High Minus Low (HML): Understanding the Value Premium What is HML? High Minus Low (HML) — often called the value premium — measures the return spread between value stocks (high book-to-market ratios) and growth stocks (low book-to-market ratios). It captures the historical tendency for value stocks to outperform growth stocks and is used to evaluate how value versus growth exposure contributes to portfolio returns. How HML is constructed
* Book-to-market ratio = book value of equity / market value of equity. High ratios indicate “value” stocks; low ratios indicate “growth.”
* HML = average return of high book-to-market portfolios − average return of low book-to-market portfolios.
* Practically, researchers sort stocks by book-to-market into portfolios (often quintiles or deciles) and calculate the difference in returns between the top and bottom groups.
Role in the Fama–French models
* Three-factor model (1992): Explains portfolio excess returns using three factors — market (excess market return), SMB (Small Minus Big, size premium), and HML (value premium). Regressing portfolio excess returns on these factors separates returns attributable to market, size, and value effects from manager skill.
* Five-factor model (2014): Adds profitability (RMW — Robust Minus Weak) and investment (CMA — Conservative Minus Aggressive) to the original three. These additional factors account for differences in returns tied to firms’ profitability and investment patterns while HML remains part of the expanded framework.
Interpreting HML beta
* HML beta is the coefficient from regressing a portfolio’s excess returns on the HML factor.
* Positive HML beta: portfolio behaves like value stocks (exposed to the value premium).
* Negative HML beta: portfolio behaves like growth stocks.
* A large positive HML loading indicates much of a fund’s historical outperformance may be attributable to value exposure rather than unique manager skill.
Why Fama–French is often preferred to CAPM
* CAPM explains returns with a single market factor; it often fails to capture cross-sectional patterns tied to size and value.
* The Fama–French models empirically explain more variation in returns by adding factors for size, value, profitability, and investment.
* Model performance can depend on portfolio construction and time period, so neither model is universally definitive.
Practical implications for investors
* Use HML (and the broader Fama–French factors) to understand return drivers and risk exposures in portfolios.
* Evaluating a manager’s performance with factor regressions helps separate factor-driven returns from true alpha.
* Allocations to value vs. growth have historically affected returns; investors should consider factor tilts relative to their objectives and risk tolerance.
Summary HML is a core factor for quantifying the value premium — the historical outperformance of high book-to-market stocks over low book-to-market stocks. Incorporated into the Fama–French multi-factor frameworks, HML helps explain portfolio returns beyond the market factor, aids performance attribution, and informs strategic tilts between value and growth. Explore More Resources