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Three Factor Model. David Ernst Leah Langsdorf Nicoleta Tulai. Eugene Fama and Kenneth French. Eugene Fama (1939 – ) Professor of Finance, University of Chicago Father of efficient market hypothesis Kenneth French (1954 – ) Professor of Finance, Dartmouth College

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Three factor model
Three Factor Model

David Ernst

Leah Langsdorf

NicoletaTulai


Eugene fama and kenneth french
Eugene Fama and Kenneth French

  • Eugene Fama (1939 – )

    • Professor of Finance, University of Chicago

    • Father of efficient market hypothesis

  • Kenneth French (1954 – )

    • Professor of Finance, Dartmouth College

    • Known for work with Fama on asset pricing


Capm shortcomings
CAPM - Shortcomings

CAPM uses only one risk factor (βeta) - which limits usefulness of the model.

β only accounts for 70% of returns.

Wanted a way to explain the other 30% of return on equity portfolios.

Equity Portfolio – long term capital appreciation by investing in mutual funds who’s holdings are both domestic and international and are in equity markets.


The test
The TEST

Are Size and Value determining factors effecting returns?


The model
The Model

  • Three Factor Model – uses 3 distinctive risk factors to compare portfolios

  • 3 factors: β, size, and value

    • = rate of return

    • = risk free rate

    • = return of the whole stock market

    • = analogous to classical β but not equal to it

    • = small (market capitalization) minus big

    • = high (book-to-market) minus low

    • = corresponding coefficient

    • = corresponding coefficient


Data

NYSE, AMEX, NASDAQ return files and industrial files of income statement and balance-sheet data.

Obtained from Center for Research in Security Prices (CRSP)

1962-1989


Data

Accounting data used from fiscal year-end of previous year.

Compared to returns of July of current year (t) through June of subsequent year (t + 1).

Book-to-market, leverage and earnings-price ratio, calculated from December of prior year (t - 1) market equity.

Market equity from June of current year (t) used to measure size.


Estimating market betas
Estimating Market Betas

  • All NYSE stocks sorted into 10 groups by size (June)

  • Eligible NYSE, AMEX, NASDAQ stocks allocated to 10 portfolios.

  • Each size group subdivided into 10 portfolios based on pre-ranked betas for each stock.

  • Pre-ranked betas estimated on 24 to 60 month returns in the 5 years before July of current year.


Informal tests
Informal Tests

Calculate equal-weighted monthly returns on portfolios for the next 12 months. (July to June)

Estimate Betas on each of the 100 portfolios.

CRSP value-weighted portfolio of NYSE, AMEX, and NASDAQ stocks used as proxy for market.

Beta estimated as sum of the slopes in the regression of the return on a portfolio on the current and prior month’s market return.




Average Size – ln(Market Equity)


Pre tests
Pre-Tests

  • Pre-tests:

    • Portfolios formed from beta only

    • Portfolios formed from size only

      Size Alone: strong negative relationship between size and average return: strong positive relationship between beta and average return.

      Beta Alone: wider range of betas – no relation between average returns and beta.


Table i results
Table I Results

Portfolios formed on size and pre-ranking betas magnifies the range of post-ranking betas.

Pre-ranking betas captures correct order of post-ranking betas.

Pre-ranking betas produce strong variation in betas that is unrelated to size.


Informal tests1
Informal Tests

Betas of size portfolios are almost perfectly correlated with size.

Unable to separate between beta and size effects.

Subdivision by pre-ranking betas presents a strong relationship between average return and size – and no return between average size and betas.


Informal tests2
Informal Tests

  • Returns sorted in 12 portfolios on size or pre-ranking betas at the end of June.

    • Middle 8 cover deciles of size or beta.

    • Extreme 4 (1A, 1B, 10A, 10B) split the bottom and top deciles in half.

  • Table II shows that when portfolios are formed on size alone, we observe the familiar strong negative relationship between size and average return and a strong positive relationship between average return and beta.




Table ii
Table II

  • Size sort seems to support the SLB asset pricing model prediction of a positive relation between beta and average return.

  • Evidence is muddied by the tight relation between size and the betas of size portfolios.

  • The portfolios formed on pre-ranking betas produce a wider range of betas than the portfolios formed on size.

  • The beta-sorted portfolios do not support the SLB model: no obvious relation between beta and average returns.


Informal test conclusions
Informal Test Conclusions

There is a relationship between size and average return, but there is no relationship between beta and average return.

The next regression confirms this conclusion.


Fama macbeth regressions
Fama-MacBeth Regressions

Regression – (table III)

Shows time-series averages of the slopes from the month-by-month regressions of the cross-section of stock returns on size, β, and the other variables (leverage, E/P, and book-to-market equity) used to explain average returns.

Size helps explain the cross-section of average stock returns

Found the average slope from month regressions of returns on size alone was -.15% and a t-statistic of -2.58. (size effect is robust)

Found that β does not help explain average stock returns



Size and book to market equity
Size and Book-to-Market Equity

There is a strong relationship between average stock returns and size, but no average stock returns and beta.

Book-to-Market and Earning-Price Tables formed using the same method as Table II.



Book to market equity results
Book-to-Market Equity Results

Strong Positive Relationship between Average Return and Book-to-Market Equity.

Betas vary little across the portfolios, indicating that beta has little effect on the results.

The Book-to-Market relation is stronger than the Size effect: 50% average slope; t-stat = 5.71


Leverage variables
Leverage Variables

Ln(A/ME) and Ln(A/BE): logs capture leveraging effects in average returns.

Higher market leverage associated with higher returns.

Higher book leverage associated with lower returns.

Opposite signs – similar absolute value: the difference explains average returns (book-to-market equity).



Earnings price results
Earnings-Price Results

U-shaped relation between average return and E/P.

If current earnings reflect future earnings, high-risk stocks with high expected returns will have low prices relative to their earnings.

Only for firms with positive earnings.

Average returns increase with E/P when positive.


Book to market equity conclusions
Book-to-Market Equity Conclusions

When we allow for variation in beta that is unrelated to size, there is no reliable relation between beta and average return.

The opposite roles market leverage and book leverage in average returns are captured well by book-to-market equity.

The relation between E/P and average return seems to be absorbed by the combination of size and book-to-market equity.


Results
Results

For the 1963-1990 period, size and book-to-market equity capture the cross-sectional variation in average stock returns associated with size, E/P, book-to-market equity and leverage.

Since the Fama-MacBeth intercept is constrained to be the same for all stocks, FM regression always impose a linear factor structure on returns and expected returns.


Results cont
Results (cont.)

Two easily measured variables, size and book-to-market equity, seem to describe the cross-section of average stock returns.

Value stocks have provided much better return than large stocks over time and around the world.


Effect on current markets
Effect on Current Markets

Enables investors to make decisions based on exposure to 3 factors instead of just 1.

Can measure historical fund manager performance to see if they are adding value.

If you rearrange into a regression model you can see if α is positive.



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