QuantFin 0.0.1

Library for Academic Research on Asset Pricing

QuantFin

A toolkit for asset pricing. Working... ...

Install

pip install -i https://test.pypi.org/simple/ QuantFin==0.0.1

Get started

Momentum Effects

1. Calculat momentum value on stocks (MOM)
2. Sort stocks on MOM into 10 decile portfolio
3. Report

import numpy as np
import pandas as pd
from QuantFin import univariate_sorting, cal_portfolio_rets

# Read CRSP monthly dataset (Source: WRDS)
crsp = ''
crsp.columns = crsp.columns.str.lower()

"""
Screen the sample

1) The sample includes only stocks that are ordinary common share. E.g., shrcd == 10 or 11.
2) The sample includes only stocks listed on NYSE, AMEX or NASDAQ. E.g., exchcd is 1,2,3,31,32 or 33.
3) The sample includes only stocks with valid share price. E.g., prc > 0.
"""

crsp.query("shrcd==10 or shrcd==11", inplcae=True)
crsp.query("exchcd==1 or exchcd==31 or exchcd==2 or exchcd==32 or exchcd==3 or exchcd==33", inplcae=True)
crsp.query('prc > 0', inplcae=True)
crsp.drop_duplicates(['date', 'permno'], inplcae=True)
crsp.set_index(['date', 'permno'], inplcae=True)
crsp = crsp['ret']

"""
# measure mom effects in an efficient way but ignore the requirement of minimum observations
mom = rollingGeometricReturn(mom, 11)
mom = 100*mom
"""

# measure mom effects
mom = mom + 1
mom = mom.rolling(window=11, min_periods=9).apply(pd.DataFrame.prod)
mom = 100*(mom-1)

# skip the most recent month, mom[-11, -1]
mom = mom.shift(1) 
mom = mom.unstack().rename('mom').reset_index()
mom = mom.merge(crsp, on=['permno', 'date'], how='left')

# sort stocks based on last period's mom, mom(t-1)
mom = mom.sort_values(['permno', 'date'])
mom.loc[:, 'mom(t-1)'] = mom.groupby(['permno'])['mom'].shift(1)
mom = univariate_sorting(mom, on='mom(t-1)', time_label='date', port_label='port', method='smart')

# calculate returns on the formed portfolis
sample = mom.query("'1963-06-30' <= date <= '2021-12-31'")
samp_ret = cal_portfolio_rets(panel_data=sample, ret_label='ret', time_label='date', port_label='port')
#samp_ret = cal_portfolio_rets(panel_data=sample, ret_label='ret', time_label='date', port_label='port', weight_on='marketCap') # calcualte value-weighted returns for portfolios
np.log(samp_ret+1).cumsum().plot(figsize=(16,8), title='Momentum Portfolios Cumulative Returns')

Print summary performance of portfolios, including mean returns and alphas(FF5):

from QuantFin import Performance
samp_ret['10-1'] = samp_ret.loc[:, 10] - samp_ret.loc[:, 1]
print(Performance(samp_ret, models=['FF5']).summary())

Run PanelOLS/Fama-MacBeth regressions and collect results:

from QuantFin.PanelRegs import multiregressions

formulas = {
    "Customised FixedEffects": "rets ~ 1 + mom', fe(permno year)",
    "Filter data": "rets ~ 1 + mom if date >= '2000-01-01', fe(permno year)",
    "Cluster Standard Errors": "rets ~ 1 + mom if date >= '2000-01-01', fe(permno year), cluster(permno)",
    "FamaMacbeth": "rets ~ 1 + mom if date >= '2000-01-01', famamacbeth, robust",
    "logFunct": "mom ~ 1 + log(marketCap) + bm + illiq + turnover, fe(permno date), cluster(permno)",
    "Interaction": "mom ~ 1 + log(marketCap)##bm + illiq +turnover, fe(permno date), cluster(date)" 
}

multiregressions(formulas, data=sample)