code: def beta_adj(self, benchmark, adj_factor=2 / 3, **kwargs):
          
  
          beta = self.beta(benchmark=benchmark, **kwargs)
          return adj_factor * beta + (1 - adj_factor)
docs: Adjusted beta.
  
          Beta that is adjusted to reflect the tendency of beta to
          be mean reverting.
          [Source: CFA Institute]
  
          Formula:
          adj_factor * raw_beta + (1 - adj_factor)
  
          Parameters
          ----------
          benchmark : {pd.Series, TSeries, pd.DataFrame, np.ndarray}
              The benchmark securitie(s) to which `self` is compared.
  
          Returns
          -------
          float or np.ndarray
              If `benchmark` is 1d, returns a scalar.
              If `benchmark` is 2d, returns a 1d ndarray.
  
          Reference
          ---------
          .. _Blume, Marshall.  "Betas and Their Regression Tendencies."
              http://www.stat.ucla.edu/~nchristo/statistics417/blume_betas.pdf