- 1Beta (market sensitivity) alone doesn't explain stock returns—contradicting the CAPM
- 2Two additional factors explain most of the variation: size (small beats large) and value (cheap beats expensive)
- 3The value premium averaged 1.53% per month during the study period
- 4This paper became the foundation of the multi-factor approach used by most quant firms today
- 5Fama and French later expanded to a five-factor model adding profitability and investment
#The Paper at a Glance
Title: The cross-section of expected stock returns
Authors: Eugene F. Fama and Kenneth R. French
Published: Journal of Finance, 1992
DOI: 10.1111/j.1540-6261.1992.tb04398.x
This may be the most influential finance paper of the last 50 years. Before Fama and French, the Capital Asset Pricing Model (CAPM) was the standard framework for understanding stock returns: a stock's expected return was determined solely by its beta—how sensitive it is to the overall market.
Fama and French showed this was incomplete. Two additional characteristics—company size and book-to-market ratio—explained far more about returns than beta alone.
#What the Paper Found
The Death of Beta (as a Sole Predictor)
The CAPM says: higher beta = higher returns. But Fama and French found no reliable relationship between beta and average returns once you control for size.
This was a bombshell. The CAPM had been the dominant model in finance for nearly 30 years.
Two New Factors
#### Size Effect (SMB: Small Minus Big)
| Portfolio | Average Monthly Return |
|---|---|
| Smallest 10% | 1.64% |
| Size Decile 5 | 1.29% |
| Largest 10% | 0.90% |
| Small - Large Spread | 0.74% per month |
Small companies outperformed large companies by about 0.74% per month—approximately 9% per year.
#### Value Effect (HML: High Minus Low Book-to-Market)
| Portfolio | Average Monthly Return |
|---|---|
| Highest B/M (Cheapest) | 1.83% |
| Middle B/M | 1.30% |
| Lowest B/M (Most Expensive) | 0.64% |
| Value - Growth Spread | 1.53% per month |
Value stocks (high book-to-market ratio) outperformed growth stocks by 1.53% per month—about 18% per year in the sample.
Combined Explanatory Power
When Fama and French combined market, size, and value factors, they could explain the vast majority of cross-sectional variation in stock returns. This three-factor model became the standard benchmark for academic research.
#Why Value and Size Work
Fama and French offered a risk-based explanation: small and value stocks are fundamentally riskier:
Value Stocks Are Distressed
Cheap stocks (high book-to-market) tend to be companies with problems: - Declining earnings - High leverage - Industry headwinds - Uncertain futures
Investors demand higher returns to hold these risky companies. The "value premium" is compensation for bearing this distress risk.
Small Stocks Are Fragile
Small companies are more vulnerable to: - Economic downturns - Competition from larger firms - Financing difficulties - Management quality issues
The "size premium" compensates investors for these added risks.
#The Debate: Risk vs. Behavioral
Not everyone accepted the risk explanation. Behavioral finance researchers argued:
Behavioral View
- Lakonishok, Shleifer & Vishny (1994): Investors extrapolate past growth too far into the future. They overpay for "glamour" stocks and underpay for "value" stocks.
- This is a mispricing story, not a risk story.
The Truth: Probably Both
Three decades of subsequent research suggests both forces contribute: 1. Value stocks ARE genuinely riskier (supporting Fama-French) 2. Investors DO systematically overreact to past growth (supporting behavioral view)
#Legacy: From Three Factors to Five
Fama and French didn't stop at three factors. Their subsequent work expanded the model:
| Year | Model | Factors |
|---|---|---|
| 1993 | Three-Factor | Market + Size + Value |
| 2015 | Five-Factor | + Profitability (RMW) + Investment (CMA) |
The five-factor model added profitability (profitable firms beat unprofitable) and investment (conservative firms beat aggressive), acknowledging the work of Novy-Marx (2013) and others.
#Has Value Worked Recently?
The value factor went through a historically poor stretch from 2017-2020, leading many to question whether it still works. However:
- Arnott et al. (2021) showed that intangible-adjusted value (accounting for R&D and SGA) performed much better
- Value had a strong recovery in 2021-2022
- The premium has been documented in 200+ years of data
This is why our value methodology uses intangible-adjusted book-to-market rather than the raw measure Fama and French originally proposed.
#How This Applies to Our Rankings
The Fama-French three-factor model is the intellectual ancestor of our entire ranking system. Every factor we use—profitability, momentum, value, low volatility, investment, and short interest—builds on the framework they established.
Our value factor (15% weight) directly descends from their HML factor, updated with Arnott et al.'s (2021) intangible adjustments to reflect the modern economy where R&D and brand value matter more than factories and equipment.
#Academic Source
Fama, E. F., & French, K. R. (1992). "The cross-section of expected stock returns." Journal of Finance, 47(2), 427-465.
Last updated: February 1, 2026