TL;DR: Yes
TL;DR (but now interested): 3.6%/mo ROI vs S&P500’s 1.6%/mo ROI (2019 – present)
Hello everyone,
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First, disclosure time: I do not trade these congressional stocks (yet) and currently just own AAPL, MSFT, and SPY. That said, I am currently paper trading these congressional stocks with an algorithmic trading bot to get a truer representation of whether this experiment is fully viable or not. Understand that my analysis today is focused on historical data and does not catch all possible economic/environmental factors. Please be careful with what I’m sharing.
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Over the last few months, I have created a series of Python scripts that have been automatically scraping as much congressional trade data as I could find and creating member portfolios from them. However, I have created these portfolios not based on the price the member bought/sold a stock at, but instead the price a retail investor would have paid had they executed this same trade on the disclosure date. (For those unaware, congressional members must disclose trades they, their spouse, or immediate family make within 45 days).
To keep the data as unbiased as possible, I have recorded the trades from every member regardless of who owns the stock (the member, their spouse, or their kid). I’ve also recorded every stock trade analyzed as long as it met the following criterial:
1- Be tradable in the US market
2- Must be a stock or ETF buy/sell, no exchanges or call options
Given that, here’s were some of the interested results I found:
Almost universally, no member lost more than 1-5% of their portfolio. The majority of members I tracked were fairly boring with the typical positions in FAANG-like and other large stocks. Surprisingly, many of the members that I did track didn’t even come close to beating the S&P 500. Out of the 186 members I tracked, only 33 members were beating the S&P 500.
However, what was interesting to see was that within those 33 market beating members, they were trading many smaller companies and relatively unknown stocks that saw massive spikes in value. These were companies such as CryoPort Inc (cyrx), Teladoc Health Inc, (tdoc), IMPINJ Inc (pi), Splunk Inc (splk), and many more. What was really interesting here was that my top-members bought, sold and disclosed these trades just before many collapsed in value. It’s uncanny just how well these members almost universally sold their positions right before a companies stock value was cut in half.
Following these high-risk trades, these members made netted average returns of 80% – 200% over just 2-3 years. Donald Beyer for example had a record gain of 370.6% on CYRX, averaging 10% ROI/month over the lifetime of that position. Keep in kind that my 10% ROI number is what our figurative retail investor would have made had they followed Mr. Beyer’s trades 1:1. Any losses made on other stocks were near-universally made up by these high-risk trades and there’s a strong pattern of consistency in that regard.
Overall, it’s absolutely worth taking the time to follow congressional members and watch their trades. After I completed my analysis and saw the figures / results, I put together a trading bot specifically to keep a list of those top members and actively (paper) trade their stocks. I’ve now transitioned into creating a real trading strategy out of this experiment based on the results.
If you would like to view the data for yourself, here is a link to all data I have collected. https://drive.google.com/file/d/1wuJYD6yiNejMCikpQ8p_zWJq7X2jhE-b/view?usp=sharing
The folders are broken up into member trades (all recorded trades), member positions (members stock portfolio), and member value (overall performance). Additionally, I’ve included the overall performance list at the root of the folder that shows every member’s performance sorted by greatest-to-least so you can see how they have all performed.
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