Dogs of the Dow Strategy
The Dogs of the Dow is an investment strategy popularized by Michael O’Higgins, in 1991 which proposes that an investor annually select for investment the ten Dow Jones Industrial Average stocks whose dividend is the highest fraction of their price.
Proponents of the Dogs of the Dow strategy argue that blue chip companies do not alter their dividend to reflect trading conditions and, therefore, the dividend is a measure of the average worth of the company; the stock price, in contrast, fluctuates through the business cycle.
This should mean that companies with a high yield, with high dividend relative to price, are near the bottom of their business cycle and are likely to see their stock price increase faster than low yield companies.
Under this model, an investor annually reinvesting in high-yield companies should out-perform the overall market. The logic behind this is that a high dividend yield suggests both that the stock is oversold and that management believes in its company’s prospects and is willing to back that up by paying out a relatively high dividend.
Investors are thereby hoping to benefit from both above average stock price gains as well as a relatively high quarterly dividend. Of course, several assumptions are made in this argument.
The first assumption is that the dividend price reflects the company size rather than the company business model. The second is that companies have a natural, repeating cycle in which good performances are predicted by bad ones.
The Dogs of the Dow were created by data mining. If you look at Dow stocks on January 1 then the high yielding stocks did significantly better than average before 1991 than they did after 1991.
The Motley Fool created the Foolish Four which used the square root of the price and dividend in order to create back tested results better than the Dogs of the Dow. All of these methods were dependent on the fact that the stocks picked on January 1 performed better than stocks picked at other times during the period of back testing.