This post is about sector ETF analysis with Famma French factors.
Which factors are used in this post?
Factor is statiscal method to analyze impact of variable to data. For example, if many investors follow other investors. In this post, I used Famma-French 5 factor + momentum models to decompose sector ETFs. Below is description for factors used in this post. You can download factor data from Kenneth R. French website. 5_Factors
Momentum
1
2
3
4
5
6
SMB (Small Minus Big) : the average return on the nine small stock portfolios minus the average return on the nine big stock portfolios
HML (High Minus Low) : the average return on the two value portfolios minus the average return on the two growth portfolios
RMW (Robust Minus Weak) : the average return on the two robust operating profitability portfolios minus the average return on the two weak operating profitability portfolios,
CMA (Conservative Minus Aggressive) : the average return on the two conservative investment portfolios minus the average return on the two aggressive investment portfolios,
Rm-Rf (Excess return on the market) : value-weight return of all CRSP firms that return data for t minus the one-month Treasury bill rate
Mom (Momentum) : the average return on the two high prior return portfolios minus the average return on the two low prior return portfolios
Sector ETF used in this post
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
IYW : iShares U.S. Technology ETF (2000-05-15)
IYF : iShares U.S. Financials ETF (2000-05-22)
IYZ : iShares U.S. Telecommunications ETF (2000-05-22)
IYH : iShares U.S. Healthcare ETF (2000-06-12)
IYE : iShares U.S. Energy ETF (2000-06-12)
IYK : iShares U.S. Consumer Staples ETF (2000-06-12)
IYG : iShares U.S. Financial Services ETF (2000-06-12)
IYJ : iShares U.S. Industrials ETF (2000-06-12)
IDU : iShares U.S. Utilities ETF (2000-06-12)
IYM : iShares U.S. Basic Materials ETF (2000-06-12)
IYC : iShares U.S. Consumer Discretionary ETF (2000-06-12)
IBB : iShares Biotechnology ETF (2001-02-05)
IGM : iShares Expanded Tech Sector ETF (2001-03-13)
SOXX : iShares Semiconductor ETF (2001-07-10)
IGV : iShares Expanded Tech-Software Sector ETF (2001-07-10)
IGN : iShares North American Tech-Multimedia Networking ETF (2001-07-10)
IGE : iShares North American Natural Resources ETF (2001-10-22)
IYT : iShares U.S. Transportation ETF (2003-10-06)
IHI : iShares U.S. Medical Devices ETF (2006-05-01)
ITA : iShares U.S. Aerospace & Defense ETF (2006-05-01)
IHF : iShares U.S. Healthcare Providers ETF (2006-05-01)
IEO : iShares U.S. Oil & Gas Exploration & Production ETF (2006-05-01)
ITB : iShares U.S. Home Construction ETF (2006-05-01)
IAT : iShares U.S. Regional Banks ETF (2006-05-01)
IAI : iShares U.S. Broker-Dealers & Securities Exchanges ETF (2006-05-01)
IAK : iShares U.S. Insurance ETF (2006-05-01)
IHE : iShares U.S. Pharmaceuticals ETF (2006-05-01)
IEZ : iShares U.S. Oil Equipment & Services ETF (2006-05-01)
(code) getting data
you can see full code of this module in here code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
class RawDataGetter:
def __init__(self):
self.today = datetime.datetime.today()
self.today_str = self.today.strftime('%Y-%m-%d')
self.START_DATE = pd.Timestamp('2007-01-03')
self.END_DATE = pd.Timestamp('2022-12-01')
self.main_dir = os.getcwd()
self.data_dir = os.path.join(self.main_dir, 'data',self.today_str)
self.universe = [
"IYW", # iShares U.S. Technology ETF|2000-05-15|
"IYF", # iShares U.S. Financials ETF|2000-05-22|
"IYZ", # iShares U.S. Telecommunications ETF|2000-05-22|
"IYH", # iShares U.S. Healthcare ETF|2000-06-12|
"IYE", # iShares U.S. Energy ETF|2000-06-12|
"IYK", # iShares U.S. Consumer Staples ETF|2000-06-12|
"IYG", # iShares U.S. Financial Services ETF|2000-06-12|
"IYJ", # iShares U.S. Industrials ETF|2000-06-12|
"IDU", # iShares U.S. Utilities ETF|2000-06-12|
"IYM", # iShares U.S. Basic Materials ETF|2000-06-12|
"IYC", # iShares U.S. Consumer Discretionary ETF|2000-06-12|
