We also introduced \u201csystematic\u201d factors that cannot be diversified or arbitraged away and hence must offer a positive risk premium in the long run.<\/p>\n
Dynamic systematic factors are the cornerstone of Smart Beta strategies (as we will see in Part 5). In this blog, we will discuss some well-known dynamic systematic factors and try to answer the following questions:<\/p>\n
- \n
- Do these factors earn a positive risk premium?<\/li>\n
- More importantly, will the premium continue to persist in the future?<\/li>\n<\/ul>\n
Dynamic Systematic Factors<\/strong><\/h2>\n
Some well-know dynamic factors that we will analyse in this blog are:<\/p>\n
- \n
- Size \u2013 Size factor was first discovered in 1981 [1]. Size factors refers to the phenomena that small-cap stocks tend to do better than large-cap stocks.<\/li>\n
- Value \u2013 This refers to the phenomena that value stocks (lower price-to-earnings or price-to-book ratio) tend to do better than growth stocks.<\/li>\n
- Momentum \u2013 Momentum factor refers to the phenomena that winner stocks continue to win and losers continue to lose. Momentum as a factor was first mentioned in academic literature in 1993 [2].<\/li>\n
- Low-volatility \u2013 More commonly referred to as risk anomaly [3], this is a phenomena where stocks with lower volatility tend to outperform stocks with higher volatility.<\/li>\n<\/ul>\n
While there are many other factors in both academic literature and the industry, we will restrict our focus to the above-mentioned 4 factors.<\/p>\n
Note that we have so far described factors as \u201cphenomena\u201d. But for the purpose of analysis and investment, we must describe these factors as an investment portfolio. There are 2 approaches:<\/p>\n
1.\u00a0\u00a0\u00a0\u00a0Market neutral long-short portfolios<\/strong><\/p>\n
These are factor portfolios that are designed to be market neutral by taking long-short positions. This style was made famous by the seminal Fama and French Model (1993)[4].<\/p>\n
The idea is to create a portfolio that is not impacted by the overall movement of the broader market (market neutral). Hence, the returns are purely driven by the factor.<\/p>\n
An example of such a portfolio is the HML<\/strong> portfolio of Fama and French Model which goes long on top-30% of stocks with highest book-to-price and goes short on bottom-30% book-to-price stocks. Similarly, they have constructed portfolios for size (SMB<\/strong> \u2013 Small minus Big) and momentum (WML<\/strong> \u2013 Winners minus Losers).<\/p>\n
These factor portfolios are \u201cpure\u201d factor portfolios since they extract out any impact of the market returns and are purely driven by the underlying factor dynamics.<\/p>\n
2.\u00a0\u00a0\u00a0\u00a0Long-only portfolio<\/strong><\/p>\n
A lot of investors face long only constraint and hence cannot replicate the market-neutral factor portfolios. Long-only factor portfolio is constructed by just going long on stocks that have higher factor scores. The exact construction will depend on the practitioner.<\/p>\n
A long-only momentum portfolio can be constructed by going long on top-30% of stocks with highest momentum with either equal weights to each stock or market-cap weights. For example, Nifty 200 Momentum 30 Index is a portfolio of top-30 stocks (from the universe of top-200 stocks by market cap) based on a certain momentum score. These stocks are then weighted based on a combination of the momentum score (signal strength) and market capitalization.<\/p>\n
For Nifty 200 Momentum 30 Index, momentum scores (pure returns) are normalized by 1-year realized volatility of individual components. Since, final weights are a combination of momentum score and market-cap, the index also has a positive tilt to the \u201clow-vol\u201d factor and a negative tilt to the \u201csize\u201d factor.<\/em><\/p>\n
We note that long-only factor portfolios are not \u201cpure\u201d factor portfolios because their returns are driven by the underlying factor dynamics as well as the overall movement of the broader market. Buying Nifty 200 Momentum 30 Index gives you exposure to the market risk as well as the factor risk.<\/p>\n
Now let\u2019s analyse these factors using 2 parameters \u2013 historical risk premium and future risk premium.<\/p>\n
Historical Risk Premium<\/strong><\/h2>\n
In this section, we will study the historical risk premium of factors mentioned above. We will first look at the US market and then analyse the Indian market.<\/p>\n
For the US market, we use data from Ken French Data Library<\/a>. Note that this gives us access to market-neutral factor portfolios (refer to previous section) and hence we can extract pure factor premiums.<\/p>\n
For India, we do not have access to pure factor portfolios but we do have long-only indices. We rely on CAPM regression to analyse the premium.<\/p>\n
Factor Premium in the US Market<\/strong><\/h2>\n
Here we analyse last 50 years of data (from January 1972) and look at the following factors:<\/p>\n
- \n
- Market minus risk free rate (Mkt \u2013 Rf<\/strong>) \u2013 The Market Factor<\/li>\n
- Small minus Big (SMB<\/strong>) \u2013 Size Factor<\/li>\n
- High minus Low book-to-price (HML<\/strong>) \u2013 Value Factor<\/li>\n
- Winners minus Losers (WML<\/strong>) \u2013 Momentum Factor<\/li>\n<\/ul>\n
The table below summarizes the return characteristics of these 4 factors:<\/p>\n
<\/p>\n
As we can see, all these 4 factors have earned a positive risk premium with Market and Momentum leading the pack (albeit with much higher volatility). Size has the lowest premium. Value, although has lower volatility, experienced the worst drawdown of 58.5%.<\/p>\n
Looking at these numbers, one might ask \u2013 why bother with Factors at all? Why not just invest in the market (passive indexing) and simply earn the market risk premium. One can do that and a lot of people actually do it (passive index investors). But it is not optimal.<\/p>\n
The idea is to find factors that exhibit a very low correlation with the market and, at the same time, earn a positive premium. This enables you to earn a risk premium which is over and above the market risk premium (adjusted for your market beta). Finding such systematic factors allows you to optimally allocate factor exposures.<\/p>\n
This is what we discuss next. We show how HML and WML portfolios exhibit negative correlation with the market and deliver a statistically significant alpha. Size on the other hand exhibits positive correlation with the market and the alpha is statistically insignificant.<\/p>\n
- Small minus Big (SMB<\/strong>) \u2013 Size Factor<\/li>\n
- Market minus risk free rate (Mkt \u2013 Rf<\/strong>) \u2013 The Market Factor<\/li>\n