CAPM & Factor Models Explained
Modern Portfolio Theory & the Efficient Frontier shows why diversification reduces unsystematic risk, but the residual market risk remains. CAPM answers the next interview question: what return should an investor require for taking systematic risk? Factor models then extend the discussion by showing that equity risk in India is also interpreted through size, value, and momentum effects.
- CAPM: E(R) = Rf + β × [E(Rm) - Rf].
- CAPM compensates only for systematic risk. Unsystematic risk is 'free to eliminate.'
- For Reliance Industries, Rf = 7.0%, E(Rm) = 14.0%, MRP = 7.0%, β = 1.05, so Expected Return E(R) = 7.0% + 1.05 × 7% = 14.35%.
- Actual recent return of ~16% (FY24) outperformed the required return for Reliance, indicating positive alpha.
- β > 1.2 is high-beta and amplifies market moves; β = 0.6-0.9 is defensive and less volatile.
- Indian empirical evidence strongly supports the size and value premiums.
- Momentum factor (Carhart 4-factor): Nifty 200 Momentum 30 Index ~22% CAGR since inception.
Big Picture: From Required Return to Factor Risk
CAPM is used as a required-return calculator: it starts with the risk-free rate, adds the market risk premium, and scales that premium by beta. Factor models like Fama-French then interpret equity risk beyond a single market beta, using Indian evidence on size, value, and momentum premiums.
CAPM: E(R) = Rf + β × [E(Rm) - Rf]
CAPM Inputs and Reliance Industries Worked Example
CAPM uses the risk-free rate (Rf), expected market return E(Rm), market risk premium (MRP), and beta (β) to estimate the required return for a stock. In the Reliance Industries example, beta is measured using 5-year monthly regression vs Nifty.
The situation is a Reliance Industries required-return estimate. The problem is to decide whether the actual recent return is above the CAPM-predicted return. The framework is CAPM: using Rf = 7.0%, E(Rm) = 14.0%, MRP = 7.0%, and β = 1.05 gives Expected Return E(R) = 14.35%.
The decision is that Reliance needed to beat 14.35% to create positive alpha. The outcome is that the actual recent return was ~16% (FY24), so it outperformed and delivered positive alpha.
Beta Interpretation
Beta (β) measures sensitivity to market moves. In interview answers, the key is not just quoting beta, but interpreting what it implies for how a stock behaves relative to the market.
Fama-French 3-Factor: Indian Evidence
Indian empirical evidence strongly supports the size and value premiums. This is where factor models add depth to a CAPM answer, because multiple risk factors matter.
SMB = Small Minus Big (small-cap premium); HML = High Minus Low (value premium - high B/P outperforms).
- Small-cap premium: Nifty Smallcap 250 CAGR ~19.5% vs Nifty 50 ~14% over 10Y - consistent with positive SMB factor.
- Value premium: BSE Value Index outperformed BSE Growth Index by ~200 bps annually - consistent with positive HML.
- Momentum factor (Carhart 4-factor): Nifty 200 Momentum 30 Index ~22% CAGR since inception.
Structuring a CAPM & Factor Models Explained Interview Answer
"Explain CAPM as a required-return calculator, then show how beta and multi-factor models interpret equity risk in India."
The strongest answers do not stop at the formula. They calculate the required return, interpret beta using Indian examples, and then explain why Fama-French and Carhart factors matter when multiple risk factors matter.
The most frequent error is treating CAPM as if β captures all risk. CAPM compensates only for systematic risk, while Indian evidence strongly supports the size and value premiums and also shows a momentum factor.
Conclusion
CAPM gives a clean required-return estimate using Rf, market premium, and beta, while factor models add a richer interpretation of equity risk through size, value, and momentum. In interviews, the winning answer is formula plus calculation plus Indian-market interpretation.