About This Dashboard
Understanding our data sources, methodology, and the purpose behind the Fama-French Value Spread Analytics dashboard.
What is This Dashboard?
The Fama-French Value Spread Analytics dashboard is a free, educational resource designed to help investors, researchers, and students understand and track the historical behavior of factor premiums in equity markets. Our primary focus is on the "value spread" - the relative valuation difference between value and growth stocks.
Understanding where current valuations stand relative to history can provide important context for investment decisions and academic research. When value spreads are historically wide (meaning value stocks are unusually cheap relative to growth), history suggests higher expected future returns for value strategies - though past performance never guarantees future results.
Our Mission: To democratize access to factor data and provide clear, accurate visualizations that help users understand the rich history of value investing and factor premiums.
Data Sources
All data on this dashboard is sourced from the Kenneth R. French Data Library, one of the most respected and widely-used sources of factor data in academic finance.
Methodology
Our analytics follow standard academic conventions for calculating and presenting factor data. Here's an overview of our methodology:
Value Spread Calculations
- HML (High Minus Low): The direct value factor return from Fama-French models, representing the spread between high and low book-to-market portfolios.
- B/M Spread: Alternative value spread using univariate book-to-market sorted portfolios (top quintile minus bottom quintile).
- Size-Value Interactions: Spreads within size buckets, such as Small Value minus Small Growth, allowing analysis of value effects across different market cap segments.
Statistical Measures
- Z-Scores: Standardized values showing how many standard deviations the current value is from the historical mean.
- Percentile Ranks: The percentage of historical observations that fall below the current value.
- Rolling Returns: Annualized returns over trailing windows (1, 3, 5, and 10 years) to show performance persistence.
- Distribution Statistics: Mean, median, standard deviation, skewness, and kurtosis of historical factor returns.
Data Updates
Data is refreshed periodically to incorporate the latest releases from the Kenneth R. French Data Library. Factor data is typically updated monthly, with a lag of several weeks for data processing and verification.
Frequently Asked Questions
Value spreads provide context for understanding the relative attractiveness of value vs. growth strategies. Research has shown that starting valuations have some predictive power for subsequent factor returns. When value stocks are unusually cheap, the historical evidence suggests higher expected returns for value strategies going forward.
We update our data periodically to match releases from the Kenneth R. French Data Library. Monthly factor returns are typically available with a 2-4 week lag.
For academic or professional research, we recommend accessing data directly from the Kenneth R. French Data Library to ensure accuracy and compliance with their terms of use. Our dashboard is intended for educational visualization purposes.
While the core three-factor data begins in July 1926, some datasets (like the five-factor model) are only available from July 1963 when additional data on profitability and investment patterns became available.
This dashboard is for educational and research purposes only. It should not be used as the sole basis for investment decisions. Factor investing involves risks, and past factor premiums may not continue in the future. Always consult with qualified financial professionals before making investment decisions.
The original 3-factor model (1993) includes market, size, and value factors. The 5-factor model (2015) adds profitability (RMW) and investment (CMA) factors, which help explain additional patterns in stock returns related to company quality and capital allocation decisions.
Academic Background
The factor models and data we use are based on groundbreaking academic research:
- Fama, E.F. & French, K.R. (1993). "Common risk factors in the returns on stocks and bonds." Journal of Financial Economics, 33(1), 3-56. - The foundational paper introducing the three-factor model.
- Fama, E.F. & French, K.R. (2015). "A five-factor asset pricing model." Journal of Financial Economics, 116(1), 1-22. - Extended the model with profitability and investment factors.
- Fama, E.F. & French, K.R. (1998). "Value versus Growth: The International Evidence." Journal of Finance, 53(6), 1975-1999. - Documented value premiums across global markets.
Eugene Fama was awarded the Nobel Prize in Economic Sciences in 2013 for his empirical analysis of asset prices, research that underpins the factor models used in this dashboard.
Who Operates This Site
FamaFrenchDashboard.com is an independent, free educational resource. It is not affiliated with, endorsed by, or sponsored by Eugene Fama, Kenneth French, Dartmouth College, or the Kenneth R. French Data Library. We build visualizations and educational material on top of the publicly available data that the library publishes.
We are not a registered investment adviser, broker-dealer, or financial planner. The Site does not sell financial products, does not manage money, does not offer personalized investment advice, and has no user accounts. It is an informational analytics and education project supported by on-page advertising.
Questions, corrections, and feedback are welcome. You can reach the site operator directly at contact@famafrenchdashboard.com or through the contact form.
Limitations and Disclaimers
Important Notice: This dashboard is provided for educational and research purposes only. The information presented here does not constitute investment advice, and no investment decisions should be made solely based on this data.
Past Performance: Historical factor returns do not guarantee future performance. Factor premiums can be negative for extended periods, and there is no assurance that historical patterns will continue.
Data Accuracy: While we strive for accuracy, users should verify critical data against original sources. We are not responsible for errors in data presentation or interpretation.
No Warranty: This dashboard is provided "as is" without warranty of any kind. Use at your own risk.