famafrench.FamaFrench.getNyseThresholdsAndRet

FamaFrench.getNyseThresholdsAndRet(self, idList, factorsBool, freq, dt_start, dt_end, *args)[source]

Select NYSE stocks used in the construction of breakpoints (ie thresholds) for portfolio sorting. Selection occurs at a given frequency and for a given sample period.

Parameters

  • idList (list, str) – List of factors or list of anomaly portfolio characteristics whose naming convention is consistent w/ earlier described conventions.

  • factorsBool (bool) – Flag for choosing whether to construct Fama-French factors or not. If False, then dim (w/ or w/out a value for retType) must be passed as additional argument(s), otherwise these additional arguments are not passed.

  • freq (str) –

    Observation frequency of the portfolios. Possible choices are:

    • D : daily

    • W : weekly

    • M : monthly

    • Q : quarterly (3-months)

    • A : annual

  • dt_start (datetime.date) – Starting date for the dataset queried or locally retrieved.

  • dt_end (datetime.date) – Ending date for the dataset queried or locally retrieved.

  • dim (list, int, [optional]) – Dimensions for sorting on each element in the list idList. For example, if idList = ['ME', 'BM'] and dim = [5, 5], then the portfolio sorting strategy is characterized by a bivariate quintile sort on both size and book-to-market.

  • retType (str, [optional]) –

    Weighting-scheme for portfolios. Possible choices are:

    • vw : value-weights

    • ew : equal-weights

Returns

  • dfportSort_tableList (pandas.DataFrame, or dict, pandas.DataFrame) –

    Dataset(s) providing one of the following:

    1. Time-series of Fama-French-style factors.

    2. Panel data consisting of portfolios sorted on specific anomaly characteristics w/ the corresponding:

      • portfolio returns

      • number of firms in each portfolio

    Observation frequency is given by freq. Sample period is from dt_start to dt_end. Rows index time periods, columns index the factors or portfolios.

  • dfportSort_characs (pandas.DataFrame, or dict, pandas.DataFrame) – Dataset providing average anomaly characteristics for each portfolio sorted on a specific set anomaly characteristics. The anomaly characteristics used to sort portfolios NEED NOT coincide w/ the average anomaly characteristics calculated for each portfolio. Observation frequency is given by freq. Sample period is from dt_start to dt_end. Rows index time periods, columns index the portfolios.