In other words, you keep 90% of the market and get paid double digits for it.

Numbers like that didn’t make any sense to me, as my initial thought process went along the lines of “if you can have that, then why invest in anything else?”

These high-yield option ETFs have been out for a few years now, but there doesn’t seem to be any rigorous, data-based insight on whether these are just self-liquidation schemes (i.e., “NAV erosion”) or actual, legit products.

So, I spent the week pulling data to answer an incredibly simple question:

“Are these things legit, or not?”

The answer is… sort of.

To get things started, I first established a universe size of ~17, covering the typical names like JEPI, the weekly 0-DTE funds, and the YieldMax funds (the triple digit APY guys).

For each fund, I pulled every dividend, split, close, and any other data point I could for the last 12 months, then computed three numbers per share:

1. Distributions paid: every dividend over the window, converted to today’s share basis (after a reverse split, one of today’s shares is several of last year’s, so the old checks get scaled up to match)

2. NAV change: what the share price itself did over the same window, split-adjusted

3. Coverage: distributions plus NAV change, divided by distributions. At 1.0, the fund earned exactly what it paid you. Below 1.0, the check was your own capital coming back.

The share-basis part matters more than you’d think as my first pass had TSLY “gaining” 185% when it actually lost 43%, so if you’ve ever audited these funds on raw price history and gotten nonsense, that’s why.

Now, if you’re still reading despite that onslaught of technical terms, thank you.

To try making things a bit easier, let’s start with a visual on what the first run came up with:

The big dogs are pretty legit

Because we’re optimists, we’ll start with the good.

Looking at the funds everyone’s parents own, the big-picture is that they actually make much more than they pay out:

• QYLD paid 12.5% of its year-ago share price and its NAV still rose 8.1%. Coverage: 1.65.

• JEPQ paid 11.7%, NAV up 8.5%, coverage 1.73.

• JEPI, XYLD, RYLD, SPYI, and QQQI all landed between 1.27 and 1.75.

Zero of the seven monthly index funds handed back a dime of your capital over the last year.

The weekly machines aren't all there

The Roundhill 0DTE funds sell same-day calls every morning and mail a check every Friday, and the findings are pretty much speak for themselves:

Read more

These were ordinary public URLs that simply didn’t exist at the moment I first asked, and the script’s whole job was to keep asking, politely, until one of them stopped returning an error and started returning a filing.

The reason this works is a quirk of how EDGAR publishes documents, and the reason it’s worth writing up is that the quirk hands you a filing tens of seconds before the feed everyone else watches knows the filing exists.

When the SEC says a filing is “public”, which clock is it talking about: the one where the file is on a server, or the one where everyone was told about it?

So today, I’m going to show you exactly how to do it yourself.

Where the seconds come from

EDGAR is pretty old now, and as such, it’s a well-documented pipeline: a submission gets accepted, validated, assigned an accession number, reassembled, and only then handed to the dissemination service that feeds the RSS streams, the paid subscriber feed, and every data vendor downstream of them.

Each stage takes time, and most importantly, the stages don’t finish all at once.

A developer named John Friedman noticed this in August 2025 in the crudest, best way possible: he looked at the HTTP headers.

• A 10-K’s SGML file carried a Last-Modified timestamp nine seconds before his RSS poller first saw the filing, and when he widened the check to roughly 4,000 filings, the delay between EDGAR accepting a document and the RSS feed publishing it averaged around 25 seconds.

If you know anything about how fast price moves after an earnings release, you know that even just a 5 second lead is major.

So, naturally, being inspired by his finding, I set out to run an experiment to see if this has already been arbitraged away to nothing.

Surprisingly, it hasn’t.

The accession number is just a counter

Every EDGAR submission gets an accession number, and the format does most of the work for you:

0001193125-26-351131 = {submitter CIK}-{two-digit year}-{sequence}

Now, this is pretty much the entire bedrock of the edge, so let’s take a second to actually define those:

• Submitter CIK: This just identifies whoever submitted the filing, and most companies pay a filing agent to press the button and the agent files under its own CIK all day long for hundreds of clients.

• Sequence #: Basically what it sounds like and is a counter that increments with each submission the agent makes that year.

What makes this prediction feasible is the submitter population is tiny and concentrated. Friedman’s follow-up repo found that ten submitters accounted for about 42% of all filings in 2024, so watching a handful of counters covers a huge slice of the market and companies tend to stick with the same agent year after year.

When I pulled the current-filings feed that morning, the busiest submitters were two law firms, Seward & Kissel and Willkie Farr, and a compliance shop, each churning out filings under a single prefix.

If the last thing an agent filed was sequence 003661, the next thing it files will be 003662.

The trick, in four steps

Once you know how that works, you can see how the whole pipeline can be run from a simple laptop:

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What got me to even care about this was the profit behind it.

In the case, there were at least 3,000 unique spoofing instances, which works out to about $372 per spoof. Call it six spoofs a day and it comes out to roughly $23,000 a month.

Nobody does 3,000 of anything by accident, so this was clearly a sophisticated operation which begs the question of how exactly did a guy in a bathrobe clock in and manipulate prices six times a day for four years?

If we can figure out how he did it, can we, just for fun, see if it’s still plausible to do right now?

Cerisano’s email to a broker: “I really Feel that My Account never should have been Closed, anyway the reason the Account was Closed was because of Layering, an easy way around this would be if the system only let Me put orders in on one side of the Market Meaning I Could only bid or offer but not both, this would take Care of the problem please advise thanks ..”

The trade, as a trade

We don’t want to make this post a sleepy academic explanation on microstructure, so to understand what happened and how spoofing works, let’s walk through one of the trades:

1. On Jan. 10, 2025 after the close, a mid-cap ETF had an bid/ask spread of 61.58x62.04.

2. The trader entered 9 sell orders from one account, at progressively lower prices. This brought down the ask to $61.65.

3. In a second account at a different broker, he was simulatenously buying, getting filled at an average price of $61.67.

