earnings with recent insider transactions v2

Let's start with something we have have experience with: the earnings endpoints.

• https://api.unusualwhales.com/docs#/operations/PublicApi.EarningsController.afterhours

We will collect tonight's earnings (Thu Dec 12th) for afterhours then thin out the responses a bit for a more concise summary:

Only 5 records, but 3 of those 5 are big companies with potentially far-reaching implications:

• RH is Restoration Hardware: how is the high-end consumer doing?
• AVGO is Broadcom: how is the infrastructure semiconductor and software biz doing?
• COST is Costco: how is the cost-sensitive consumer doing?

Let's use this as an opportunity to pull in some insider trading data and see if any of these names have seen recent transactions. Using the insider buying and selling endpoint:

https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.insider_buy_sell

will give us a json() response like this:

{
  "data": [
    {
      "filing_date": "2023-12-13",
      "purchases": 12,
      "purchases_notional": "14317122.490",
      "sells": 10,
      "sells_notional": "-1291692.4942"
    },
    {
      "filing_date": "2023-12-12",
      "purchases": 78,
      "purchases_notional": "46598915.1911",
      "sells": 211,
      "sells_notional": "-182466466.7165"
    },
    {
      "filing_date": "2023-12-11",
      "purchases": 96,
      "purchases_notional": "431722108.8184",
      "sells": 210,
      "sells_notional": "-1058043617.3548"
    }
  ]
}

So let's also add a ticker column to each (for easier bookkeeping):

OK now that we have our insider transactions for each of these names, let's concatenate the dataframes together and clean up the data a bit:

Hmm... nothing too recent, but notable that it's mostly selling. You know what might actually be interesting here? A view of price and insider buying vs. selling over time side-by-side with the actual price of the underlying -- maybe these insiders are good market timers, let's find out.

A little Unusual Whales "inside baseball", when I just need daily closing prices I actually use the realized volatility endpoint because it comes back with one year of prices as well as the daily implied volatility and realized volatility (which I might end up using anywayy), so let's get started there:

https://api.unusualwhales.com/docs#/operations/PublicApi.TickerController.realized_volatility

Just like with the insider transaction dataframes, let's concatenate the results and clean up the data a bit:

OK nice, now we have all the data we need to build these plots, let's create them in the same order as above:

Starting with RH we can see a lot of red dots -- first item on the "Dig Deeper" list is going to be figuring out how much of this selling is planned out in advance as part of a total compensation plan (it seems like there is a "periodicity" to some clusters of red dots) and how much of it is discretionary. Additional commentary Tue 5/20/2025: the Dec'24 and Jan'25 RH insiders really nailed the top on this name, perhaps they had better insight into the potential impact of tariffs?

Next up is finding our lone wolf buyer. N=1 so it is impossible to put a lot of weight on this, but that single buy was objectively great, coming in very close to $240/share (which is near the bottom of RH's trading range).

AVGO is a very interesting case compared to RH because AVGO has gone more or less "up and to the right" on autopilot over the last two years. Nearly every sale has, with the benefit of hindsight, been poorly-timed, and the max drawdown the Sep'23 buyers felt was a few dollars at most.

COST only did a 2X (compared to AVGO's 3x) over the last two years, but the takeaways are very similar to what we learned from AVGO: thanks to hindsight, every sale has been poorly timed as COST price continually marches up-and-to-the-right.