Fama and MacBeth, 'Risk, Return, and Equilibrium: Empirical Tests'.SMB and HML for July of year t to June of t+1 include all NYSE, AMEX, and NASDAQ stocks for which we have market equity data for. Rm-Rf includes all NYSE, AMEX, and NASDAQ firms. Equity-Backed IPOs ), have used the Fama French 3 factor model but do not. Intuitively, if the price of risk and other variables are constant over time, then the two estimates will be the same. See Fama and French, 1993, 'Common Risk Factors in the Returns on Stocks and Bonds,' Journal of Financial Economics, for a complete description of the factor returns. Subtract 1 month average Rf from average 1 month return, repeat until the 36th month. Note that FM estimates per-period prices of risk, and then averages over time, while the cross-sectional regression averages returns over time, and estimates a single price of risk. Where the appropriate test is one which tests if a_i is zero. Therefore the time window is set to 52 weeks.

The dataset is prepared in Excel and contains weekly data from 2011-2017. I want to conduct a 1 year rolling regression with fama-french factors. The factors are related to the market return, firm’s size (market capitalization) and firm’s book-to-market equity (BE/ME) ratio. I was wondering if someone could guide me through the rollapply function in R. Third, run the following cross-section regression. Fama French Model Fama French model is a 3-factor model which is an extension of the Capital Asset Pricing Model (CAPM). Second, compute time-series averages returns Rbar. Fama and French (1996), explore the ability of the FF model to capture dynamic patterns in returns such as the momentum effect of Jegadeesh and Titman (1993). How is FM different? Contrast with what is commonly called the cross-sectional regression approach:įirst, do the same first stage as FM to get beta's. Under assumptions about returns, you can compute test statistics. Third, construct estimates of unconditional lambda's and alpha's by averaging over time. This yields an estimated lambda_t (price of risk) and alpha_t for each time period. Second, for each time period t, run a cross-sectional regression: This yields an estimated betahat_i for each stock.

To add some detail to /u/Gymrat777's explanation, suppose that your asset returns are R_it and your factors are F_t.įirst, run the following time-series regression for each stock i:

Fama french regression excel