OK, I'm just kidding... there is no grand battle here between economists (and Nobel prize winners) Eugene Fama and William Sharpe.
That being said, CIPM candidates at the Principles level *are* expected to deal with the following Learning Outcome Statement regarding the work of these economists:
- Compare Sharpe's return-based style analysis and Fama and French's three-factor model
First, it might be helpful to review what style analysis is. Style analysis is a process that is used to try to classify either a portfolio or an individual stock, based on traits (or characteristics) of the given portfolio or stock. It is a process that evolved over some time, and major advances in this area came from separate contributions by William Sharpe and Eugene Fama (along with Kenneth French).
Equity style itself is based on the idea that stocks that share certain traits tend to have similar returns. Style attempts, then, to aggegate stocks at an intermediate level between the broad market (on the macro end of the spectrum) and by industry or other small groupings (at the micro end of the spectrum). If we think of the linear market equation as being a predecessor to style analysis, that equation attempted to describe stock or portfolio performance in terms of a single factor, the market factor (or beta):
Work by researchers in the 1970s found that two factors explained a large portion of stock returns: capitalization (i.e., size) and valuation. The capitalization factor is based on the idea that large cap stocks perform differently than small cap stocks, generally speaking. The valuation factor is based on the idea that stocks that sell for low multiples of earnings or book value based variables tend to perform differently than stocks selling for high multiples of earnings or book value based variables.
In the American stock markets, we typically categorize styles based on size on one dimension (large cap, mid cap, small cap) and value on the other dimension (value, core/neutral, and growth). Combinations of these are also considered styles or sub-styles (e.g., large-cap growth, small cap value, etc). This is a system that was popularized by Morningstar in the 1990s, when they began to classify mutual funds in this fashion.
William Sharpe, in 1988, published work based on his methodology for using regression analysis to explain the performance of any portfolio or stock by doing linear regression to find the appropriate mix (or, equivalently, the allocation) of style indexes. The major contributions of Sharpe with respect to style analysis, as cited in your reading, are:
- All portfolios except style index funds are a mix of styles, and that style is a continuum
- His research allowed plan sponsors to separate manager style bets from the manager’s pure alpha
- His work focused on price/book variables while that of his predecessors' focused on price/earnings variables; this influenced future construction of style benchmarks
where SMB is the size effect (small minus big) and HML is the value effect (high multiples minus low multiples).
So, to summarize, Sharpe and Fama/French have the following similarities:
- both are methods of returns-based style analysis; and , thus, use style as a means of understanding performance
- both used book value based variables rather than earnings values to explain the fundamental value of companies
A couple of points of contrast:
- Sharpe and Fama/French used different factors in their returns-based style analysis approaches. Sharpe explained performance as a weighted scheme of style indexes. Fama/French explained performance in terms of the three factors (beta, size, value) and coefficients assigned to those factors.
- Your reading tells you that Fama/French used book value based variables, whereas it points out specifically that Sharpe used price/book (aka P/B), which was a major change from predecessors and shaped the development of style indexes into the future
P.S.: Yesterday I mentioned that Eugene Fama's work that recently won the Nobel prize in economics can be found here. If you want to read William Shape's Nobel prize winning work, you can find that here.