Online Stock Trading

Have you ever heard the supposition that higher priced stocks tend to perform better than lower priced stocks? Arguments against this supposition will remind us that stock prices ultimately are irrelevant; that, should you have two identical companies, with one whose stock price is half the other’s (i.e. trading at $10 vs. $20), all this means is that the lower priced company simply has twice as many shares. In other words, it’s diluted. Now that we have a dataset, we can set out to examine this.

Is The Theory That Higher Priced Stocks Perform Better A Myth?

For this experiment, I took the stock prices of all the companies in the dataset on September 9th, and October 6th, and used these to find the one month yield for all 7,478 stocks. I then divided those yields into three equal subsets based on their September 9th prices, each containing about 2,493 stocks (labeled groups A, B and C). Group A consisted of all stocks less than $11.97, group B between $11.98 and $25.25 and group C over $25.26.

The Results

For reference, the dataset as a whole yielded 5.90% over the month. It turns out that both groups A and B performed worse than the market as a whole—in other words, you would have been slightly better off literally picking any of the 7,478 stocks at random than by picking any stock at random that you knew was under $25.25 (keep in mind this is only valid for the market conditions as we saw them in the month of September—I’ll be doing this more, using different time periods to see if something similar happens at other times).

What these findings tell us is already apparent: the more expensive stocks must have performed significantly better in order for the average yield of the whole to have been better than the yields of the two less expensive groups. Indeed, group C averaged 7.78%.

What was even more surprising about the two less expensive groups was that the most frequent yield for both groups was actually slightly negative. Here is the graph of all three groups. It is a frequency distribution showing different small yield ranges, and their corresponding frequencies (you may click on it to get a much larger and clearer picture):

Finally, because the most expensive stocks were still chugging along so strongly even at the very limits of the yield range I selected to graph, I decided to construct a second graph that would associate the yields, not as frequencies, but as a relational graph showing the strict trend between price and performance alone. In order for the graph to be even vaguely useful however, I had to smooth it using a 1000 value run. The way to read it then, is as a “leading” graph. At every price point on the x-axis, the yield at that point represents the average yields of the next forward 1000 stock prices. This graph is very interesting too: after $21.00, the yields just keep on getting better, on average, and after about $27 they are sustained even higher still. Here is the graph:

Lastly, let’s look at how the Dow Jones Industrial Average, the NASDAQ Composite, and the S & P 500 did during this time:

So, if anyone tells you that higher priced stocks don’t perform better with certainty, know yourself, that they just may. There could be many reasons for this phenomenon. Likely it is because better performing companies do not dilute their shares, or their share price. Also, even though the more expensive stocks performed better during this time than both the market as a whole, and all the less expensive stocks, they just barely kept nose to nose with the major indices. But here’s a major point, you could have kept nose to nose with the major indices by doing nothing more than selecting stocks by their prices. You wouldn’t even have to know their names. That’s fascinating. Do I recommend doing that? Hell no. After seeing what we have here, I’m interested in doing a similar experiment on stocks, just as above, but by making them subsets of the indices.

Cheers and happy calculating, musing, and of course, always…thinking