"IBB", # iShares Biotechnology ETF|2001-02-05|
"IGM", # iShares Expanded Tech Sector ETF|2001-03-13|
"SOXX", # iShares Semiconductor ETF|2001-07-10|
"IGV", # iShares Expanded Tech-Software Sector ETF|2001-07-10|
"IGN", # iShares North American Tech-Multimedia Networking ETF|2001-07-10|
"IGE", # iShares North American Natural Resources ETF|2001-10-22|
"IYT", # iShares U.S. Transportation ETF|2003-10-06|
"IHI", # iShares U.S. Medical Devices ETF|2006-05-01|
"ITA", # iShares U.S. Aerospace & Defense ETF|2006-05-01|
"IHF", # iShares U.S. Healthcare Providers ETF|2006-05-01|
"IEO", # iShares U.S. Oil & Gas Exploration & Production ETF|2006-05-01|
"ITB", # iShares U.S. Home Construction ETF|2006-05-01|
"IAT", # iShares U.S. Regional Banks ETF|2006-05-01|
"IAI", # iShares U.S. Broker-Dealers & Securities Exchanges ETF|2006-05-01|
"IAK", # iShares U.S. Insurance ETF|2006-05-01|
"IHE", # iShares U.S. Pharmaceuticals ETF|2006-05-01|
"IEZ" # iShares U.S. Oil Equipment & Services ETF|2006-05-01|
]
def get_market_date(self):
# Get Monthly sector ETF data from yahoo finance
# mkt_data = yf.download(self.universe, start=self.START_DATE,interval="1mo")['Adj Close']
# Get Daily sector ETF data from yahoo finance
mkt_data = yf.download(self.universe, start=self.START_DATE, interval="1d")['Adj Close']
mkt_data.to_csv(os.path.join(self.data_dir,'sector_ETF.csv'))
return mkt_data
def get_fama_french_data(self):
url = \
"http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/" + \
"F-F_Research_Data_5_Factors_2x3_CSV.zip"
file_name = "F-F_Research_Data_5_Factors_2x3.csv"
file = self.get_file_from_url(url, file_name)
df_ff5 = pd.read_csv(file, index_col=0, skiprows=3)
df_ff5 = df_ff5.loc[
self.START_DATE.strftime('%Y%m'):
]
url = \
"https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/" + \
"F-F_Momentum_Factor_CSV.zip"
file_name = "F-F_Momentum_Factor.CSV"
file = self.get_file_from_url(url, file_name)
df_mom = pd.read_csv(file, index_col=0, skiprows=13).iloc[:-1]
df_mom = df_mom.loc[
self.START_DATE.strftime('%Y%m'):
]
# save factor data to csv
save_factor_data = True
if save_factor_data == True and os.path.exists(self.data_dir) == False:
os.mkdir(self.data_dir)
df_ff5.to_csv(os.path.join(self.data_dir, "F-F_Research_Data_5_Factors_2x3.CSV"))
df_mom.to_csv(os.path.join(self.data_dir, "F-F_Momentum_Factor.CSV"))
elif save_factor_data == True and os.path.exists(self.data_dir) == True:
df_ff5.to_csv(os.path.join(self.data_dir, "F-F_Research_Data_5_Factors_2x3.CSV"))
df_mom.to_csv(os.path.join(self.data_dir, "F-F_Momentum_Factor.CSV"))
else:
pass
return df_ff5, df_mom
def get_file_from_url(self, url, file_name):
r = urlopen(url).read()
file = ZipFile(BytesIO(r))
file = file.open(file_name)
return file
def run(self):
self.get_fama_french_data()
self.get_market_date()
Process raw data
you can see full code of this module in here [code] (https://github.com/QuhiQuhihi/Famma-French-Factors-with-Sector-ETF/blob/main/proc_rawdata.py)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
class RawDataProc:
def __init__(self):
self.today = datetime.datetime.today()
self.today_str = self.today.strftime('%Y-%m-%d')
self.START_DATE = pd.Timestamp('2007-01-01')
self.END_DATE = pd.Timestamp(self.today)
self.main_dir = os.getcwd()
self.data_dir = os.path.join(self.main_dir, 'data',self.today_str)
self.ff5_data = pd.read_csv(
os.path.join(self.data_dir, 'F-F_Research_Data_5_Factors_2x3.csv') ,index_col=0
)
self.mom_data = pd.read_csv(
os.path.join(self.data_dir, 'F-F_Momentum_Factor.CSV') ,index_col=0
)
self.sector_etf_data = pd.read_csv(
os.path.join(self.data_dir, 'sector_ETF.csv'), index_col='Date', parse_dates=True
)
def _process_sector_etf_data(self):
mkt_data = self.sector_etf_data
first_date_data = mkt_data.iloc[0:1]
mkt_data_m = mkt_data.resample('M').last()
mkt_data = pd.concat([mkt_data_m,first_date_data])
mkt_data.sort_index(inplace=True)
mkt_data = mkt_data.ffill()
mkt_data = 1+np.log(mkt_data/mkt_data.shift(1))