4. In the original account, he then canceled the sell orders and the ask went back to the original $62.04.

5. He then ran the above steps in reverse:

   1. 10 buy orders pushing up the bid to $61.87

   2. Sold the shares in his second account at that price, collecting a $2.8k profit in 19 minutes.

So in sum, he sent fake orders to lower the price for his second account to buy in, then he made fake buy orders to raise the price for his second account to cash out.

It’s simple, definitely illegal, but that’s pretty much the end-game of spoofing.

Finding the ETF

The complaint anonymizes the ETF used, but it quotes exact prices at exact timestamps, which for anyone with a market data subscription is pretty easy to reverse-engineer:

• A mid-cap ETF trading between $61.58 and $62.04 on January 10, 2025

• I checked the daily bars for the obvious candidates and IJH, the iShares Core S&P Mid-Cap ETF, printed a low of 61.59 and a high of 62.20 that day.

  • Surprisingly, this is a $128 billion fund, one of the largest mid-cap vehicles in the world.

Next, I pulled the consolidated quote tape for IJH that evening, and the spoofing record is all there (chart below):

• At 6:25:46.107 p.m., a 300-share offer appears at $61.81 against the standing $62.04.

• The offer steps down through $61.78, $61.75, $61.71, $61.68, $61.65, exactly the ladder the SEC describes.

• At 6:31:31 it snaps back to $62.04 in a single update, which is what mass cancellation looks like.

• Then the bid walks up from $61.57 to $61.87, and starting at 6:37:19 you can watch him peel the buy ladder off tier by tier, about one cancel per second, until the final order dies at 6:44:05.336 p.m.

• Additionally, the complaint says “approximately 6:44:05 p.m.”, so there’s a good chance this was the actual trade.

The only thing that matters is the book

During regular trading hours (RTH) none of this works, and you can exactly why (chart below).

I pulled all 735,000 of that day’s IJH quote updates and computed the time-weighted spread by half hour:

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The account is a real brokerage account, the order was a real order, and the only thing standing between a language model and the market was a config file I edited in a text editor. By Friday it held four positions, and I couldn’t reconstruct the reasoning behind a single one of them.

Taking a step back, this only exists now because every retail broker decided, roughly at once, that language models should be allowed to trade:

• Robinhood opened Agentic Trading on May 27 and extended it to crypto in July

  • Apparently, more than 50,000 customers connected an agent within weeks of launch, with daily volume in the millions.

• Coinbase shipped an MCP server for agent trading back in June

• eToro says its Tori assistant has crossed 500,000 trades across everything from equities to FX.

With so many attention-grabbing headlines, it naturally begged the question of whether this wave brings a genuinely useful research tool or just a slow-motion margin call.

So, I spent the week finding out.

MCP This, MCP That

Put simply, an MCP server is just a menu.

It tells the model, in a machine-readable format, which tools it may call: fetch a quote, list positions, check buying power, place an order, cancel one.

The model reads the menu, reasons about what it wants, and emits a tool call, and the broker’s server executes it against your account like any other API request. That’s the entire integration, which is why setup takes four minutes and why 50,000 people did it.

The guardrails, in every one of these launches, are variations on the same three ideas:

• A dedicated account holding only the money you’ve reserved for the agent

• A push notification on each trade

• A disconnect button in the app.

Robinhood’s own terms are admirably direct about where their responsibility ends, stating that the company “does not control, supervise, monitor, recommend, or audit” the agents you connect. You pick the model and write the prompt, so you own the outcome.

The budget cap constrains how much the agent can lose, but nothing anywhere constrains how it loses it: there’s no order-type restrictions, no position limits, no drawdown stop, and no rule about turning the whole account over eleven times a day (chart below).

Where It Actually Breaks

I’ve been running variations of this harness privately for a while, well before brokers made it a product, and the failure modes are consistent enough that I can rank them.

Surprisingly, the model’s stock-picking ability barely makes the list.

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The fill came back in about a quarter of a second, a fifth of a cent per share better than the offer on my screen, and the confirmation email landed shortly after.

Pretty simple: you clicked, a computer matched you with a seller, done.

Naturally though, what wasn’t in the email was that my order almost certainly never touched an exchange.

In reality, my broker sold the order, a market maker filled it out of its own inventory from a data center in New Jersey, and the “better than the quote” fill was my cut of a pie that got divided three ways before I ever saw it.

Every junior quant eventually has to learn these mechanics, usually in fragments, so I traced my Ford order through every tollbooth to see who got paid along the way.

When you hit buy in a brokerage app, where does the order actually go, and whose P&L does it land in?

Hop one: your broker sells the order

To anyone who was around during the PFOF debacle with GME back in 2021, this part will be common knowledge, but it won’t hurt to start here.

At the end of the day, your commission-free broker’s routing decision is a revenue line. Wholesale market makers pay brokers for the right to execute their customers’ orders, which is the famous payment for order flow.

Interestingly enough, every broker publishes a quarterly Rule 606 report listing exactly which venues got its orders and what it was paid per hundred shares, and Robinhood’s disclosures even spell out the pricing model: the market maker pays a fixed percentage of the bid-ask spread on each execution.

Wide-spread names generate bigger checks than tight ones, so naturally, most of the profit comes from more illiquid stocks.

Per share it looks tiny, a fraction of a cent, but multiplied across a few billion retail shares a month it’s the reason “free” trading exists.

Hop two: the wholesaler keeps the spread

Now, remember how we were so confident that our order never touched the exchange?

Well, marketable retail orders overwhelmingly skip the exchange entirely: more than 90% of them are routed to off-exchange wholesalers and filled from the wholesaler’s own book.

The wholesaler business, like any high-barrier industry, is about as concentrated it gets, with Citadel Securities handling roughly 41% of retail market orders, Virtu 26%, and G1 another 16%, so three firms see more than 80% of everything retail does.

What makes the business so attractive to market makers is that, in theory, retail flow is the safest counterparty in the market.

An order for 100 shares of Ford from a phone carries essentially zero short-term information; nobody’s personal account is front-running an index rebalance.

When the counterparty is uninformed, the market maker gets to earn the bid-ask spread without the adverse-selection tax that comes from trading against people who know something, and the SEC’s own economists have a working paper walking through exactly this logic.

