Peters and Taylor (2016)

Total Q, and intangible capital measures from Peters and Taylor (2016)

from __future__ import annotations
from typing import List

import pandas as pd
import numpy as np

import pandasmore as pdm
from finsets.wrds import wrds_api

PROVIDER = 'Wharton Research Data Services (WRDS)'
URL = 'https://wrds-www.wharton.upenn.edu/pages/get-data/peters-and-taylor-total-q/peters-and-taylor-total-q/'
LIBRARY = 'totalq'
TABLE = 'total_q'
LINK_LIBRARY = 'crsp'
LINK_TABLE = 'ccmxpf_lnkhist'
FREQ = 'A'
MIN_YEAR = 1950
MAX_YEAR = None
ENTITY_ID_IN_RAW_DSET = 'permno'
ENTITY_ID_IN_CLEAN_DSET = 'permno'
TIME_VAR_IN_RAW_DSET = 'datadate'
TIME_VAR_IN_CLEAN_DSET = f'{FREQ}date'

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list_all_vars

 list_all_vars ()

Collects names of all available variables from WRDS f{LIBRARY}.{TABLE} and {LIBRARY}.{COMPANY_TABLE}.

all_vars = list_all_vars()

all_vars

	name	type	wrds_library	wrds_table
0	gvkey	VARCHAR(6)	totalq	total_q
1	datadate	DATE	totalq	total_q
2	fyear	DOUBLE_PRECISION	totalq	total_q
3	k_int_know	DOUBLE_PRECISION	totalq	total_q
4	k_int_org	DOUBLE_PRECISION	totalq	total_q
5	k_int_offbs	DOUBLE_PRECISION	totalq	total_q
6	k_int	DOUBLE_PRECISION	totalq	total_q
7	q_tot	DOUBLE_PRECISION	totalq	total_q

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get_raw_data

 get_raw_data (vars:List[str]='*', required_vars:List[str]=['gvkey',
               'datadate'], nrows:int=None, start_date:str=None,
               end_date:str=None)

Downloads vars from start_date to end_date from WRDS {LIBRARY}.{TABLE} library and adds PERMNO and PERMCO as in CCM

	Type	Default	Details
vars	List[str]	*	Default is to get all available variables
required_vars	List[str]	[‘gvkey’, ‘datadate’]	list of variables that will get downloaded, even if not in vars
nrows	int	None	Number of rows to download. If None, full dataset will be downloaded
start_date	str	None	Start date in MM/DD/YYYY format
end_date	str	None	End date in MM/DD/YYYY format
Returns	pd.DataFrame

raw = get_raw_data(vars='*')
raw.head()

	permno	permco	liid	linkprim	gvkey	datadate	fyear	k_int_know	k_int_org	k_int_offbs	...	q_tot	xrd	xsga	cogs	rdip	at	capx	ppegt	ppent	dp
0	25881.0	23369.0	01	P	001000	1970-12-31	1970.0	0.0	5.328974	5.328974	...	0.817259	NaN	9.420	30.529	NaN	33.450	2.767	14.517	8.876	1.352
1	25881.0	23369.0	01	P	001000	1971-12-31	1971.0	0.0	7.607690	7.607690	...	0.508109	NaN	10.548	33.973	NaN	29.330	1.771	13.269	7.639	1.399
2	25881.0	23369.0	01	P	001000	1971-12-31	1971.0	0.0	7.607690	7.607690	...	0.508109	NaN	NaN	23.380	NaN	NaN	NaN	NaN	NaN	0.868
3	25881.0	23369.0	01	P	001000	1972-12-31	1972.0	0.0	8.550650	8.550650	...	0.516247	NaN	7.551	22.702	NaN	19.907	1.254	11.709	7.013	1.200
4	25881.0	23369.0	01	P	001000	1972-12-31	1972.0	0.0	8.550650	8.550650	...	0.516247	NaN	NaN	21.444	NaN	NaN	NaN	NaN	NaN	0.933

5 rows × 21 columns

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process_raw_data

 process_raw_data (df:pandas.core.frame.DataFrame=None,
                   clean_kwargs:dict={})

Applies pandasmore.setup_panel to df

	Type	Default	Details
df	pd.DataFrame	None	Must contain permno and datadate columns
clean_kwargs	dict	{}	Params to pass to pdm.setup_panel other than panel_ids, time_var, and freq
Returns	pd.DataFrame

df_clean = process_raw_data(raw)

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features

 features (df:pandas.core.frame.DataFrame=None)

ftrs = features(df_clean)

pdm.wins(ftrs[['i2k_tot','i2k_phy','i2k_int']]).describe()

	i2k_tot	i2k_phy	i2k_int
count	175623.000000	175623.000000	203412.000000
mean	0.245097	0.119157	0.122255
std	0.225062	0.164408	0.124075
min	0.000340	0.000000	0.000000
25%	0.115130	0.028412	0.025475
50%	0.191825	0.068188	0.100432
75%	0.294431	0.139660	0.172285
max	1.453925	1.066099	0.686001