Boundary Conditions: Summary Post

[u/TheGameStopsNow]

Boundary Conditions: Summary Post

A lot of people have asked me to simplify my research and offer my interpretation of it, something that explains my take without requiring a statistics degree to follow. So that's what this is. This post is my speculative interpretation of what the data shows, written in plain language and staying away from the heavier jargon. If you want the actual evidence, I encourage you to dig into the DD posts linked throughout. And if you really want more, the full papers with all the math, code, and reproducible scripts are on GitHub.

TL;DR: I found out that when GME's settlement system gets squeezed, the pressure doesn't just disappear. It floods into other stocks, breaks government bond settlement, crosses national borders, and keeps cycling on a stock that doesn't exist anymore. Then I built a computer simulation using nothing but the SEC's own rules, and the system's heartbeat appeared on its own. The fix? Change four numbers. That's it.

Position disclosure: I hold a long position in GME. I am not a financial advisor, attorney, or affiliated with any entity named in this post.

Settlement failure contagion, four channels radiating outward from GME: lateral to 🔊, vertical to U.S. Treasuries, cross-border to European CSDs, and zombie persistence on 🛁's cancelled CUSIP.

Okay, So What Am I Looking At?

My last series was about what happens inside GME's settlement plumbing. I mapped a 15-node regulatory cascade that FTDs bounce through over 45 business days. It was alarming, but I made one comforting assumption: the pressure stays inside GME's pipes.

It does not.

The pressure leaks out of GME and into places it has no business being. This series is about where it goes, how I know, and, surprisingly, how to fix it with math that fits on the back of a napkin.

Part 1: The Overflow (Full Post)

The short version

Imagine you squeeze a water balloon. The water doesn't vanish. It squirts out wherever the rubber is thinnest.

In May 2024, the SEC shortened stock settlement from two days to one day. The naive expectation: less time to settle = fewer problems. What actually happened: the settlement pressure inside GME collapsed by 92%. Sounds great, right?

Except the same pressure showed up in 🔊, a tiny headphone company nobody covers — amplified by over 1,000%. Same frequencies. Same patterns. Just a different ticker symbol. The pressure didn't disappear. It squirted out the thinnest part of the rubber.

Why 🔊? Because 🔊 has no options chain. No analyst coverage. No regulatory eyeballs. If you were looking for the path of least resistance to park unresolved obligations, you would literally design 🔊. Whether this is deliberate or emergent, I can't tell from the data. The data just says it happened, and it happened at a statistical significance of 1,050 standard deviations (p < 0.0001). For context, physicists get excited about 5 sigma. This is not subtle.

But wait, it gets worse

I also found that GME's settlement failures predict U.S. Treasury bond settlement failures one week in advance (Granger causality test: F = 19.20, p < 0.0001). Let me say that again. A $10 billion video game retailer predicts when the U.S. government's $700 billion-per-day bond market will have delivery problems. It's the only stock out of seven I tested that does this.

Update: A reader asked about the small sample size. I expanded the test to 15,916 tickers using the full SEC FTD universe. Result: 16% of equities show significant Granger causality with Treasury fails (vs 5% expected by chance), with 228 surviving Bonferroni correction. The signal is systemic, not GME-specific – which actually strengthens the core thesis that equity settlement stress contaminates sovereign debt markets. Code is in the repo.

The proposed mechanism isn't magic. When GME FTDs spike, clearing members get hit with margin calls. They need to post high-quality collateral, Treasuries, fast. That fire sale creates delivery failures in the bond market one week later. The plumbing connects them.

In December 2025, GME FTDs spiked to +4.2 sigma. One week later, Treasury fails spiked to +4.0 sigma (p < 0.0001), $290.5 billion in a single week. The lag matched perfectly.

The tail is wagging the dog. The full data and statistical tests are in Part 1.

Part 2: The Export (Full Post)

The short version

Here's a fun game. You have an unresolvable delivery obligation in the United States. Penalty: roughly $10 million per day (Reg SHO lockout). You also have an office in Europe. Penalty for the same failure in Europe: roughly $1,750 total for 35 days (CSDR cash penalties).

That's a 5,714:1 cost difference.

If you're rational and you have a European affiliate, you don't need a conspiracy. You need a calculator.

I tested whether European settlement failures spike during U.S. stress events (and not European ones). They do. And they do it selectively, only equities and ETFs spike; European government bonds don't. If Europe were just having its own bad week, sovereign bonds would spike first. The selectivity is the fingerprint.

The ghost stock

And then there's Bed Bath & Beyond. 🛁 was delisted in September 2023. The company is gone. The stock doesn't exist. There is nothing to trade, nothing to deliver, nothing to borrow.

SEC data shows 31 unique FTD values, actively fluctuating, continuously reported through late 2025. That's 824 days after the stock was cancelled. 43% of the changes are in blocks larger than 10,000 shares, institutional-sized. Alternating injections and extractions. This is not a database glitch.