The wholesaler’s fill is where my “price improvement” came from. The quote on my screen was, say, $14.38 bid, $14.40 offered. The wholesaler filled my buy at $14.398, two tenths of a cent inside the offer, and booked the difference between that and wherever it lays the risk off as its own edge.

By some measure, it’s a win-win and everyone walks away feeling good: I beat the quote, the broker got its check, the wholesaler kept the fat middle of the spread.

The key thing to grasp is that price improvement is measured against the public quote, and the public quote is set on exchanges the retail flow never visits.

The wholesaler “improves” on a benchmark it is partly responsible for leaving wide.

That’s the sentence that made the whole industry make sense to me, and I’d hold onto it before forming an opinion on whether the arrangement is good or bad for the little guy.

Whether the benchmark itself would be tighter in a world where retail flow hit the lit market is an open-ended question.

Hop three: the exchange gets the leftovers

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There were dozens of tools like it, odds overlays, portfolio trackers, copy-trade mirrors, stop-loss managers, and as of August 1st they’re all contraband. Google updated the Chrome Web Store policies to prohibit extensions that facilitate real-money trading on prediction outcomes, gave developers until August 1st to comply (rebrand from “arbitrage” → “simulated analytics”), and started pulling listings.

The same date was supposed to be a much bigger deadline. Minnesota passed a law in May making it a felony, up to five years and a $10,000 fine, to operate a prediction-market platform in the state, effective August 1st.

Fortunately (or unfortunately, depending on where you stand) that ban never arrived, as a federal judge blocked it two days early on the argument that the Commodity Exchange Act preempts state law, in a case where Kalshi, Polymarket, and the CFTC were all on the same side.

So, let’s tally up what happened on August 1st: a state legislature tried to ban the venues and got stopped in federal court, while an ad company banned an entire class of trading software by editing a policy page.

At this point, a pretty important question comes up:

If the arb bots just died, shouldn’t the spread between Kalshi and Polymarket show it?

Thankfully, it’s a measurable question, and I’ll get to the measurement, but it’s worth being precise about what actually died first, because “Google banned trading bots” gives the bots more credit than they earned.

What on Earth is a “Browser Bot”?

The extension ecosystem around these venues was bigger than most people realize: odds converters that reprinted prices in American or decimal format, wallet trackers that showed you what the accounts you follow were buying, stop-loss managers promising professional-grade risk management from a browser popup, and so forth.

An extension reads prices after the site posts them, injects its own buttons next to the real ones, and when it wants to act, it either simulates your click or calls the venue’s own client-side endpoints from within the page, wearing your session cookie.

That architecture, while effective, put a hard ceiling on how fast or how big these things could ever get. The loop is: venue updates, page renders, extension reads, extension decides, extension clicks.

On a good day that’s a few hundred milliseconds behind the book, on a normal day it’s seconds, and the whole thing is throttled by the same public rate limits your browser tab gets, on one retail account, at retail size.

The full stack looks like this (diagram below).

Google’s policy deleted exactly the top layer of that diagram, but everything underneath is untouched.

Kalshi uses a real central limit order book behind public REST and WebSocket APIs, and Polymarket matches orders off-chain against a book of its own before settling on-chain; so if your bot spoke to the book directly, August 1st changed nothing for you.

Who’s Trading This Stuff, Anyway?

The marginal spread on both venues stopped belonging to retail tooling a long time ago.

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Every time that pair of numbers makes the rounds, the same idea follows:

“both of these things are decay machines by construction, so short $100 of each, net out the market, and collect the drag from both sides at once.”

took a run at it in July and landed about where the academic literature lands, the decay is real but frictions eat a meaningful chunk of it.

Now, if you’ve been reading our work for awhile, you know that we love nothing more than these kinds of potentially scalable, market-neutral edges.

So naturally, I wanted to look at the data myself: the borrow in actual dollars, the margin it locks up, what the rebalance schedule changes, and what the tail did across the full sample.

So, I rebuilt the whole thing from scratch and ran every version of the trade side by side.

Short $100 of TQQQ and $100 of SQQQ. Where does the money actually go?

Why there’s drag to harvest

Both funds promise 3x the daily move of the Nasdaq-100, and the daily part is where we need to look: they reset leverage every close, so over any horizon longer than a day the compounding takes over:

• If the index goes up 1% and then down 1%, it sits 0.01% lower while the 3x fund sits 0.09% lower. What makes the trade tempting is that the minus-3x fund is also down 0.09%.

A flat, choppy market grinds both legs down simultaneously, and a short position in both collects the grind.

In theory, the harvest should scale with realized variance, and in the data it does: the yearly gross P&L of the pair short correlates with the Nasdaq’s realized variance at 0.60 across the sample (2015-2026).

So, right off the bat, chop pays.

The Reddit version

The Reddit version is as crude as it gets: short $100 of each in January 2015, walk away, let both legs rot.

The SQQQ leg performs exactly as advertised and hands you the full $100, since a short’s gain caps out at the entire value of what you sold, but the TQQQ leg is where you’re effectively guarnteed to blow-up.

A short’s loss has no cap, TQQQ went up 37x, and what started as a $100 short liability compounds into a $3,698 position you owe.

The pair finishes down $3,498, and at the low in June 2026 it was down $4,132 (chart below).

Rebalancing fixes it

Now, obviously the naive version doesn’t work; so at this point, we can ask ourselves a simple question: instead of just holding forever, what if you just rebalanced the position frequently?

Fortunately, this is where things get good.

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That’s about $2.5 million at the $77,135 average the filing reported, out of a treasury that measures its position in the hundreds of thousands of coins, and it was the company’s first sale in over three years.

The disclosure was one line in an 8-K filed June 1, the kind of housekeeping detail an analyst skims on the way to the leverage tables.

It flipped a $60 million Polymarket contract from a near-certainty into an 81-cent panic, drew two formal resolution proposals that both got challenged, went to a token-holder vote, and as of July it’s the subject of a lawsuit in the New York Supreme Court.

I spent this week reading everything that line broke.

