Fama French three factor model has been widely used through out
the world to identify risk and returns characteristics of various investment
strategies. It uses a three factor approach(contrasted against one factor approach of CAPM) to decompose the returns of stocks
into the returns of the market as a whole. These three factors are:
 Market returns(MKT)
 Returns of High B/M stocks over low B/M stocks(HML)
 Returns of small market capitalization stocks over large market capitalization stocks(SMB)
Given these three factors the returns of a stock can
be mathematically written as:
Why bother with Fama French?
 It can be used to measure and control portfolio risk in a more holistic manner as it takes into account two more factors apart from market beta.
 It can be used to identify the investment styles of various fund managers by regressing the returns of their portfolios on factor returns
 As investors think differently during different times, behavior of HML and SMB can be used for regime identification and classification
 The time series properties of these factors can be used to create long short trading ideas to generate alpha
Fama French in Indian context:
The analysis is done using the data from Jan 2006 to Apr 2014. Everyday stocks are sorted based on their market capitalization. Top 200 stocks are picked up for further analysis. This step is done to minimize survivorship bias. Within this list, stocks are once again sorted on their B/M. The difference between the returns of top and bottom quartile of this sorted list is the return of HML index for the day. Similarly sorting based on market capitalization is performed on this list of 200 stocks to generate returns of SMB index for the day. These steps are repeated for all the days to generate the HML and SMB indices. CNX Nifty is used as a proxy for MKT. The following are the performance of these three factors since 2006.
As we can see, these three factors are not independent of each other. Extreme movements in one factor typically correspond with the extreme movements in other factors. An example of this would be the rally of 2009 when high B/M stocks outperformed low B/M stocks. Also during the same time small market cap stocks heavily outperformed large market cap stocks. This means that even a market factor neutral portfolio can be very volatile during sharp moves in markets(as other factor exposure might not be zero).
Correlations matrix(values significant at 95% levels are marked in orange):
MKT

HML

SMB


MKT

0.46

0.40


HML

0.01


SMB

Autocorrelation function(values significant at 95% levels are marked in orange):
Lag 1

Lag 2

Lag 3


MKT

0.05

0.01

0.03

HML

0.15

0.06

0.03

SMB

0.07

0.03

0.06

It is clear that the factors(in particular HML) show significant positive serial correlation and hence, very likely to exhibit momentum characteristics. This means style ignorant short term reversion strategies can suffer during sharp trends in these factors.
This is interesting..especially the auto correlations in these factors.
ReplyDeleteI wonder what is the incremental variance that HML and SMB explain(since they are heavily correlated with MKT)?