Fama–French three-factor model
E431728
The Fama–French three-factor model is a widely used asset pricing framework that extends the traditional CAPM by explaining stock returns through market risk, company size, and value factors.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Fama–French three-factor model canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T4329463 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fama–French three-factor model Context triple: [Eugene Fama, knownFor, Fama–French three-factor model]
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A.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
B.
Black–Scholes model
The Black–Scholes model is a fundamental mathematical framework in financial economics for pricing options and other derivatives by modeling asset prices as stochastic processes.
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C.
Markowitz
Markowitz is a locality in what is now Poland that is historically notable as the birthplace of the classical philologist Ulrich von Wilamowitz-Moellendorff.
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D.
Fisher separation theorem
The Fisher separation theorem is a foundational result in financial economics stating that a firm's investment decision can be made independently of its owners' consumption preferences, focusing solely on maximizing the present value of the firm.
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E.
The Theory of Corporate Finance
The Theory of Corporate Finance is a comprehensive textbook by economist Jean Tirole that systematically develops modern corporate finance theory using tools from contract theory and information economics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fama–French three-factor model Target entity description: The Fama–French three-factor model is a widely used asset pricing framework that extends the traditional CAPM by explaining stock returns through market risk, company size, and value factors.
-
A.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
B.
Black–Scholes model
The Black–Scholes model is a fundamental mathematical framework in financial economics for pricing options and other derivatives by modeling asset prices as stochastic processes.
-
C.
Markowitz
Markowitz is a locality in what is now Poland that is historically notable as the birthplace of the classical philologist Ulrich von Wilamowitz-Moellendorff.
-
D.
Fisher separation theorem
The Fisher separation theorem is a foundational result in financial economics stating that a firm's investment decision can be made independently of its owners' consumption preferences, focusing solely on maximizing the present value of the firm.
-
E.
The Theory of Corporate Finance
The Theory of Corporate Finance is a comprehensive textbook by economist Jean Tirole that systematically develops modern corporate finance theory using tools from contract theory and information economics.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
asset pricing model
ⓘ
econometric model ⓘ factor model ⓘ financial model ⓘ |
| addresses |
size effect in stock returns
ⓘ
value effect in stock returns ⓘ |
| alsoKnownAs |
FF3 model
NERFINISHED
ⓘ
Fama–French 3-factor model NERFINISHED ⓘ |
| assumption |
idiosyncratic risk is diversified away in well-diversified portfolios
ⓘ
investors are compensated for bearing systematic risk factors ⓘ |
| calibrationMethod | time-series regression of portfolio returns on factors ⓘ |
| component |
alpha term representing abnormal return
ⓘ
beta coefficients for each factor ⓘ |
| coreIdea | expected stock returns are explained by exposure to three systematic risk factors ⓘ |
| criticizedFor |
limited performance outside U.S. in some studies
ⓘ
not fully explaining momentum in stock returns ⓘ |
| developedBy |
Eugene F. Fama
NERFINISHED
ⓘ
Kenneth R. French NERFINISHED ⓘ |
| empiricalBasis |
observed higher returns for high book-to-market (value) stocks
ⓘ
observed higher returns for small-cap stocks ⓘ |
| equationForm | E(Ri) − Rf = αi + βiM (RM − Rf) + βiSMB SMB + βiHML HML ⓘ |
| explains | cross-section of average stock returns ⓘ |
| extends | Capital Asset Pricing Model NERFINISHED ⓘ |
| factorName |
HML
ⓘ
SMB NERFINISHED ⓘ market factor ⓘ |
| field |
asset pricing
ⓘ
financial economics ⓘ investment management ⓘ |
| HMLDefinition | High Minus Low, return of high book-to-market stocks minus low book-to-market stocks ⓘ |
| includesFactor |
market risk factor
ⓘ
size factor ⓘ value factor ⓘ |
| influenced |
Carhart four-factor model
NERFINISHED
ⓘ
Fama–French five-factor model NERFINISHED ⓘ |
| introducedIn | 1990s ⓘ |
| introducedInPublication | The Cross-Section of Expected Stock Returns NERFINISHED ⓘ |
| marketFactorDefinition | excess return on the market portfolio over the risk-free rate ⓘ |
| marketPortfolioProxy | broad stock market index ⓘ |
| publicationYear | 1992 ⓘ |
| publishedInJournal | Journal of Finance NERFINISHED ⓘ |
| riskFreeRateRole | used as baseline to compute excess returns ⓘ |
| SMBDefinition | Small Minus Big, return of small-cap stocks minus return of large-cap stocks ⓘ |
| typicalHorizon | long-term average returns ⓘ |
| usedFor |
cost of equity estimation
ⓘ
performance evaluation of hedge funds ⓘ performance evaluation of mutual funds ⓘ portfolio construction ⓘ risk attribution ⓘ |
| usesData | portfolio returns sorted by size and book-to-market ratio ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Fama–French three-factor model Description of subject: The Fama–French three-factor model is a widely used asset pricing framework that extends the traditional CAPM by explaining stock returns through market risk, company size, and value factors.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.