A market about a thing that never happens

The contract was “MicroStrategy sells any Bitcoin by May 31, 2026?”, and for most of its life it was one of the most boring things on the venue.

Saylor’s entire public identity is that he never sells, so YES traded down at a few cents the way deep out-of-the-money puts trade at a few cents:

someone is always willing to hold a lottery ticket, and someone else is always happy to collect pennies for insuring an event that never happens.

About $60 million of volume went through on those terms.

And if you were short YES down there, you were arguably making the cleanest bet on the platform:

• The company files everything with the SEC

• The treasury wallets are watchable on-chain

• The one man who decides whether to sell gives interviews about never selling.

As forecasting problems go, there was almost nothing to get wrong.

June 1, 81 cents

On June 1st, the 8-K landed:

• The sale executed between May 26 and May 31, which sits inside the market’s window.

• The disclosure arrived June 1, which sits one day outside it, since the contract’s cutoff was 11:59 PM ET on May 31.

Within hours YES had spiked from low single digits to 81 cents as traders piled into what looked like a resolved question, because the filing said, in plain SEC prose, that the thing the market asked about had happened on time.

Polymarket’s team then pinned a clarification to the market page, which is what the next two months of fighting were about:

“Confirmation achieved outside of the market’s time frame does not qualify.”

Read one way, the market asked whether Strategy sells by May 31, Strategy sold by May 31, done.

Read the other way, a market has to be resolvable from information available inside its own window, and on May 31 no filing, no on-chain evidence, and no credible reporting confirmed any sale.

Both readings are coherent, which was pretty much the problem as the resolution text supported either or.

I pulled the contract’s price history and annotated the whole arc (chart below)

That middle nub, where YES hovered near 12 cents for days, is a beautiful representation of how markets become efficient. By then, the original outcome was pretty much established (he sold the Bitcoin), so the price at 12 cents acted as a proxy for how the hundred or so pseudonymous token holders would vote on the final outcome.

“Pseudonymous token holders” might sound like jargon-slop at first glance, but if you’re getting into prediction markets, you quite literally need to know how they work.

Two disputes and a token vote

Polymarket outsources settlement to UMA’s optimistic oracle:

• Anyone can propose an outcome by posting a bond, and if nobody challenges it within a couple of hours, it stands.

Almost every market resolves this way, cheaply and instantly.

The keyword here is almost, as this one went a bit differently:

• Someone proposed “No” and got challenged, a second “No” proposal followed and got challenged too, and after that the question escalated to UMA’s voting layer, where UMA token holders pick the outcome and voting power is proportional to how many tokens you hold.

The ballot had three options: settle it “No” on the announcement-timing reading, settle it “Yes” on the event-timing reading, or call it too early to resolve.

By the end, about 98.6% of voting power went “No”, the vote wrapped June 4, and YES went to effectively zero.

One account had bought roughly 50,000 YES shares for about 35,000 USDC after the 8-K dropped, which is to say they paid about 70 cents a share for a claim on an event that had already, verifiably occurred, and they collected nothing.

Another trader claims he lost $500,000 on the same trade.

On July 3, two of them sued Polymarket and its executives in the New York Supreme Court, alleging breach of contract and deceptive practices and asking for the full $1.00-per-share redemption value.

Who counts the votes

So the natural next question is who these voters are, which is what the Wall Street Journal spent the spring answering.

Their analysis of UMA’s voting records, published about two weeks before this dispute, found that:

• At least 60% of active UMA voters are linkable to live Polymarket accounts

• The ten largest wallets carry more than half the voting power in most disputed markets

• In roughly one in five disputes, at least one voter held a financial stake in the very outcome they were ruling on (chart below).

None of that proves this particular vote was corrupt, and to be fair, a 98.6% landslide doesn’t need any self-dealing to explain it. Still though, you have to admit it’s… interesting.

In any regulated corner of finance, an arbitrator with a position in the case is a Bloomberg-headline-turned-arrest, but in prediction markets, it’s pretty much the default.

What you’re actually long

If there was any general lesson to pull out of this, I’d say:

• A prediction-market contract is a package of two bets: one on the event, and one on the resolution process agreeing with you about the event.

  • On the happy path, the second bet is invisible and free, so everyone prices these contracts as if the first bet is the whole product.

This episode highlights what it looks like when that “free” second bet gets nasty. It happens exactly when the outcome is ambiguous and the money at stake is large, since that’s when someone finds a dispute worth bonding. And you can’t hedge it, because your counterparty in the settlement fight may literally be the one voting.

For the sellers of long shots, the pennies-collecting crowd, this is something you need to be aware of. You’re likely already automated, so at the very minimum, pass in the contract’s resolution rules to an LLMs API to get a score on the ambiguity of the settlement; it might be the only thing that keeps you in business.

Final Thoughts

I love prediction markets, and none of this changes the fact that they price ordinary events well and settle nearly all of them without drama.

As we covered in our "Bonding Bots" in Prediction Markets post, you really can run a solid business of executing high-probability trades, but the settlement risk will always be there.

So, if you’re just getting started and want to trade these markets, then at the very minimum, just keep that in mind.

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

You’ve been trading your own account for a few years and the track record is real: a decent Sharpe, a small following, and a few calls that aged unusually well.

Suddenly, an email lands from a quant fund you’ve never heard of.

There’s no job in it, but for some reason, the fund wants to buy your trade ideas: ticker, long or short, a time horizon, how confident you are, all submitted through a portal.

If the ideas work, you get paid. Nobody interviews you, nobody audits your model, nobody even asks for a resume.

A multi-billion dollar quantitative fund, the kind of shop that measures execution slippage in fractions of a basis point, wants to wire money to strangers on the internet in exchange for stock picks.

The whole thing reads like a phishing attempt. However, it’s real.

In fact, that model has been running for over two decades, and the industry calls it alpha capture.

So today, we’re going to walk through where it came from, what actually happens when you hit submit, and what happened when two siblings in a London flat figured out how to game it.

It Started With an Argument

In 2001, the two founders of Marshall Wace had a disagreement.

