The Trades That Work Because Nobody Wants to Do Them, Part 2

This whole time, all you needed to do was read a filing.

Quant Galore

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6 days ago

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On September 28, our bot shorted GYGY at $1.23 in the opening auction and bought it back a little after 2 PM at 97 cents, which is about +21% on a stock that had been up 72% before the bell.

Now, we’re cherry-picking that result for a reason.

Back in July, GYGY had sold $8 million of convertible preferred stock to Streeterville Capital, priced at the lesser of a fixed price or a discount to wherever the stock happened to be trading. In August it registered 22.2 million shares for resale, on a company worth about $24 million at the time, and that registration went effective on September 16.

So when GYGY ripped 72% in the pre-market 12 days later, there was someone sitting on a pile of freshly sellable shares who had every reason to hit the bid.

In part 1, we went through why some trades keep working for years after they’re known: the names are too small, the plumbing is annoying, the payout takes forever, and shorting carries a stigma.

However, when you’re in the business of edge, no edge is too small or too much work. So today, we’re going to take it a level deeper and show you some of the grittier mechanics of more of these types of trades.

Not All Money is Good Money

“Toxic financing” might sound like jargon-slop at first, but the reason it’s called that is pretty simple.

Basically, a small company that can’t raise money the normal way signs a deal with a specialty fund, and the deal usually works like this:

1. The fund buys a convertible note, convertible preferred, or an equity line from the company
2. The conversion price floats, which essentially means the fund’s price follows the stock wherever it goes
   — Usually that’s a 5% to 20% discount to the recent market price, or the lesser of a fixed price and that discount
3. The company files a resale registration so the fund can legally sell the shares it gets
4. The fund converts, sells, converts again, and sells again, for as long as there’s a bid.

The fund’s whole business is buying stock at a discount and dumping it into whatever liquidity shows up, and a stock running 72% in the pre-market is the best liquidity a company that size will see all quarter.

So, that shines a bit more light on who the “forced seller” from part 1 might be.

There’s Still Value in The Math

Now, the other half of this is our Pre-Market Drop Risk dataset, where every trading morning a model takes in the microcaps that have been active before the open and ranks them by their chance of falling 5% or more from the open to the close.

Rows land a few minutes after 9 AM ET and the history goes back to May 2024, so here’s what the list has done since then for names trading at $1 or more:

• 3,452 flagged names over 609 trading days, about 5 a morning.
• 65% of them fell from the open to the close.
• The median one fell 5.7%.
• The top 3 names each day fell 70% of the time, for an average short return of +4.7% per trade.

You get all of that without reading a single filing, since the model only sees prices, which also means it has no idea who’s holding a convertible.

That’s where the Toxic Financing dataset comes in, since it tracks the PIPEs (basically a fund buying stock straight from the company) and structured private placements disclosed in 8-Ks since 2024.

Each deal comes with:

• The pricing formula and the discount.
• The investors behind it.
• The resale registration dates.
• A 0-to-3 toxicity grade, where 3 means the price floats with the market.

Pull one out of a hat

I wanted to see how dumb I could make this and still get paid, so I simulated the laziest possible version, where every morning you pick one name at random from the list, short it at the open, and cover at the close.

I ran that across all 609 days, 10,000 separate times, which is basically 10,000 different people each drawing their own name out of a hat every morning for about 2.5 years.

Even with zero filtering, 88% of them finished in profit, averaging +1.7% per trade.

Then I made it one notch less dumb by only drawing from names on the list that also had a deal in the Toxic Financing feed over the prior year, which left 512 days with at least one of those names to draw from.

This time, all 10,000 runs finished in profit, averaging +4.1% per trade (chart below).

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So out of 10,000 people pulling from those 2 lists at random, not a single one lost money. You can literally pick one of these names out of a hat, short it, and make money.

Interestingly enough, the edge keeps growing the tighter you make the filter, so here’s every name on the list sorted by what was sitting in its filings before the morning it got flagged:

• Any name on the list: 65% fell, average short return +2.5% (3,452 trades).
• Any private placement in the past year: 67% fell, +4.8% (1,205 trades).
• A deal graded 2 or 3 in the past 90 days: 72% fell, +6.2% (302 trades).
• A resale registration that went effective in the past 90 days: 72% fell, +7.5% (209 trades). These come from our Stock Dilution feed, which flags registrations of shares existing holders already own.

That last one is GYGY’s exact setup, and it’s the cleanest version of the mechanism, because the financier’s shares just became sellable and then the stock ran.

The names with a financier behind them also beat the rest of the list in every single year of the history (chart below).

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Why it’s still sitting there

So if it’s this easy, why doesn’t everyone do it already?

Mostly because it checks almost every box on part 1’s list:

• Too small. The median opening auction for one of these names trades about $73,000, which a fund running a few billion can’t even see, never mind trade
• Operationally annoying. You need a list by 9 AM, a locate before 9:30, a market-on-open order, and an afternoon cover, every single day.
• Short-side stigma. You’re betting against tiny companies that just announced a “strategic financing”

The fourth friction, the slow payout, is the one that’s gone, since this trade opens at 9:30 and closes the same afternoon, so you know whether you were right before dinner and there’s no overnight borrow to pay.

How to run it yourself

1. At about 9:05 AM ET, pull the day’s list with min_price=1.
2. Pull every Toxic Financing deal from the past year and keep the names that show up in both.
3. Check the borrow in the Short Locates feed, which snapshots quoted locate costs before the open, and skip anything you can’t get.
4. Short what’s left with market-on-open orders, equal dollars per name.
5. Cover everything at 2 PM ET, which is what our bot does.

Steps 1 and 2 are a dozen lines of Python:

import requests, pandas as pd

KEY = "YOUR_ALPHANUME_KEY"
B = "https://api.alphanume.com/v1/"
H = {"X-API-Key": KEY}
today = pd.Timestamp.now(tz="America/New_York")

# This morning's list, $1 and up
picks = pd.DataFrame(requests.get(B + "premarket-drop-risk", headers=H,
    params={"date": today.strftime("%Y-%m-%d"), "min_price": 1}).json()["data"])

# Every toxic financing deal from the past year
deals = pd.DataFrame(requests.get(B + "capital/private-placements", headers=H,
    params={"date_gte": (today - pd.Timedelta(days=365)).strftime("%Y-%m-%d")}).json()["data"])

# Names on the list with a financier behind them
shorts = picks[picks["ticker"].isin(deals["ticker"])]
print(shorts[["ticker", "rank_for_date", "prob_drop", "px_at_trading", "premarket_return_at_cutoff"]])

If you want the stricter version, add min_toxicity=2 to the second call and shrink the window to 90 days.

Keep each name small, since every so often one of these runs a few hundred percent before the close. The worst top-3 day in the history was PLAG in August, up 443% from the open, and the top 3 still finished every year in profit.

Final Thoughts

Part 1 was the argument for why this kind of edge sticks around, and this is the part where you get to go collect it.

What I like about this one is how little discretionary judgment it asks for, since the model finds the names that ran before the open, the filings tell you who’s waiting to sell into them, and the rest is basically a couple of orders and a timer.

If you want to try it, both datasets are in Alphanume Pro, which comes with a 3-day free trial, so you can pull a few mornings (or years) of the list and see it for yourself.

It also makes me wonder how far the same idea stretches, since at-the-market programs and warrant exercises come to mind first, and there’s probably a version of this for anyone with a reason to sell.

As always, thanks for reading, and we’ll see you in the next one.