anchor.diff:  from urllib import urlopen
   import pandas.io.data as web
   import statsmodels.api as sm
  -
   url = urlopen("http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_5_Factors_2x3_CSV.zip")
   zipfile = ZipFile(StringIO(url.read()))
   FFdata = pd.read_csv(zipfile.open('F-F_Research_Data_5_Factors_2x3.CSV'), 
  
positive.code: 
  ( Ri - Rf ) = alpha + beta * ( Rm - Rf ).
  
  Gene Fama and Ken French argued convincingly that there are additional risk factors that can better describe the expected excess return, namely a size premium (SMB) and a value premium (HML). Recent research has shown that the expected return can further be improved with additional factors. Cliff Asness eloquently  https://www.aqr.com/cliffs-perspective/our-model-goes-to-six-and-saves-value-from-redundancy-along-the-way[describes them] as follows:
  
  . Premium Factors
  * RM-RF - The return spread between the capitalization weighted stock market and cash.
  
negative.code: print(results.summary())
  
  
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