Ian Wace, formerly Deutsche Bank’s global head of equity trading, and Paul Marshall, ex-Mercury Asset Management, couldn’t agree on whether the stock recommendations brokers constantly phoned in were actually worth anything.

To settle it, they handed the problem to a fresh graduate named Anthony Clake:

“build a system that tracks every broker idea and measures whether following them makes money.”

The first version was an Excel spreadsheet that eventually became TOPS, the “Trade Optimised Portfolio System,” and in July 2002 it started trading real money through the Eureka Fund.

In its first full year it returned +23.9% gross while the market benchmark fell -21.1% (chart below).

The scaling from there was aggressive. By 2005 the London operation was ingesting 800 to 900 ideas per day, and the firm had collected roughly half a million trading ideas from 2,200 individuals across 246 securities firms.

Marshall Wace was paying brokerage firms over $250 million a year by 2004-05, and Wace described it himself:

“All TOPS is a Hoover... It sucks in great quantities of ideas and tries to sieve out what is interesting.”

Clake made partner in 2004, is now the firm’s third-largest owner, and reportedly took home around $330 million in a recent year. TOPS vehicles run roughly $30 billion today.

Too Good to Be True

If you wave dollar bills in the air and tell people you’ll pay them for trade ideas, eventually, you’ll get people who do whatever it takes to get paid.

Squarepoint Capital runs offices in London, New York, Singapore, Dubai and Paris.

Oerta Korfuzi, an Albanian day trader, submitted trade ideas to Squarepoint’s alpha capture program through an entity called Pelkor Capital. She shared a London flat with her brother, Redinel, a research analyst at Janus Henderson.

Her ideas were good. Specifically, they were short ideas on names about to announce dilutive equity raises, and they were remarkably well-timed.

Part of her brother’s job involved “wall-crossing” emails: confidential notices from companies gauging investor interest ahead of equity raises and block sales. If you know a company is about to announce a dilutive raise, you know its stock is about to drop.

Within minutes of those emails hitting Redinel’s inbox, trades went off in accounts operated by his sister.

The instrument of choice was CFDs, leveraged shorts on the exact names about to announce. Between 17 December 2019 and 25 March 2021, the pair traded ahead of announcements in 13 stocks, clearing over £960,000 in profit.

Now, what separates this from garden-variety insider trading is that the same information was being monetized twice.

Once through their own leveraged trading. And once more, sold to a quant fund as a “signal” through Pelkor Capital.

Think about what Squarepoint’s platform saw:

• a contributor whose short ideas kept hitting within days

• a hit rate a legitimate analyst would kill for

• alpha with almost no decay, because the catalyst was already scheduled

Prescience and inside information look identical in a track record.

Worse than identical: the insider scores better. Higher hit rate, faster payoff, cleaner alpha versus benchmark (chart below).

A system that pays strangers for prescient ideas has no easy way to ask why the ideas are prescient.

The FCA (UK version of the SEC) caught it the way modern regulators catch most things: market surveillance and trading-pattern analysis across large datasets. The trades clustered too tightly around announcements Redinel had been wall-crossed on.

Investigators also found 173 deposits funneled to Albania and about £25,000 in cash sitting in a Knightsbridge safe deposit box.

Both siblings were convicted in 2025; Redinel got 6 years, Oerta got 5.

How It Actually Works

While the high-level version of this is interesting on its own, things get really quirky when you look at the finer parts.

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• The game is in the 9th and one team is up eight runs

• The election has been called

• The movie has already premiered, whatever.

A “bonding bot” systematically buys these near-certain contracts and collects the last few cents at settlement. Institutional bond traders typically call it pull to par, as you’re buying something at a discount to face value and clipping the yield as it matures.

The math on it is honestly seductive, at first:

1. Buy YES at $0.98.

2. Contract settles at $1.00.

3. Collect $0.02, roughly 2% on capital, often within minutes.

4. Recycle the capital into the next “decided” event.

5. Repeat across every market on the board, every night.

Run that loop across a full slate of outcomes and the equity curve looks gorgeous. Straight line up, win rate north of 99%, the kind of thing that screenshots well.

The problem, however, is the skew. When you buy at $0.98, you’re risking 98 cents to make 2, so one miss wipes out roughly 49 perfect trades.

The payoff profile (chart below) is a long staircase of two-cent steps up, and a trapdoor that gives back all of them at once:

It’s the same profile as selling deep out-of-the-money puts: a long stream of small wins funding one occasional catastrophic loss.

The Run That Didn’t Count

On July 24, that trapdoor opened at Oracle Park, a little after 1 a.m. Eastern.

We’re not a sports publication, so to keep things high-level:

• In the final inning of a tie game, Team A made a play that visually won it by multiple runs

• Data feeds posted the multi-run final and bots rushed the “win by 2+” contracts up to $1

  • Volume went from 11,149 contracts to 5 million in a few minutes.

• The official scoring was subsequently corrected under an MLB rule

• The “bond-like” contracts closed the market at $0, with aggregate bonder losses in the seven figures.

This was the last game of the night, so a good chunk of that carnage likely happened while the owners of those bots were asleep. Kalshi appears to have paid traders on both sides afterward, though there’s no official postmortem on the mechanics yet:

Now, believe it or not, even after a face-plant like that, running one of these can be genuinely profitable with a bit of sophistication.

Well, Maybe a Bit of Free Money

A naive bonding program essentially buys anything with a high price:

while markets_open:
    for market in board:
        if feed_status == "final" and ask < $1.00:
            buy(everything)

In contrast, a more sophisticated operator knows which two cents are actually free, and it generally doesn’t even take that much.

Read more

“The night before every earnings report, the options market publishes a forecast of how far the stock is about to move. But is that forecast any good?”

That question has been sitting in our research notes for a while, and it turns out to be one of the cleanest studies you can run with public data.

It’s a strange forecast to begin with. No analyst signs it, nobody defends it on CNBC, and it’s backed by real money on both sides.

It also gets graded automatically. The report drops, the stock moves, and the forecast was either too big or too small. Every quarter, for every listed name, forever.

Yet almost nobody keeps score. Sell-side analysts get their price targets mocked on Fin-Twit every quarter, economists get their CPI calls stomped within seconds of the print, but when was the last time someone overpaid for vol and called for a brand new options pricing model?

