earn higher returns than CAPM predicts, high-beta lower. These findings motivated extensions to CAPM: (a) Fama-French Three-Factor Model: adds SMB (small minus big, size factor) and HML (high minus low book-to-market, value factor). More empirically successful than CAPM alone. (b) Carhart Four-Factor Model: adds momentum factor. (c) Fama-French Five-Factor Model: adds profitability and investment factors. (d) Arbitrage Pricing Theory (APT): generalizes CAPM to multiple factors. Modern factor investing (Dimensional Fund Advisors, AQR, BlackRock factor ETFs) is commercialization of these extensions. While CAPM is conceptual foundation, actual portfolio construction uses multi-factor approaches. Core CAPM insight —expected returns related to systematic risk factors— survives; specific "market is the only factor" has been extended.
Aplicaciones del CAPM
Applications de CAPM en práctica: (1) Cost of equity calculation: para DCF valuations, estimate cost of equity = Rf + β × (market risk premium). Used universally in finance/valuation. (2) Portfolio performance attribution: decompose portfolio returns into market (beta) component and alpha (excess return). "Alpha" is CAPM concept. (3) Risk management: assess portfolio beta, identify exposure to systematic risk. Targeting specific beta level is common strategy. (4) Index construction: CAPM logic informs weighting schemes. (5) Sharpe Ratio calculation: uses CAPM framework implicitly. (6) Investment communication: "This portfolio has beta 1.2" is standard description —CAPM-derived. (7) Required return hurdles: companies use CAPM to set investment hurdle rates for capital budgeting decisions. (8) Sell-side research: analyst targets often grounded in CAPM-based valuations. Even acknowledging empirical limitations, CAPM provides conceptual vocabulary that's unavoidable in investment profession. Students must learn it; practitioners use it while aware of limitations.
Aplicación en Opciones
CAPM en opciones trading: (1) Portfolio beta awareness: options positions have "effective beta" based on underlying stock beta and position's delta. Long calls on high-beta tech have amplified beta. Important for understanding portfolio risk. (2) Hedging betas: if portfolio has high beta exposure, short SPY calls or long SPY puts can reduce beta —effectively hedging market exposure. (3) Sector rotation via options: shift exposure from high-beta (cyclicals) to low-beta (defensives) during uncertainty. Options allow rapid rotation. (4) Defensive strategies: during expected bear markets, reduce portfolio beta via hedges. Options are capital-efficient hedges. (5) Low-beta anomaly plays: some research suggests low-beta stocks outperform. LEAPS on low-beta quality names could exploit this anomaly with low capital. (6) Volatility trading: relates to CAPM extensions. Buying VIX calls provides anti-market-factor exposure (vol spikes during equity declines). (7) Market-neutral strategies: long high-beta / short low-beta within sector creates market-neutral exposure while capturing factor. Options can implement capital-efficiently. (8) Cost of equity calculations: when evaluating companies for LEAPS positions, CAPM provides discount rate for DCF valuations.