Anatomy of a Winner: Part I

In this series, I’ll be exploring whether there are any useful characteristics that separate stocks that I’ll define as “winners” from those that are not (the “losers”).

For this series, we are going to be using a few assumptions:

1. NASDAQ will be the benchmark.

2. The time period over which stocks will be measured will be a period of 365 days (the “test period”).

3. All stocks considered are common shares listed on the NYSE and NASDAQ.

4. A winner is defined as a stock that over the 365-day period (a) beats the NASDAQ in overall cumulative gain, (b) experiences a gain of more than 80%, and (c) has a volatility metric value less than that of the NASDAQ over the same time period.

For those of you who’ve been following my blog, this series will be similar to the “Anatomy of a Market Beater” Series except we are redefining “winner” as the above. Before, “winner” was simply defined as point (a) above.

I ran 1,000 randomized trials whereby for each trial: (1) a random 365-day period was chosen, and (2) all existing stocks in that period were sorted into winners and losers based on the above definitions.

I. Proportion of Days in the Test Period a Winner/Loser Beat the NASDAQ

The following stats pertain to the proportion of days in the 365-day test period where a stock’s normalized price graph was above that of the NASDAQ’s. For an illustration, go to my past article. We will call this proportion the “beatpct”.

In any given 365-day time period, the group of winners would tend to have a median beatpct of 96.9% while the losers group tends to have a median beatpct of 41.4%. 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 47.1% and 100% (96.9% median); for the losers, between 0.54% and 99.4% (41.4% median):

Figure 1. Median Beatpct figures. Stat_min is the minimum median beatpct, stat_max is the maximum median beatpct. Stat_median is the median, median beatpct.

The standard deviation in these median figures is 8.2% for winners but for the losers is wilder at 20.7%. There were many trials where there were no winners which is why the sample size for winners is smaller than 1,000 (610 trials had winners while the remainder had none).

These are figures over all 1,000 trials. But what about the spread of beatpct within a winner group or within a loser group only:

Figure 2. Median Absolute Deviations of beatpcts among winners and losers for any given random test period of 365 days.

The median, median absolute deviation of beatpct values amongst winners is about 1.64% while it is about 30% for the loser group. These spread figures are fairly stable as the standard deviations of these “MAD” values are 4.54% and 3.69% respectively (the green columns above). Since the qualifications for a winner as so strict, this seems to result in stocks that are very similar in beatpcts amongst winners. Conversely, it also results in high variability in beatpct amongst losers because the loser pool is by consequence far greater than the very selective winner pool, so it is not surprising that the loser pool would experience a far greater deviation value than the winner group.

Is there any overlap in beatpct between the very worst performing winner and that of the very best performing loser?

Figure 3.

Yes, there is overlap but this is not surprising. Out of 1,000 trials, the worst-performing winner in terms of beatpct was a stock whose beatpct was 1.3%, so out of 365 days, 1.3% of those days were days where its normalized price graph was above the NASDAQ. But on average, the worst-performing winner of a given trial had a beatpct of 92.3%, with a standard deviation of 19%. The best-performing loser had on average a beatpct of 100% with a standard deviation of 0.22%.

The high requirements of qualifying as a winner explains why there are still very decent performing losers.

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