It is believed that an investor is his own biggest enemy while investing. Our brains have evolved in such a way that it makes investing in a rational manner very difficult indeed. Rather than buying low and selling high, we mostly end up doing the exact opposite. Little wonder, the famous investor Warren Buffett has quoted that investing may be simple but it is certainly not easy.
One way out of this misery of constantly falling prey to our wrongly wired brains is to take emotions out of the equation. In other words, developing a system where subjective inputs are completely done away with. Instead, they are replaced with completely mechanical processes.
While we are not exactly sure but we think the discipline of technical analysis falls under this category. But as our long time readers know, we are all about fundamental analysis. And hence, we will stick to this routine of ours.
The question that now remains answering is whether a completely mechanical system, devoid of any subjective inputs, can be developed in fundamental analysis or not? The answer is yes, it certainly can be done.
One of such systems or strategy, whatever you may want to call it, is known as the 'Dogs of the Dow' strategy. Wikipedia informs us that 'The Dogs of the Dow' is an investment strategy that was popularised by Michael Higgins in 1991.
What is this strategy? Well, it is a portfolio construction strategy that involves buying those 10 stocks from the index that have the highest dividend yields. You may wonder if the strategy is all about buying highest dividend yield stocks, why are they derogatorily called as 'Dogs'. Well, it is because a high dividend yield more often than not corresponds to beaten down prices. Therefore, they are called 'Dogs' due to the fact that their prices are trading at record lows.
Coming back to the strategy, all that one has to do is buy 10 highest dividend yield stocks from the benchmark index by investing equal sums in them and then hold it for a year. At the end of the year, one is supposed to sell all the stocks and repeat the process, which is to buy highest dividend yield stocks all over again. This process is then repeated year after year.
So, how has been the performance of this strategy? Well, not bad we should say. As per one study, this strategy has given returns that are neck and neck with the benchmark index (Dow Jones Industrial Average) for the last 5, 10 and 20 year period. But for the 62 year period between 1930 and 2001, this strategy has beaten the benchmark a whopping 43 times. This amounts to a success rate of close to 70%.
What could be the result if such a strategy is tried on the Sensex stocks? Let us call the 10 stocks with the highest dividend yields on the Sensex as 'The Sensex Sufferers'. So, how have Sensex sufferers performed over the recent past? Not bad we believe.
As the table below shows, a portfolio of 10 stocks with the greatest dividend yields would have helped you beat the index in four of the last five years. It was only during FY07 that the portfolio performed worse than the Sensex.
Period | Returns from 'Sensex Sufferers' | Sensex returns |
FY07 | -2.0% | 16.0% |
FY08 | 33.0% | 20.0% |
FY09 | -21.0% | -38.0% |
FY10 | 140.0% | 81.0% |
FY11 | 23.0% | 11.0% |
Naturally, one would be inclined to ask what would a portfolio of stocks with the best dividend yields currently look like. As per the most recent data, the Sensex stocks with the best dividends yield are as shown below.
Company name | Current dividend yield (Aug 19, 2011) |
Hero MotoCorp Ltd. | 5.3 |
ONGC | 3.2 |
Tata Motors | 2.8 |
Bajaj Auto | 2.8 |
Infosys | 2.7 |
Tata Steel | 2.6 |
ITC | 2.2 |
NTPC | 2.2 |
Hindustan Unilever | 2.1 |
BHEL | 1.9 |
It should be noted that the date at which one starts investing is of little consequence here. All that one may have to do is reconstruct one's portfolio after a fixed interval of one year. Will the portfolio of the above mentioned stocks beat the Sensex over the next one year? Well, we are not allowed to be subjective and pass judgement, isn't it? Let us wait for the time to tell us.