1 / 15

Fama -French 3-Factor Model: Theoretical and Conceptual Underpinnings

Fama -French 3-Factor Model: Theoretical and Conceptual Underpinnings. Richard A. Michelfelder, Ph.D. Rutgers University School of Business – Camden April 17, 2009. Disclaimer.

Thomas
Télécharger la présentation

Fama -French 3-Factor Model: Theoretical and Conceptual Underpinnings

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fama-French 3-Factor Model: Theoretical and Conceptual Underpinnings Richard A. Michelfelder, Ph.D. Rutgers University School of Business – Camden April 17, 2009

  2. Disclaimer The views and methods reflected herein are those of the presenter and may not be consistent with those of Rutgers University, AUS Consultants, or any other affiliations of the presenter.

  3. Today’s Discussion 1. Introduction 2. Evolution from the Capital Asset Pricing Model 3. The Fama-French 3 Factor Model 4. A 4th Factor? 5. The Data 6. Estimation of the Factor Betas 7. Pros and Cons 8. Conclusion References

  4. 1. Introduction • More evidence the better: estimating cost of common equity • DCF, CAPM, Risk Premium are the workhorses • Wide range of estimates & a lot of uncertainty • Empirical & theoretical issues with all methods • Methods exist that are barely being used or not at all: • Fama-French 3 Factor Model (today’s discussion) • COMING SOON!: Consumption Asset Pricing Model (maybe next year’s discussion; completed research project with AUS Consultants)

  5. 2.Evolution from the CAPM • CAPM: • Max return & min risk by diversification • Diversified-market portfolio return to explain stock return • Other factors that explain returns? • Fama-French (1992, 1993): • Small size & financial distress (high book/market) stocks have return premiums due to added non-diversifiable risk • Combine these with market return to explain stock return • Not proxies for unidentified “state” variables • R-Squares are higher (10→50% rather than 1→5%)

  6. 3. The Fama-French 3 Factor Model Fama-French 3 Factors: RPM: difference between returns on a diversified market portfolio (value-weighted CRSP returns) and a risk-free return SMB (small minus big): difference between returns on diversified portfolios of small and large capitalization stocks HML (high minus low): difference between returns on diversified portfolios of high (distressed firms) and low B/M (not – distressed firms) stocks

  7. 3. The Fama-French 3 Factor The model in estimation form is: Ri,t is the utility stock return during period t Rf,t if the return on the risk-free asset α should be zero β’s are the “betas” for each factor βi,mis the CAPM “beta” εi,tis the regression error term

  8. 3. The Fama-French 3 Factor Model For application the model is (illustration purposes only!): or RFPL is an initial estimate of the FPL cost of common equity Rf,t is the return on the risk-free asset β’s are FPL’s factor “betas”

  9. 4. A 4th Factor? 4th Factor (developed by Carhart (1997)): MOM (momentum): difference between returns on diversified portfolios of stocks that perform well and poorly in the short-term (less than one year) • Momentum is short-lived and therefore not useful to estimate the cost of capital although it does explain stock returns • For cost of common equity capital estimates let’s forget MOM. • Doesn’t explain utility stock returns anyway, based on our forthcoming research

  10. 5. The Data • Web Site Source: mba.tuck.dartmouth.edu/faculty/ken.french/data_library.html • Data is free and available to the public • Daily, weekly, monthly, quarterly, annual frequencies beginning at 7/1926 `

  11. 5. The Data Factors are highly volatile → volatile estimates of the cost of common equity capital: Descriptive Statistics of 3 Factors 1Based on annual data ranging from 1926 to 2008 2Statistically significant at 1% level

  12. 6. Estimation of Factor Betas: FPL 3 Factor CAPM

  13. 7. Pros and Cons Pros: • Recently developed addition to the toolkit for estimating the cost of common equity capital • Explains a greater proportion of the non-diversifiable volatility of stocks returns relative to CAPM Cons: • Not based on theory, just a “brute force” way to explain more of the volatility in returns • Factors are highly volatile as are cost of capital estimates • Adding any variable to a regression increases R-square

  14. 8. Concluding Remarks • Cannot ignore any additional evidence on cost of common equity capital estimation • Fama-French 3 Factor Model is a newer tool that offers such evidence • Starting to be adopted for utility ratemaking • As we understand better the application issues, benchmark the results over time under different economic & financial states, its adoption will grow • Now we will hear more about application & benchmarking issues for utility ratemaking

  15. References Fama, E. and K. French, 1992. “The Cross-Section of Expected Stock Returns, ” Journal of Finance 32, 427-465 Fama, E. and K. French, 1993. “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics 39, 3-56 Carhart, M. M., 1997. “On Persistence in Mutual Fund Performance,” Journal of Finance 52, 57-82

More Related