Hassan, et al. (2019)

Firm-level risk and sentiment derived from quarterly earnings conference calls

This module downloads and processes data developed by:

Tarek A. Hassan, Stephan Hollander, Laurence van Lent, Ahmed Tahoun, 2019, “Firm-Level Political Risk: Measurement and Effects,” Quarterly Journal of Economics, 134 (4), pp.2135-2202. https://doi.org/10.1093/qje/qjz021.

The dataset also contains data developed by the papers below:

Tarek A. Hassan, Stephan Hollander, Laurence van Lent, Ahmed Tahoun, “The Global Impact of Brexit Uncertainty,” 2020, NBER Working Paper 26609
Tarek A. Hassan, Stephan Hollander, Laurence van Lent, Markus Schwedeler, Ahmed Tahoun, “Firm-level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1,” 2020, mimeo London Business School. Avilable at SSRN: https://ssrn.com/abstract=3566530

See the authors’ dedicated website for more information on this dataset: https://www.firmlevelrisk.com/

PROVIDER = 'Tarek A. Hassan, Stephan Hollander, Laurence van Lent, Ahmed Tahoun, 2019'
URL = 'https://www.dropbox.com/s/96xo9f1twlu3525/firmquarter_2022q1.csv?raw=1'
HOST_WEBSITE = 'https://www.firmlevelrisk.com/'
FREQ = 'Q'
MIN_YEAR = 2002
MAX_YEAR = 2022
ENTITY_ID_IN_RAW_DSET = 'gvkey'
ENTITY_ID_IN_CLEAN_DSET = 'permno'
TIME_VAR_IN_RAW_DSET = 'date'
TIME_VAR_IN_CLEAN_DSET = f'{FREQ}date'

source

list_all_vars

 list_all_vars (url:str='https://www.dropbox.com/s/96xo9f1twlu3525/firmqua
                rter_2022q1.csv?raw=1', delimiter:str='\t')

alv = list_all_vars()
alv.head()

	name
0	gvkey
1	date
2	PRisk
3	NPRisk
4	Risk

print(list(alv.name))

['gvkey', 'date', 'PRisk', 'NPRisk', 'Risk', 'PSentiment', 'NPSentiment', 'Sentiment', 'PRiskT_economic', 'PRiskT_environment', 'PRiskT_trade', 'PRiskT_institutions', 'PRiskT_health', 'PRiskT_security', 'PRiskT_tax', 'PRiskT_technology', 'company_name', 'hqcountrycode', 'isin', 'cusip', 'ticker', 'date_earningscall', 'Covid_Exposure', 'Covid_Neg_Sentiment', 'Covid_Pos_Sentiment', 'Covid_Net_Sentiment', 'Covid_Risk', 'SARS_Exposure', 'H1N1_Exposure', 'Zika_Exposure', 'Ebola_Exposure', 'Brexit_Exposure', 'Brexit_Neg_Sentiment', 'Brexit_Pos_Sentiment', 'Brexit_Net_Sentiment', 'Brexit_Risk']

source

get_raw_data

 get_raw_data (url:str='https://www.dropbox.com/s/96xo9f1twlu3525/firmquar
               ter_2022q1.csv?raw=1', nrows:int=None, delimiter:str='\t')

Download raw data from url

	Type	Default	Details
url	str	https://www.dropbox.com/s/96xo9f1twlu3525/firmquarter_2022q1.csv?raw=1
nrows	int	None	How many rows to download. If None, all rows are downloaded
delimiter	str
Returns	pd.DataFrame

raw = get_raw_data(nrows=3)

raw

	gvkey	date	PRisk	NPRisk	Risk	PSentiment	NPSentiment	Sentiment	PRiskT_economic	PRiskT_environment	...	Covid_Risk	SARS_Exposure	H1N1_Exposure	Zika_Exposure	Ebola_Exposure	Brexit_Exposure	Brexit_Neg_Sentiment	Brexit_Pos_Sentiment	Brexit_Net_Sentiment	Brexit_Risk
0	1004	2002q1	359.55072	2928.6014	168.98235	997.86415	5550.5807	469.39542	9001.563	6331.43	...	0	0	0	0	0	NaN	NaN	NaN	NaN	NaN
1	1004	2002q2	0.00000	0.0000	0.00000	1594.73210	-5656.6074	544.82417	0.000	0.00	...	0	0	0	0	0	NaN	NaN	NaN	NaN	NaN

2 rows × 36 columns

source

process_raw_data

 process_raw_data (df:pandas.core.frame.DataFrame=None,
                   gvkey_permno_link:Union[bool,pandas.core.frame.DataFram
                   e]=True)

Converts gvkey to string and applies pandasmore.setup_panel. Adds permno if gvkey_permno_link is not False.

	Type	Default	Details
df	pd.DataFrame	None	Raw data
gvkey_permno_link	bool \| pd.DataFrame	True	Whether to download permno or not. If DataFrame, must contain `permno`, `gvkey`, and `Qdate`
Returns	pd.DataFrame

df = process_raw_data(raw, gvkey_permno_link=False)

df

		date	dtdate	PRisk	NPRisk	Risk	PSentiment	NPSentiment	Sentiment	PRiskT_economic	PRiskT_environment	...	Covid_Risk	SARS_Exposure	H1N1_Exposure	Zika_Exposure	Ebola_Exposure	Brexit_Exposure	Brexit_Neg_Sentiment	Brexit_Pos_Sentiment	Brexit_Net_Sentiment	Brexit_Risk
gvkey	Qdate
001004	2002Q1	2002-03-31	2002-03-31	359.55072	2928.6014	168.98235	997.86415	5550.5807	469.39542	9001.563	6331.43	...	0	0	0	0	0	NaN	NaN	NaN	NaN	NaN
001004	2002Q2	2002-06-30	2002-06-30	0.00000	0.0000	0.00000	1594.73210	-5656.6074	544.82417	0.000	0.00	...	0	0	0	0	0	NaN	NaN	NaN	NaN	NaN

2 rows × 36 columns