The annual report and annual meeting are a big event every year. I wondered if that might cause some buying, either leading up to it, or afterwards based on things learned.

So I got the meeting dates and pricing data for 1997 through 2022 (26 years worth) and aligned the prices to the meeting date for each year. I adjusted Berkshire's returns against SPY's daily returns to remove market movement, then used a 20 day moving average to further smooth the data. I averaged those 26 time series and was hoping to see some outperformance in the months leading up to the annual meeting. Unfortunately I didn't find anything useful.

I graphed average daily returns for 6 months on both sides of the annual meeting. They stayed within roughly +/- 0.1% the entire time. (There was a small bias in the positive direction which you'd expect because Berkshire has a positive trend.) A daily 0.1% return seemed too small to be of interest but I wasn't sure what my expected error bars should be. So to check it, I generated a bunch of random numbers with a distribution that matched the Berkshire data inputs and ran them through the same program multiple times. The random data generated similar graphs that seemed to reach up to about the same 0.1% daily return.

My conclusion is I found nothing and am not going to pursue it any further. The only glimmer of hope was the actual data had average daily returns of roughly 0.1% that lasted longer (~2 months) than what was typical for the random data. I would look further at that except it is not where I would expect to see such an effect (months 2 and 3 preceding the annual meeting). I would expect it to immediately precede the meeting, and possibly extend past the meeting date. But that's not I saw. So, I've tossed it into my failure pile.

Comments/suggestions about any errors or improvements are welcome. If anyone wants a copy of the data or software, I'm happy to send them, although the software is a quick and dirty one off.