Three Fundamental Factors and their Analytical Proxies
The
3 fundamental factors and the financial or analytical proxies that capture
these factors in a quantitative measure:
There are certain firm characteristics,
known as style anomalies, which appear to be proxies for risk. The traditional
Capital Asset Pricing Model does not capture these anomalies. The three
fundamental factors that we deal with are moment, size and value. These
fundamental characteristics could essentially be responsible for a portion of
the shares returns.
The momentum
anomaly says that have outperformed the market over the past 12 months are
likely to continue outperforming in the near future. This is often caused by investors
overreacting in the short run. A proxy used to measure this momentum factor
would be relative price appreciation.
Size is one of the more common fundamental factors. This anomaly says
that the smaller firms tend to outperform the larger firms (inverse
relationship between firm size and stock returns). This is measures by market capitalisation, and was first demonstrated
by Banz (1981). Small companies grow more easily than larger companies, as
relatively they could achieve a much larger growth percentage relative to a
large company, even with much smaller revenues. These small companies are
therefore able to grow faster than the larger companies, and this is reflected
in their stock performance. This higher return, does, however come with higher
risk.
Value
anomaly indicates that under-priced stocks
typically outperform in the long term. This is also knows as the book-to-market effect. This effect
compares the book value of a company to the companies price of its stock
(inverse of price to book ratio). The bigger the book-to-market ratio is, the
more fundamentally cheap the investigated company is.
Pure
value effect portfolios are created as long stocks with highest book-to-market
ratio.
The two primary approaches to investigating
style anomalies are portfolio sort, and regression-based methods.
1.
The portfolio sorting approach
sorts shares monthly into fractiles on the basis of one or more firm
characteristics. The difference between the payoff at the top and bottom
fractiles represents the payoffs to the characteristics in question.
2.
The second approach is to fit a
univariate regression of realised share returns against firm characteristics in
order to establish whether certain characteristics are able to explain or
predict share returns.
Comments
Post a Comment