mkt_data = mkt_data.fillna(1)
mkt_data.index = mkt_data.index.strftime('%Y%m%d')
mkt_data = mkt_data.loc[
self.START_DATE.strftime('%Y%m%d') : self.END_DATE.strftime('%Y%m%d')
]
mkt_data = mkt_data.iloc[1:]
mkt_data.index = pd.to_datetime(mkt_data.index)
mkt_data.index = mkt_data.index.strftime('%Y%m')
mkt_data.to_csv(os.path.join(self.data_dir,'sector_data.csv'))
return mkt_data
def _process_factor_data(self):
ff5_data = self.ff5_data.loc[: ' Annual Factors: January-December ']
mom_data = self.mom_data.loc[:'Annual Factors:']
mom_data.rename(columns = {'Mom ' : 'Mom'}, inplace = True)
factor = pd.concat([ff5_data.iloc[:-1], mom_data.iloc[:-1]], axis=1)
factor.to_csv(os.path.join(self.data_dir,'factor_data.csv'))
return factor
def run(self):
sector_data = self._process_sector_etf_data()
factor_data = self._process_factor_data()
return sector_data,
(code) build factor model
you can see full code of this module in here code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
class FactorDecompose:
def __init__(self):
self.today = datetime.datetime.today()
self.today_str = self.today.strftime('%Y-%m-%d')
self.main_dir = os.getcwd()
self.data_dir = os.path.join(self.main_dir, 'data',self.today_str)
self.factors = pd.read_csv(os.path.join(self.data_dir, 'factor_data.csv'), index_col=0)
self.sectors = pd.read_csv(os.path.join(self.data_dir, 'sector_data.csv'), index_col=0)
self.years = ['2007','2008','2009','2010','2011','2011','2012','2013','2014','2015','2016','2017','2018','2019','2020','2021','2022']
self.months = ['01','02','03','04','05','06','07','08','09','10','11','12']
self.factor_list = ['Mkt-RF', 'SMB','HML','RMW','CMA','Mom']
def factor_ols(self, year, ticker, factors_list):
# select columns for factor data
rf_data = self.factors[['RF']]
factor_data = self.factors[['Mkt-RF', 'SMB','HML','RMW','CMA','Mom']]
sector_data = self.sectors[ticker] - self.factors['RF'] -1
if year != '2022':
date_index = []
for month in self.months:
date_index.append(int(year+month))
else:
date_index = []
months = ['01','02','03','04','05','06','07','08','09','10']
for month in months:
date_index.append(int(year+month))
rf_data = rf_data.loc[date_index]
factor_data = factor_data.loc[date_index]
sector_data = sector_data.loc[date_index]
X = factor_data
y = sector_data
X = sm.add_constant(X)
ff_model = sm.OLS(y, X).fit()
# print(ff_model.summary())
intercept, b1, b2, b3, b4, b5, b6 = ff_model.params
rf = rf_data['RF'].mean()
market_premium = factor_data['Mkt-RF'].mean()
size_premium = factor_data['SMB'].mean()
value_premium = factor_data['HML'].mean()
robust_premium = factor_data['RMW'].mean()
safety_premium = factor_data['CMA'].mean()
momentum_premium = factor_data['Mom'].mean()
expected_monthly_return = rf + b1*market_premium + b2*size_premium + b3*value_premium \
+ b4*robust_premium + b5*safety_premium + b6*momentum_premium
print("Ticker is {}".format(ticker))
print("Expected monthly Return = {}".format(expected_monthly_return))
print("Expected Annual Return = {}".format(expected_monthly_return*12))
return intercept, b1, b2, b3, b4, b5, b6
def run(self):
tickers = self.sectors.columns
factors_list = self.factor_list
df_column = ['id','ticker','year','Mkt-RF', 'SMB','HML','RMW','CMA','Mom']
factor_results = pd.DataFrame(columns=df_column)
factor_results.set_index('id', inplace=True)
i = 0
for year in self.years:
for ticker in tickers:
factor_append = pd.DataFrame(columns=df_column)
factor_append.set_index('id', inplace=True)
intercept, b1, b2, b3, b4, b5, b6 = self.factor_ols(year, ticker, factors_list)
factor_append.loc[i,'ticker'] = ticker
factor_append.loc[i,'year'] = year
factor_append.loc[i,'Mkt-RF'] = b1
factor_append.loc[i,'SMB'] = b2
factor_append.loc[i,'HML'] = b3
factor_append.loc[i,'RMW'] = b4
factor_append.loc[i,'CMA'] = b5
factor_append.loc[i,'Mom'] = b6
print(factor_append)
factor_results = pd.concat([factor_results,factor_append], axis=0)
i=i+1
return factor_results