def estimate(self, nparrays: List[np.recarray]):
"""Compute the book leverage
Parameters
----------
nparrays : List[np.recarray]
Input datasets, same order as specified in `DATASETS_REQUIRED`
Returns
-------
Tuple[pd.DataFrame, Dict[str, str]]
Output dataset and the `VARIABLE_LABELS`
"""
nparray = filter_funda(nparrays[0])
# debts = long-term debt + debt in current liabilities
debts = np.nansum([nparray.dltt, nparray.dlc], axis=0)
# assets = debts + common equity
assets = np.nansum([debts, nparray.ceq], axis=0)
# book leverage = debts / assets
bleverage = np.true_divide(debts, assets, where=(assets != 0))
# set book leverage to missing if common equity is somehow missing
bleverage[np.isnan(nparray.ceq)] = np.nan
# add book leverage to the result
nparray = rfn.rec_append_fields(nparray, NAME, bleverage)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ["gvkey", "datadate"]))
def estimate(self, nparrays: List[np.recarray]):
nparray = filter_funda(nparrays[0])
size = np.log(nparray.at, where=(nparray.at > 0))
size[np.isnan(nparray.at)] = np.nan
nparray = rfn.rec_append_fields(nparray, NAME, size)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ["gvkey", "datadate"]))
def estimate(self, nparrays: List[np.recarray]):
nparray = filter_funda(nparrays[0])
roa = np.true_divide(nparray.ib, nparray.at, where=(nparray.at != 0))
roa[np.isnan(nparray.at)] = np.nan
nparray = rfn.rec_append_fields(nparray, NAME, roa)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ["gvkey", "datadate"]))
def estimate(nparrays: List[np.recarray]):
def filter_funda(x):
return x[np.in1d(x.datafmt, ('STD')) & np.in1d(x.indfmt, ('INDL'))
& np.in1d(x.popsrc, ('D')) & np.in1d(x.consol, ('C'))]
nparray = filter_funda(nparrays[0])
capx = np.true_divide(nparray.capx, nparray.at, where=(nparray.at != 0))
capx[np.isnan(nparray.at)] = np.nan
nparray = rfn.rec_append_fields(nparray, name, capx)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ['gvkey', 'datadate']))
def estimate(self, nparrays: List[np.recarray]):
nparray = filter_funda(nparrays[0])
# market value at fiscal year
mv = nparray.prcc_f * nparray.csho
# market-to-book = market value of equity / common equity
mtb = np.true_divide(mv, nparray.ceq, where=(nparray.ceq != 0))
# set mtb to missing if common equity is somehow missing
mtb[np.isnan(nparray.ceq)] = np.nan
# add book leverage to the result
nparray = rfn.rec_append_fields(nparray, NAME, mtb)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ["gvkey", "datadate"]))
def estimate(nparrays: List[np.recarray]):
def filter_funda(x):
return x[np.in1d(x.datafmt, ('STD')) & np.in1d(x.indfmt, ('INDL'))
& np.in1d(x.popsrc, ('D')) & np.in1d(x.consol, ('C'))]
nparray = filter_funda(nparrays[0])
# debts = long-term debt + debt in current liabilities
debts = np.nansum([nparray.dltt, nparray.dlc], axis=0)
# assets = debts + common equity
assets = np.nansum([debts, nparray.ceq], axis=0)
# book leverage = debts / assets
bleverage = np.true_divide(debts, assets, where=(assets != 0))
# set book leverage to missing if common equity is somehow missing
bleverage[np.isnan(nparray.ceq)] = np.nan
# add book leverage to the result
nparray = rfn.rec_append_fields(nparray, name, bleverage)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ['gvkey', 'datadate']))
def estimate(nparrays: List[np.recarray]):
def filter_funda(x): return x[
np.in1d(x.datafmt, ('STD')) &
np.in1d(x.indfmt, ('INDL')) &
np.in1d(x.popsrc, ('D')) &
np.in1d(x.consol, ('C'))
]
nparray = filter_funda(nparrays[0])
# market value at fiscal year
mv = nparray.prcc_f * nparray.csho
# market-to-book = market value of equity / common equity
mtb = np.true_divide(mv, nparray.ceq, where=(nparray.ceq != 0))
# set mtb to missing if common equity is somehow missing
mtb[np.isnan(nparray.ceq)] = np.nan
# add book leverage to the result
nparray = rfn.rec_append_fields(nparray, name, mtb)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ['gvkey', 'datadate']))
def estimate(self, nparrays: List[np.recarray]):
"""Compute the capital expenditures scaled by total assets
Parameters
----------
nparrays : List[np.recarray]
Input datasets, same order as specified in `DATASETS_REQUIRED`
Returns
-------
Tuple[pd.DataFrame, Dict[str, str]]
Output dataset and the `VARIABLE_LABELS`
"""
nparray = filter_funda(nparrays[0])
capx = np.true_divide(nparray.capx,
nparray.at,
where=(nparray.at != 0))
capx[np.isnan(nparray.at)] = np.nan
nparray = rfn.rec_append_fields(nparray, NAME, capx)
# keep only useful columns
cols = set(rfn.get_names_flat(nparray.dtype))
nparray.sort(order=(keys := ["gvkey", "datadate"]))
