Home > Factors, Portfolio Construction, R > Factor Attribution 2

I want to continue with Factor Attribution theme that I presented in the Factor Attribution post. I have re-organized the code logic into the following 4 functions:

• factor.rolling.regression – Factor Attribution over given rolling window
• factor.rolling.regression.detail.plot – detail time-series plot and histogram for each factor
• factor.rolling.regression.style.plot – historical style plot for selected 2 factors
• factor.rolling.regression.bt.plot – compare fund’s performance with portfolios implied by Factor Attribution

Let’s first replicate style and performance charts from the Three Factor Rolling Regression Viewer at the mas financial tools web site.

###############################################################################
# Load Systematic Investor Toolbox (SIT)
# http://systematicinvestor.wordpress.com/systematic-investor-toolbox/
###############################################################################
setInternet2(TRUE)
con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))
source(con)
close(con)

#*****************************************************************
#******************************************************************
tickers = 'VISVX'

periodicity = 'months'

data <- new.env()
getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)
for(i in ls(data)) {

period.ends = endpoints(temp, periodicity)
period.ends = period.ends[period.ends > 0]

if(periodicity == 'months') {
# reformat date to match Fama French Data
monthly.dates = as.Date(paste(format(index(temp)[period.ends], '%Y%m'),'01',sep=''), '%Y%m%d')
data[[i]] = make.xts(coredata(temp[period.ends,]), monthly.dates)
} else
data[[i]] = temp[period.ends,]
}
data.fund = data[[tickers]]

#*****************************************************************
# Fama/French factors
#******************************************************************

data <- new.env()
data[[tickers]] = data.fund
data\$factors = factors\$data / 100
bt.prep(data, align='remove.na', dates='1994::')

#*****************************************************************
#******************************************************************
obj = factor.rolling.regression(data, tickers, 36)

#*****************************************************************
# Reports
#******************************************************************
factor.rolling.regression.detail.plot(obj)

factor.rolling.regression.style.plot(obj)

factor.rolling.regression.bt.plot(obj)

Next let’s add the Momentum factor from the Kenneth R French: Data Library and run Factor Attribution one more time.

#*****************************************************************
# Fama/French factors + Momentum
#******************************************************************

factors\$data = merge(factors\$data, factors.extra\$data)

data <- new.env()
data[[tickers]] = data.fund
data\$factors = factors\$data / 100
bt.prep(data, align='remove.na', dates='1994::')

#*****************************************************************
#******************************************************************
obj = factor.rolling.regression(data, tickers, 36)

#*****************************************************************
# Reports
#******************************************************************
factor.rolling.regression.detail.plot(obj)

factor.rolling.regression.style.plot(obj)

factor.rolling.regression.bt.plot(obj)

To visualize style from the Momentum point of view, let’s create a style chart that shows fund’s attribution in the HML / Momentum space.

factor.rolling.regression.style.plot(obj, xfactor='HML', yfactor='Mom')

I designed the Factor Attribution functions to take any user specified factors. This way you can easily run Factor Attribution on any combination of the historical factor returns from the Kenneth R French: Data Library Or use your own historical factor returns data.

To view the complete source code for this example, please have a look at the three.factor.rolling.regression() function in bt.test.r at github.

1. June 28, 2012 at 9:47 am

Thanks for you blog, I always find it very informative. I was wondering how would you use your own factor models. could you give me an example of one factor that is loaded from a saved .txt file in same format as the FF factors are. eg. one factor I use is Emerging Market over US Market i.e. MSCI EM vs S&P 500.

• June 30, 2012 at 3:41 am

John,

Let’s say you store the EEM/SPY factor in the “EEM_SPY.csv” file. For this example, I created some sample weekly data for EEM/SPY factor using following code:

#*****************************************************************
#******************************************************************
tickers = spl('EEM,SPY')

data <- new.env()
getSymbols(tickers, src = 'yahoo', from = '1970-01-01', env = data, auto.assign = T)
bt.prep(data, align='remove.na')

#*****************************************************************
# Create weekly factor
#******************************************************************
prices = data\$prices

periodicity = 'weeks'
period.ends = endpoints(prices, periodicity)
period.ends = period.ends[period.ends > 0]

hist.returns = ROC(prices[period.ends,], type = 'discrete')
hist.returns = na.omit(hist.returns)

#Emerging Market over US Market i.e. MSCI EM vs S&P 500 = EEM - SPY
EEM_SPY = hist.returns\$EEM - hist.returns\$SPY
colnames(EEM_SPY) = 'EEM_SPY'

write.xts(EEM_SPY, 'EEM_SPY.csv')

Now let’s add the EEM/SPY factor to the Fama/French 3 factor model:

#*****************************************************************
#******************************************************************
tickers = 'VISVX'

data <- new.env()
getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)
for(i in ls(data)) {

period.ends = endpoints(temp, periodicity)
period.ends = period.ends[period.ends > 0]

data[[i]] = temp[period.ends,]
}
data.fund = data[[tickers]]

#*****************************************************************
# Fama/French factors
#******************************************************************

factors\$data = merge(factors\$data, factors.extra, join='inner')
data <- new.env()
data[[tickers]] = data.fund
data\$factors = factors\$data / 100
bt.prep(data, align='remove.na')

#*****************************************************************
# Check Correlations, make sure the EEM/SPY factor is NOT highly correlated
#******************************************************************
pairs.panels(coredata(data\$factors))

#*****************************************************************
#******************************************************************
obj = factor.rolling.regression(data, tickers, 36)

#*****************************************************************
# Reports
#******************************************************************
factor.rolling.regression.detail.plot(obj)

factor.rolling.regression.style.plot(obj)

factor.rolling.regression.style.plot(obj, xfactor='HML', yfactor='EEM_SPY')

factor.rolling.regression.bt.plot(obj)

2. July 3, 2012 at 4:05 pm