So today we’re going to walk through this little corner of the derivatives business, end to end, the same way we’d run any event study on the desk.

It’s Essentially a Betting Line

The forecast lives in the ATM straddle: the call and the put at the strike nearest the stock price, bought together.

Say a stock trades at 100 the afternoon before it reports, the ATM call costs 2.60 and the ATM put costs 2.40, so the straddle costs 5.00.

Whoever buys it makes money only if the stock ends more than 5 points away from 100, in either direction. Whoever sells it keeps the premium only if the stock stays inside that band.

Divide the straddle by spot and you get the break-even as a percentage:

5.00 / 100 = 5%

That ratio is the implied earnings move: the size of move at which straddle buyers and straddle sellers both walk away flat.

The reason we call it a forecast is that it gets set exactly the way a betting line gets set:

• If traders collectively believed the stock would move 8% on the print, a 5.00 straddle is free money and they’d buy it until the price rose.

• If they believed 2%, they’d sell it until the price fell.

The price settles where the marginal buyer and the marginal seller cancel out. A live, money-backed consensus estimate of the move’s magnitude.

There’s also a mechanism-first reason to suspect the line might sometimes be off:

• Market makers are obligated to quote through the event, and every quarter a reliable crowd shows up on the other side: hedgers protecting positions through the print, and speculators buying the straddle as a lottery ticket.

  • Someone has to warehouse all that demand, and warehousing risk you can’t hedge away commands a premium. That premium lives inside the straddle price, which means it lives inside the forecast.

A suspicion isn’t a result, though, so let’s see what the data actually says.

Don’t Botch the Clock

The first rule of the loop we run on every event class: write the hypothesis down before touching data, precisely, so the data can’t seduce you into testing a prettier claim later.

Hypothesis: The earnings move implied by the options market is, on average, larger than the move that gets realized.

Yes/no answer, with a clean, measurable magnitude.

Now, when working with earnings data, it’s especially important to make sure you’re tracking the clock.

Earnings come in two flavors, bmo (before market open) and amc (after market close):

• The capture date is the last session that closes before the announcement. That close is where the straddle still contains the full event premium, so it’s where the implied move gets read.

• The reaction date is the first session that trades after the announcement, the one that absorbs the move.

For a company reporting amc on Tuesday: capture Tuesday, reaction Wednesday. For a company reporting bmo on Tuesday: capture Monday, reaction Tuesday, because by Tuesday’s open the answer is already out.

This is why we like event datasets that carry the alignment explicitly. In the earnings-move-history data we use, every row is one event stamped with its timing (bmo or amc), its capture date, and its reaction date, alongside implied_move_pct and realized_abs_move_pct measured at exactly those moments.

Was the Market Right?

With the clock handled, the measurement is pretty straightforward.

The core metric to track is the implied-minus-realized spread, event by event:

events["spread"] = events["implied_move_pct"] - events["realized_abs_move_pct"]

print(f"Events:      {len(events)}")
print(f"Mean spread: {events['spread'].mean():.2f}pp")
print(f"Hit rate:    {(events['spread'] > 0).mean():.1%}")

Three numbers, and each one earns its place:

1. Event count tells you whether you have evidence or an anecdote. Eight events is a story; several hundred is a distribution.

2. Mean spread is the headline: on average, was the forecast an overestimate?

3. Hit rate tells you the distribution of outcomes. A positive mean driven by 55% of events is a very different animal from one driven by 90%.

Run this across broad universes of liquid names and you land on the population verdict: the implied move has historically tended to overshoot the realized one. The line runs rich, on average, exactly the way the warehousing story predicts (chart below).

Interestingly enough, this is where most write-ups stop, declare victory, and back into a short-straddle strategy.

It’s also where the study starts getting good, because a population average is a blunt instrument. It says the crowd overpays on average, but it says nothing about who.

Okay, So Who?

Some names overprice their own earnings move nearly every single quarter: the street buys the straddle, the report drops, and the stock moves half of what was priced in. Other names, far rarer, keep delivering more than the market priced.

So, the question narrows from “does the market overpay?” to “who does the market chronically overpay for?”

In the dataset, every event row carries the name’s running record as of that event:

• n_events_to_date: how many events are on record.

• hit_rate_to_date: the share of past events where implied overshot realized, on a 0 to 100 scale.

• avg_over_under_to_date: the trailing average of implied minus realized, in percentage points.

• overpriced: the outcome on this specific event.

Now, the _to_date suffix is doing more work than it looks like:

• Each row’s running stats are computed only from events up to and including that row’s date, then frozen into the row forever.

• Which means a backtest that ranks names by hit_rate_to_date on some historical date is ranking on exactly the information a trader standing on that date could have had. Look-ahead bias, the classic backtest sin, is designed out of the data from the start.

Reading the records, you’re looking for three things at once (chart below):

1. A high hit rate, say above 75. The straddle overshoots most quarters, habitually.

2. A bias big enough to survive costs. A +0.2pp (percentage points) average is real but untradeable after spreads; a +2pp average is a different conversation.

3. Enough events to believe it. Roughly 12 or more, three years of quarters. A perfect record over 4 events is a coin that came up heads four times.

A name that clears all three is what the market calls a chronic overpricer: the market keeps buying its event insurance rich, quarter after quarter, and the record says so in writing.

The public snapshot begins in 2024, so no name yet clears that 12-event conviction floor. Using a deliberately looser eight-event floor only to show what the live ranking produces, the top three candidates in this snapshot were:

Final Thoughts

Notice what never happened in this walkthrough: nobody predicted a single quarter. The entire edge came from being the one who bothered to grade the forecast.

Everything to this point gets you a list of suspects, though. Turning that list into a position is its own discipline: checking whether a name is charging its usual rich price right now, gating the watchlist ahead of the reporting week, and attacking whatever survives for the tails, regimes, and costs that can erase a +2pp edge.

We go substantially deeper into that discipline, deploying this exact study into a trade, in Module 5 of Alphanume Learn: you build the chronic-overpricer tally and the full pre-earnings screen yourself, live in the browser, against the same dataset we just used.

