top_color = g.vs[list(np.argsort(score)[-5:])]["name"]
node_color.update({k: 'cyan' for k in top_color})
pos = igraph_draw(
g, num=1, center_name=center_name,
node_color=node_color, node_size=node_size, edge_color='r', k=2,
pos=None, font_size=10, figsize=(11,12),
labels={k:v for k,v in zip(g.vs['name'], g.vs['bea'])},
title=f"Production Flows {list(total['year'].unique())}")
plt.show()
# Construct monthly BEA industry returns for the same period of years
codes = Sectoring(sql, f"bea{vintage}", fillna='')
naics = pstat.build_lookup('lpermno', 'naics', fillna=0)
caps, counts, rets = [], [], []
for year in years:
date = bd.endyr(year - 1)
univ = crsp.get_universe(date)
univ['bea'] = codes[naics(univ.index, date)]
univ = univ[univ['bea'].ne('')]
grouped = univ.groupby('bea')
caps.append(grouped['cap'].sum().rename(year))
counts.append(grouped['cap'].count().rename(year))
months = bd.date_range(date, bd.endyr(year), 'endmo')
for rebaldate, end in zip(months[:-1], months[1:]):
r = pd.concat([crsp.get_ret(bd.begmo(end), end),
crsp.get_cap(rebaldate, use_permco=False),
univ['bea']], axis=1, join='inner').dropna()
grp = r.groupby('bea') # industry ret is sum of weighted rets
r['wtdret'] = r['ret'].mul(r['cap'].div(grp['cap'].transform('sum')))
rets.append(grp['wtdret'].sum(min_count=1).rename(end))
plt.savefig(os.path.join(imgdir, f'{sent}.jpg'))
plt.show()
# Quintile cap-weighted spread portfolio weights: return Correlation
"""
- same year filings [yr]0101:[yr]1231 = bd.begyr(caldate) to caldate
- lagged [yr+1]0401:[yr+2]0331 = bd.begmo(caldate,4) - bd.endmo(caldate,15)
"""
for ifig, key in enumerate(['mdasent', 'mdachg', 'mdacos']):
ret1 = {} # to collect year-ahead spread returns
ret0 = {} # to collect current-year spread returns
for year in sorted(np.unique(data['year'])): # loop over years
# compute current year spread returns
beg = bd.begyr(year)
end = bd.endyr(year)
univ = data[data['year'] == year]\
.dropna(subset=[key])\
.set_index('permno')\
.join(crsp.get_cap(bd.offset(beg, -1)), how='inner')\
.join(crsp.get_ret(beg, end, delist=True), how='left')
if len(univ):
sub = fractiles(univ[key], [20, 80])
pos = weighted_average(univ.loc[sub == 1, ['cap', 'ret']],
'cap')['ret']
neg = weighted_average(univ.loc[sub == 3, ['cap', 'ret']],
'cap')['ret']
ret0[end] = {
'ret': pos - neg,
'npos': sum(sub == 1),
'nneg': sum(sub == 3)