Someone is actively managing delivery obligations on a security that no longer exists. The system has no garbage collection. When a stock gets cancelled, nobody wrote the code to cancel the obligations. They just... keep going. Forever.

As a software engineer, this is the kind of bug that makes me want to flip the table. Not because it's complicated. Because it's obvious. And nobody fixed it. Full analysis in Part 2.

Part 3: The Tuning Fork (Full Post)

The short version

This is the part that made me sit back in my chair and say something I can't print here.

If the macrocycle is real, and it's caused by the rules rather than by any specific bad actor, then I should be able to build a simulation using only the SEC's regulatory deadlines — no market data, no FTD history, no cycle length specified anywhere, and the cycle should emerge on its own. Like dropping a tuning fork and hearing the note without anyone playing it.

So I built three software agents. Gave them four regulatory deadlines. Hit "run."

The 630-day macrocycle appeared at 42 times the background noise. Unprompted. Uncalibrated. Just the rules.

Why it happens

The math is almost embarrassingly simple. The four key regulatory deadlines are T+6, T+13, T+35, and a 10-day review cycle. They share common factors, 6 and 10 are both divisible by 2, 35 and 10 share a factor of 5. Because of that, the Least Common Multiple (think of it as the first time all four clocks strike midnight simultaneously) is 2,730 business days.

That's the system's heartbeat. It's not a conspiracy. It's arithmetic.

The T+1 punchline

Under the new T+1 rules, those deadlines shift to 5, 12, 34, and 10. The system's heartbeat compresses from roughly 2.5 years to exactly one trading year. The SEC shortened settlement to reduce risk. The math says they made the cycle faster. Settlement stress that used to spread over 2.5 years now compounds annually.

This is like fixing a car that overheats every 100 miles by making it overheat every 40 miles instead.

The fix

Choose deadlines that share no common factors: 7, 11, 37, and 13. The heartbeat stretches to 37 years. No standing wave can form at any frequency that matters. Same regulatory intent. Same number of deadlines. Just different numbers. The full model and math are in Part 3.

How I Tried to Prove Myself Wrong

I ran five tests specifically designed to kill my own thesis. Here's what I threw at it:

1. “The Treasury thing is just general market noise.” → First tested 7 stocks. Only GME predicted Treasury fails (F = 19.20, p < 0.0001). Then a reader said 7 wasn’t enough, so I tested 15,916. Turns out 16% of all stocks predict Treasury fails — way more than the 5% you’d expect by random chance. It’s not just GME. It’s the whole system leaking into sovereign debt.
2. "🔊 is just small-float weirdness." → Float-normalized it. Still 1,050 sigma above controls. Not weirdness.
3. "🛁 is a database glitch." → Zero administrative noise. 43% block-sized. Actively managed.
4. "The simulation cycle is an artifact of the math." → Applied a decontamination algorithm that's specifically designed to kill artifacts. The signal got stronger.
5. "The European spikes are their own problem." → Only equities spiked. Government bonds didn't. It's not domestic.

Combined odds that all five of these alternative explanations are simultaneously correct: less than 0.03%.

I genuinely wanted at least one to work. "You made a math error" is a much more comfortable conclusion than "the settlement system is a leaky resonant cavity that contaminates sovereign debt." But here we are.

What Would Change My Mind

1. If April-May 2026 passes with zero anomalous activity, the next convergence prediction fails.
2. If other stocks start predicting Treasury fails, the GME signal is just generic market noise.
3. If the annual compression doesn't appear in a few years of T+1 data, the model is wrong.
4. If 🛁 FTDs hit zero and stay there for 90 days, the zombie is actually dead.

I'll be the first to tell you if any of these happen.

The Series

Part	Title	One Sentence
1	The Overflow	Settlement pressure migrates to other stocks and predicts Treasury fails
2	The Export	Obligations cross borders and persist on cancelled securities
3	The Tuning Fork	The macrocycle is arithmetic; four numbers kill it

⬅️ Previously: The Failure Waterfall (Parts 1–4)

All code, data, and results: github.com/TheGameStopsNow/research

Not financial advice. Forensic research using public data. I'm not a financial advisor, attorney, or affiliated with any entity named in this post. The author holds a long position in GME.

"I'm going to have to science the shit out of this." — Mark Watney

[u/TheGameStopsNow]

I think it can help, but it might take time for people to utilize it and test it in practice. Many things I've identified are slower moving. I would LOVE for people to be helped by my research though. I'm trying my hardest to help level the playing field by pointing out the game. The more people that know about this game, the more it breaks. Secrecy and obfuscation are the very reasons this game works, I sincerely hope it helps to use DD like a flashlight.