Learn is the series we created to be the first of its kind: an interactive quant curriculum that goes genuinely deep into systematic, event-driven trading. Twelve modules, one research loop, run on volatility, 0-DTE, dilution, de-SPACs, defaults, and the rest of the catalog.

Module 1 is free, and the full course is included with Alphanume Pro ($99/month).

Start with Module 1 (free) →

As always, thank you for reading, and we hope to see you in the next one.

To see why anybody would want that, put yourself behind a gas utility desk:

Ten weeks into winter somewhere in the Midwest, and nothing has broken. No pipe burst, no substation dropped, no crew got called out at three in the morning.

The winter is just… mild. A few degrees above normal, day after day.

Every therm (unit of gas) you didn’t deliver is a therm nobody pays you for.

Now, remember, you still own every mile of pipe, you still staff every crew, and you still service the same debt.

Mild winter → fewer therms out the door → lower revenue

By March you’ve missed guidance, and everyone on the call knows why.

So, you call your broker to ask what it costs to insure against this next year, but find that nothing really exists.

Insurance pays on a loss event, something discrete that an adjuster can drive out to, photograph, and attach a number to. When all you’ve got is a furnace in a house in Wisconsin that cycled on four fewer times a day for two months, you don’t really have a claim.

At this point, you ask a question:

“How do you still make a profit, even when the weather acts against you?”

Fortunately for you, this is the whole idea behind weather derivatives.

It’s a niche market, tiny compared to equities or rates, and almost nobody outside a handful of trading desks and utility hedging teams actually trades it.

Even so, the mechanics are elegant once you see them, and the fact that this market exists at all tells you something about how far derivatives can be stretched once you’re willing to turn anything measurable into an index.

So today, we’re going to take a quick journey into the strange land of trading weather futures.

How Does This Stuff Even Work?

The whole market rests on the degree day.

Heating and cooling demand tracks how far temperature strays from 65°F, the rough point where a building doesn’t need the furnace or the AC. That’s the industry convention, and it’s the base of almost every US contract written.

From there, each day converts into one of two numbers:

• HDD = max(0, 65 - average daily temperature)

• CDD = max(0, average daily temperature - 65)

Average daily temperature is the day’s high plus the day’s low, divided by two.

A 50°F day gives you 15 HDD and 0 CDD. An 80°F day gives you 0 HDD and 15 CDD (chart below).

Cold day → HDD. Hot day → CDD. 65°F → nothing happened.

Sum the daily values across a month or a season at one weather station and you have a cumulative HDD or CDD index.

Just Add It Up

At this point, seeing how this maps to a tradeable product likely seems vague, but it’s actually pretty simple:

1. Pick a city and a period, either a calendar month or a multi-month seasonal strip. CME designates one weather station per city.

2. Convert each day, taking the station’s reported temperature and adding the resulting HDD or CDD to a running cumulative total.

3. Settle on the total. At expiration, that cumulative number is the settlement index.

4. Multiply. For US cities the contract unit is $20 times the cumulative index. A 31-day month averaging 55°F throughout is 310 cumulative HDD, worth $6,200 per contract.

5. Pay in cash. Whoever was positioned for a colder or hotter month than showed up pays the difference.

Run it through our utility desk from the top of the post and it looks like this:

• The exposure: a low-HDD month is what hurts you, since fewer degree days means fewer therms sold

• The position: short 20 contracts at the market’s 310 HDD level

• Riding on it: 310 × $20 × 20 = $124,000

The month comes in warm. Your station stops at 260 cumulative HDD.

• Settlement index: 260

• Move against the level: 310 - 260 = 50 HDD

• Hedge P&L: 50 × $20 × 20 = $20,000

The 50 degree days you didn’t get are the same 50 degree days the contract just paid you for.

Where Trading Gets Interesting

As usual, every market built for hedging ends up attracting people with nothing to hedge. Weather is no exception as whoever took the other side of that utility’s short doesn’t own a mile of pipe and has never sold a therm in their life.

To see how, let’s work through a real-world edge-case:

Read more

For a decent slice of the buy side, that print will be a mere formality, as they’ve already watched the quarter assemble itself one card swipe at a time.

Little known to the general public, every tap of a Visa at a salad counter becomes data exhaust. The anonymized record flows out of an issuer or processor, gets tagged to a merchant, and lands in a dashboard some fund is paying handsomely to refresh.

By the time management gets on the call to explain the quarter, the sophisticated money has already traded it.

So today, we’re going to walk through how this actually works, using the best real-world example on record: two data analysts at Capital One who used the bank’s own card database to turn roughly $147,300 into $2.8 million, and got charged by the SEC for their trouble.

We’ll start with the textbook version of transaction-panel nowcasting, and by the end you’ll see how the version a real desk runs is where it gets really interesting.

So, without further ado, let’s get right into it.

Your Receipts, For Sale

A transaction panel is a sample of millions of de-identified credit and debit card accounts, sourced from issuers, processors, and financial apps.

To the fund, your $16 harvest bowl is account #8837421 spending $16 at merchant ID “SWEETGREEN #204” on a Tuesday, from an account that’s been in the panel since 2021.

The vendors packaging this are a small oligopoly: YipitData (450+ institutional clients), Consumer Edge (93M+ cards across 40K tagged merchants), Bloomberg Second Measure, and M Science. Below all of them sits the public tier, Bank of America’s aggregate card-spend data, which anyone can read.

The core pipeline for this generally flows like this:

1. Ingest raw transactions from the card feeds.

2. Tag each descriptor string to a ticker (the merchant-mapping problem, which is harder than it sounds).

3. Sum the matched spend for the company’s fiscal quarter.

4. Compare it to the same panel’s matched spend a year ago.

That YoY growth of matched spend is your revenue proxy. If the panel’s Sweetgreen spend is running -9% against last year, you have an estimate of the quarter weeks before the print.

Accuracy varies by name and desk, but a 2019 study found that card-panel based methods outperformed earnings consensus on ~57% of quarterly revenue predictions.

