More Day of Month Goodies
In my previous post I looked at day-of-month seasonality.
Using a simple walk-forward test to minimize hindsight bias, I showed that trading the days of the month that have been strong historically has consistently led to stronger returns in the future. That’s as true today as it was in 1950.
Recall the graph above from my previous post. Trading the best half of days is shown in red, and the worst half in grey.
In this post I’ll run the same test, but break daily returns down into quartiles (1). The question is: do days in the top 25%, perform better in the future than the second 25%, the second 25% better than the third 25%, and so on…
Remember, we’re “walking this test forward”, so like my previous post, we decide which quartile each day belongs to in any given month based on that day’s average return over the 20 years prior to that month.
Results show that quartile 1 (i.e. the strongest 5 days of the month) goes on to outperform quartile 2, 2 outperforms 3, and 3 outperforms 4, all by a wide margin, lending further credence to the value of day of month seasonality.
The conclusion would be similar had I used other quantiles (tertiles, quintiles, etc)
Lastly, here are the days that the model would have predicted to be strong (quartiles 1 and 2) and weak (quartiles 3 and 4) for October (2).
I’ve highlighted one additional day, 10/24 (FRB announcement), as particularly bullish since, as we’ve talked about on the blog often, Fed days have been consistently bullish events for nearly two decades and it seemed a waste not to include it.
Now that I’m back in the blogging saddle again, I’m considering releasing this calendar monthly, so be on the lookout.
(1) In my previous post I normalized all months to 21 trading days. In this post, I’ve normalized to 20 trading days to make them fit evenly into our quartiles.
(2) In this series of posts I’ve been using average daily return to identify best and worst days. In this calendar (and any future calendars) though I’m using a slightly more sophisticated approach based on the distribution of historical returns. Results will be similar, but (I hope) a bit better.
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