White Paper - "Trading Seasonality" - Seasonality Heat
We have discussed seasonal cycles, but the fact of the matter is that they are simple guidelines to past behavior during specific periods. Seasonal cycles are composites of past price action, and as such they "hide" measures of seasonal consistency throughout the price action's history. We can adjust the historical look back period to optimize for Cycle "Rtm" as discussed above, but this optimization process tells us more about the validity of the seasonal pattern than it does about the consistency of the seasonal pattern. Our "seasonal heattm " process is designed to address the need to measure seasonal consistency.
At the beginning of each New Year the seasonal cycle is updated with new seasonality zones, incorporating information from the just completed prior year's data. While these seasonality zonestm do not appear precisely at the same time intervals each year, they typically do not deviate greatly from past years. This is at least the case for stocks that have strong seasonal tendencies. The key is to identify those moments in time when seasonality zonestm of prior years most often occur. For any given day of the year, the more frequently seasonality zonestm have occurred in past years, the greater the "seasonal heattm" is (in our charts seasonal heattm is greatest when the background coloring is brightest.) Seasonal heattm can be either positive or negative. Positive seasonal heattm is a greater number (brighter background color above the 50% line in the charts.) Negative seasonal heattm is a greater negative number (brighter background color below the 50% line in the charts.)
Figure 6 Disney with Seasonal Heattm Map
Figure 6 shows the Disney chart with the seasonal heattm map added. Positive seasonal heattm is shown above the 50% line; negative seasonal heattm is shown below the 50% line. Looking at the heat map above the 50% line, the brightest sections correspond to those periods most frequented by strong seasonal zonestm in prior years. This illustrates the consistency of seasonality. The more a seasonal pattern repeats, the more likely it is to occur in the future. This is true for strong and weak seasonal patterns. Moreover, those periods in a seasonal map during which the positive seasonal heattm is absent (darkest) are potentially riskier moments for Disney than other times of the year. This was certainly true in 2005, as Figure 6 demonstrates.
Let's take the concept of seasonal heattm a bit further, and examine all the Dow Jones Industrial Average 30 component issues during 2005. We will confine our strategy to buying these issues only during the strongest seasonal zonestm that have the most positive seasonal heattm. In a case where there are two strong seasonal zonestm with similar extremes in seasonal heat, we will count both as trades. The same goes for short sales. We will confine our short selling strategy to shorting issues only during weak seasonal zonestm that have the most negative seasonal heattm. In the case where there are two weak seasonal zonestm with similar peaks in seasonal heattm, we will count both as trades. Table 2 shows the results.
The results for 2005 are striking. Out of 69 trades, 55 were profitable resulting in a win/loss ratio of 4:1, an average win to average loss ratio of 1.97:1, with average wins equal to 7.05% and average loss equal to -3.57%. The average trade yielded a 4.90% gain. The maximum gain was 22.50%, and the maximum loss was -7.94%. For the year of 2005 as a whole, the Dow Jones Industrial Average itself lost -0.61%.