Anatomy of a Market Beater: Part I
In this series, I’ll be exploring whether there are any useful characteristics that separate stocks that beat a given benchmark index (“winners”) from those that lose (“losers”).
Suppose the benchmark index you are using is the NASDAQ.
Also, suppose that we are comparing stocks against the NASDAQ over a period of 365 days (the “test period”).
I ran 1,000 randomized trials on existing NYSE and NASDAQ stocks whereby for each trial: (1) a random date was chosen, (2) every existing stock was compared against the NASDAQ index over the period beginning with that random date and ending 365 days later. The stocks for each trial were grouped into either “winners” or “losers” depending on whether a stock’s overall growth over the 365-day period was greater (“winner”) or lesser (“loser”) than the NASDAQ.
The winners and losers were compared by days a stock would be beating or losing against the NASDAQ, the amount of that gain or loss (margin), and the absolute gain or loss that stock was experiencing.
Here’s what I found.
I. Proportion of Days in the Test Period a Winner/Loser Beat the NASDAQ
For each trial, the number of days in the test period that a stock beat the NASDAQ was calculated (let’s call this the “beatpct”).
To illustrate what I mean exactly by “beat”, I would take a normalized price graph of a given stock and compare it to the NASDAQ like so:
the beatpct here would be the number of days PYPL’s line was above NASDAQ’s divided by 365. In this particular example, PYPL would be categorized as a loser here because its overall growth over the 365-day period fell short of NASDAQ’s.
In any given 365-day time period, the group of winners would tend to have a median beatpct of 82.7% while the losers group tend to have a median beatpct of 14.2%. This means that in any given 365-day time period, if we were to take all the winners of that period and calculate the beatpct of each stock in that group, the median beatpct of that winners group would fall somewhere between 75.8% and 87.9% (82.7% median), or for the losers, between 10.4% and 21.5% (14.2% median):
These figures are stable over 1,000 trials as you can see the standard deviation in the median figures is around 3.2%.
However, it is not accurate to say that if a stock was beating the NASDAQ for 14% of the 365-days (or 51 days) that it would be a loser because on the last day it could very well end up higher than the NASDAQ.
And lo and behold if we look at the spread of possible beatpcts within a winner group or a loser group, the spread is quite large:
The median, median absolute deviation of beatpct values amongst winners is about 19.4% while it is about 20.2% for the loser group. These spread figures are fairly stable as the standard deviations of these “MAD” values are 2.07% and 1.31% respectively (the green columns above).
Further obscuring the picture is the fact that there is a lot of potential overlap between the beatpct of the very worst performing winner and that of the very best performing loser:
Over 1,000 trials, the stock with the lowest beatpct of each winner’s group ranged from a low of 0.27% (or approximately 1 day out of the 365 days) to a high of 10.4% (0.82% median), meaning that, at best, the worst-performing winning stock had a normalized price graph that was above NASDAQ’s for about 37 days, and at worst, 1 day.
For losers, the stock with the highest beatpct of each loser’s group ranged from a low of 90.1% (or approximately 329 days out of the year) to a high of 99.45% (99.17% median), meaning that the very best loser beatpct was being better than NASDAQ for every day of the year except for the last day, when it finally dipped below the NASDAQ, thereby categorizing it as a loser.
What all this seems to suggest is that beatpct alone cannot tell you whether a stock has beaten the NASDAQ on a random given 365-day period. It only can tell you that a group of winner stocks would collectively exhibit a better beatpct than a group of loser stocks. Once you break that group apart, the beatpct becomes rather useless.
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