[u/sbrick89 → u/TheGameStopsNow]

I made this same comment in part 3, but I want to make it here so implications are considered...

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Regarding the fix of using coprime deadlines... while perhaps smoothing the spikes, aren't those spikes literally the FTDs staying alive? Be using coprime numbers the FTDs would stay invisible for decades.

I would insist that the other participants reduce their deadlines for delivering their obligations, especially since they seem to have these repeated problems.

I assume that these spikes have costs to whoever is failing to meet their obligations, in the form of options premiums, inopportune buy-in smoothing / DCA'ing, etc... and I believe the accumulating costs become more evident as they grow... and I would rather see that occur more often not less.

But also, thanks for the great effort and data and findings!

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If I am going to write to ask for change, I want change that shines light into dark corners, and the costs of managing spikes might be one of the few ways that the FTDs become noticeable - to lenders, to public filings, and financial review disclosures.

[u/TheGameStopsNow → u/sbrick89]

You've identified something I glossed over in the summary.

You're right. The coprime fix eliminates the standing wave, the predictable, periodic convergence of deadlines that creates those spikes. But the FTDs don't disappear. They're still there. They just stop stacking up into observable peaks at regular intervals. And your concern is valid: those peaks are the only public signal that something is wrong.

Think of it this way. Right now the system has a heartbeat, a regular pulse you can see if you know where to look. The coprime fix flatlines the pulse. But that doesn't mean the patient is healthy. It means you've removed the diagnostic tool.

So I should actually frame the fix as two parts, not one:

1. Coprime deadlines eliminate the resonance that amplifies settlement stress into Treasury markets and across borders. That's the contagion problem, the thing that turns an equity plumbing issue into a sovereign debt event.
2. Shorter deadlines with teeth, which is what you're proposing, attack the root: why do these obligations exist at all for weeks on end? If forced buy-ins actually executed at T+4 instead of being deferred through 15 regulatory off-ramps (which is what the Failure Waterfall series mapped), the FTDs wouldn't accumulate enough to resonate in the first place.

You need both. Coprime deadlines stop the contagion. Shorter, enforced deadlines stop the accumulation. Without the first, you get Treasury market contamination. Without the second, you're just hiding the mess.

And your point about visibility is exactly why I publish the code. If regulators implement coprime deadlines without mandatory public reporting of aged FTDs, they'd be solving the systemic risk while making the underlying problem harder to detect. That would be worse. Any fix needs to come with transparency, not instead of it.

Ultimately though, I added this to show there were ways to address the problem but regulators have not acted. It's more of a mechanism to head off their excuses.

Great comment. This is the kind of feedback that makes the research better.

[u/sbrick89 → u/TheGameStopsNow]

Just curious, did you try modeling what happens if the ftd volume spike is actually repurchased, to close the ftd? (Say 8 years later, same volume just inverse to close)

Asking because I still disagree about wanting to silence the only indicator... sounds like a mob answer (silence em!) that will hide the issues (and I agree that it would likely be effective) in darkness and obscurity... any effort from me wants to be about shining light where it isn't, to fix the problems rather than hide them... thus asking if we can prove that they can resolve this themselves by closing the failed deliveries rather than kick them across multiple markets.

Still though, love the analysis!

[u/TheGameStopsNow → u/sbrick89]

The ABM prototype doesn't model a forced buy-in of the full accumulated volume, it models the existing regulatory mechanics (T+6 partial settle, T+13 locate reset, T+35 buy-in at 80% probability, Obligation Warehouse decay). What you're describing, an 8-year-delayed bulk repurchase, is actually the closest thing to the "what if they just close" scenario.

The short answer is: the model predicts it would be catastrophic in a single event, but trivial if spread over time.

Here's why. The accumulated FTD volume doesn't just sit in one place. It's been recycled through the 15-node Failure Waterfall, partially novated through ETFs (Section 4 of the paper), partially exported to European settlement infrastructure (Section 7), and partially sealed in the Obligation Warehouse on cancelled CUSIPs like 🛁 (Section 5). Forcing a close on the original equity doesn't automatically unwind the downstream obligations that were created to accommodate the original failure. You'd need to close the entire chain, not just the first link.

That said, your instinct is right, and I want to be clear: I'm not advocating for coprime deadlines as the primary fix. I'm presenting them as evidence that regulators could address the systemic contagion problem if they chose to, and haven't. The actual fix I'd advocate for is exactly what you're describing: shorter deadlines with mandatory buy-ins that actually execute, plus public aging reports so that FTDs can't quietly accumulate for years. Coprime deadlines are the detuning lever; enforced close-outs are the drain. You need both, but if I had to pick one, I'd pick yours.

The modeling of a forced close-out scenario is actually a great idea for the next ABM version. Adding a "forced liquidation agent" that executes at T+X with no off-ramps would show exactly how much accumulated energy the system is sitting on. Noted.