finlab.tools

finlab.tools.event_study

create_factor_data

create_factor_data(factor, adj_close, days=[5, 10, 20, 60], event=None)

create factor data, which contains future return

PARAMETER	DESCRIPTION
`factor`	factor data where index is datetime and columns is asset id TYPE: `DataFrame`
`adj_close`	adj close where index is datetime and columns is asset id TYPE: `DataFrame`
`days`	future return considered TYPE: `List[int]` DEFAULT: `[5, 10, 20, 60]`

Return

Analytic plots and tables

Warning

This function is not identical to finlab.ml.alphalens.create_factor_data

Examples:

現金增減資分析

from finlab.tools.event_study import create_factor_data
from finlab.tools.event_study import event_study

factor = data.get('price_earning_ratio:股價淨值比')
adj_close = data.get('etl:adj_close')
benchmark = data.get('benchmark_return:發行量加權股價報酬指數')

# create event dataframe
dividend_info = data.get('dividend_announcement')
v = dividend_info[['stock_id', '除權交易日']].set_index(['stock_id', '除權交易日'])
v['value'] = 1
event = v[~v.index.duplicated()].reset_index().drop_duplicates(
    subset=['stock_id', '除權交易日']
).pivot(index='除權交易日', columns='stock_id', values='value').notna()

# calculate factor_data
factor_data = create_factor_data({'pb':factor}, adj_close, event=event)

r = event_study(factor_data, benchmark, adj_close)

plt.bar(r.columns, r.mean().values)
plt.plot(r.columns, r.mean().cumsum().values)

event_study

event_study(factor_data, benchmark_adj_close, stock_adj_close, sample_period=(-45, -20), estimation_period=(-5, 20), plot=True)

Run event study and returns the abnormal returns of each stock on each day.

PARAMETER	DESCRIPTION
`factor_data`	factor data where index is datetime and columns is asset id TYPE: `DataFrame`
`benchmark_adj_close`	benchmark for CAPM TYPE: `DataFrame`
`stock_adj_close`	stock price for CAPM TYPE: `DataFrame`
`sample_period`	period for fitting CAPM TYPE: `(int, int)` DEFAULT: `(-45, -20)`
`estimation_period`	period for calculating alpha (abnormal return) TYPE: `(int, int)` DEFAULT: `(-5, 20)`
`plot`	plot the result TYPE: `bool` DEFAULT: `True`

Return

Abnormal returns of each stock on each day.

Examples:

現金增減資分析

from finlab.tools.event_study import create_factor_data
from finlab.tools.event_study import event_study

factor = data.get('price_earning_ratio:股價淨值比')
adj_close = data.get('etl:adj_close')
benchmark = data.get('benchmark_return:發行量加權股價報酬指數')

# create event dataframe
dividend_info = data.get('dividend_announcement')
v = dividend_info[['stock_id', '除權交易日']].set_index(['stock_id', '除權交易日'])
v['value'] = 1
event = v[~v.index.duplicated()].reset_index().drop_duplicates(
    subset=['stock_id', '除權交易日']
).pivot(index='除權交易日', columns='stock_id', values='value').notna()

# calculate factor_data
factor_data = create_factor_data({'pb':factor}, adj_close, event=event)

r = event_study(factor_data, benchmark, adj_close)

plt.bar(r.columns, r.mean().values)
plt.plot(r.columns, r.mean().cumsum().values)

plot_event_study

plot_event_study(returns)

Plot the event study for the given returns.

PARAMETER	DESCRIPTION
`returns`	A DataFrame containing the returns data. TYPE: `DataFrame`

Return

ax (matplotlib.axes.Axes): The axes object containing the plot.