Anatomy of a Market Beater: Part III
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.
III. Marginal Gains of a Winner/Loser
For each trial, the marginal gains over the 365-day period of each winner and loser were calculated. By “marginal gains” I mean the amount by which a stock exceeded the gains of the given benchmark, in this case, the NASDAQ.
In any given 365-day time period, the group of winners would tend to have a median margin of 22.4% while the losers group tended to have a median margin of -24.7%. This means that in any given 365-day time period, if we were to take all the winners of that period and calculate the marginal gains of each stock in that group, the median of that winners group would fall somewhere between 16.6% and 39.9% (22.4% median), or for the losers, between -35.6% and -13.6% (-24.7% median):
These median margin figures are rather stable: the standard deviation of these median values is 8% for winners and 6% for losers.
While these median figures are fairly reliable with a low standard deviation, the spread of marginal gain values within a given group, whether winner or loser, is still rather large: the median, median absolute deviation (“MAD”) for winners is 39.6% while that of losers is 18.6%:
We do not have the problem of overlapping values as we do with measuring straight gains because a loser by definition cannot have a marginal gain greater than 0%, otherwise, it would technically be beating the NASDAQ, which would then make it a winner and not a loser. But even still, the rift between the very best performing loser, and the very worst-performing winner is still very small: the absolute lowest marginal gain a winner stock ever experienced over 1,000 trials was 0.00165 % while the absolute maximum marginal loss a loser stock ever experienced was -0.00395%:
So, while the median marginal gain figures are more stable than the median gain figures, the spread of marginal gains amongst winners or losers respectively is about as wild as the spread of straight gains amongst winners or losers respectively. Furthermore, the distinguishing of marginal values of winners versus losers is rather redundant because negative values are always losers and positive ones are always winners.
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