So, at first glance, it’s better than a coin flip, but far from a crystal ball.

Now, to see what this looks like when someone actually runs it with conviction, let’s go back in time.

Two Analysts Walk Into a Database

In January 2015, the SEC announced insider trading charges against Bonan Huang and Nan Huang, two data analysts at Capital One.

Their day job was fraud investigation. Mechanically, that job description means one thing: raw query access to the bank’s database of customer credit-card transactions.

Millions of cardholders, every swipe, updating continuously.

Somewhere along the way, they realized what they were staring at all day. Capital One’s transaction database is a transaction panel, the same asset the vendors above sell for six or seven figures a year, except theirs was enormous, clean, and free.

Per the SEC complaint, the playbook went like this:

1. Query the database for cardholder spend at a public company, say Chipotle, across its fiscal quarter. (They ran thousands of these searches, across 170+ publicly traded companies.)

2. Compare it against the same spend a year earlier. That YoY number is your revenue estimate, the same thing the street is trying to model.

3. Hold the estimate up against Wall Street consensus.

4. Panel running hot versus consensus → buy calls into the earnings print. Running cold → buy puts.

5. Wait for the print, collect, repeat next quarter.

Options gave them leverage, and quarterly earnings gave them a catalyst with a date stamped on it, so the account compounded violently.

Over roughly three years, they turned about $147,300 into $2.8 million for an ~1,800% return. Their single best session came in June 2014, when one Chipotle earnings surprise paid them more than $377,000 in a day (chart below).

For scale, a fund that compounds at 20% a year gets a marketing deck and a waitlist, but these two were running multiples of that from a cubicle on the fraud desk.

The Only Illegal Part

At this point, you might assume the illegal part of this is using that kind of data at all.

Read more

CoreWeave, the AI infrastructure name whose entire business model is buying GPUs faster than the revenue comes in, went out for $3.0 billion of convertible notes. Demand was strong enough that the deal got upsized to $3.5 billion and settled within days.

That’s a coupon the US Treasury itself couldn’t beat.

So, something about this picture is clearly off:

“Who hands a cash-burning AI credit billions of dollars at 1.75%, and why were they fighting each other for allocation?”

The short answer is that nobody in that order book was lending.

The buyers were, overwhelmingly, hedge funds running one of the oldest trades in the business: convertible bond arbitrage. And within hours of the deal pricing, most of them had shorted CoreWeave stock against their allocation.

This might seem a bit strange at first, so today, we’re going to walk through the whole machine using the real CoreWeave deal: what the instrument actually is, how the trade gets put on in dollars, what three months of CRWV chaos did for the people running it, and why the issuer is thrilled to play along.

Nobody Here Is Lending

At root, a convertible bond is two instruments stapled together:

convert ≈ straight bond + embedded call option

For the CoreWeave deal, each $1,000 note converts into 8.3612 shares, which pencils out to a conversion price of about $119.60, a 30% premium over the $92.00 close on the day the deal priced.

Now, priced as straight debt, a 1.75% coupon on this credit is nonsense. A plain six-year bond from a cash-burning AI name would need a coupon several times higher to clear the market.

The spread between that hypothetical coupon and 1.75% is the price of the call option. The buyer accepts a below-market coupon and gets paid in optionality instead (chart below).

There’s a sweetener, too: new converts typically come a few points cheap to theoretical value, because the only way to move billions of dollars of paper in a single evening is to price it at a discount. The arb funds are buying vol below fair.

This explains the last piece of the pattern, the one that confuses equity holders every single time: the stock usually drops the morning a convert is announced.

Every fund that expects an allocation is shorting its delta at the same moment. That drop is the hedge going on, live and in public.

The Trade, In Dollars

Say you took down $1 million face of the CoreWeave 1.75s of 2032 at issue. The position looks like this:

1. Buy $1 million face of the notes at par.

2. Count your shares. At 8.3612 per $1,000, the position controls 8,361 shares of CRWV if converted.

3. Short the delta. The model calls the embedded option something like a 55 delta at pricing, so you short roughly 4,600 shares at $92, about $425K of stock against your $1 million of bonds.

4. Collect the carry. The 1.75% coupon, plus a positive rebate on the short-stock proceeds. During the zero-rate decade this second leg paid nothing; now the short leg itself is a source of yield.

5. Rebalance as the stock moves and the delta moves with it.

At this point you own the convert, you’re flat the stock, and you’re clipping carry from two directions.

And step 5, the boring-sounding one, is where the actual money is.

The Swings Are the Salary

A delta-hedged convert is a long gamma position, and long gamma has a very specific personality:

Read more

This past June 26th, the Nasdaq Closing Cross printed roughly $334 billion of stock in one shot. Nothing else all year came close.

The Closing Cross is Nasdaq’s closing auction where instead of stocks trading continuously the way they do all day, every buy and sell order queued up for the close gets matched at one single price per stock, all at once.

What’s strange is that the date of that $334 billion trade had been public for years.

It happens because FTSE Russell rebuilds its stock indexes every June on a published calendar, an event called the reconstitution, and roughly $11 trillion is managed against Russell indexes. When the lists change, the money has to move. All of it, at the same moment.

Everywhere else in markets, participants go to absurd lengths to hide their orders: slicing them into crumbs, routing them through dark pools, randomizing their timing. But here, the biggest order of the year is announced in advance, sized in advance, and executed at a pre-scheduled second.

So naturally, it raises the question that every trading desk eventually asks:

“If the biggest order of the year is announced in advance, who gets paid for standing in front of it?”

For about thirty years, the answer was basically anyone who bothered.

Today, standing in front of that order is one of the largest-scale, most sophisticated trades in markets: probability-weighted baskets spanning hundreds of events a year, hedged to the dollar, levered 5 to 10x inside the biggest funds on earth.

So we’re going to walk through exactly how it’s run, from scratch and down to the final details, assuming you’ve never touched market structure before. And by the end, you’ll be able to run a baby version of it yourself.

The Most Obedient Buyers on Earth

An index fund is exactly what it sounds like: a fund that copies a list.

The S&P 500 is a list of 500 big American companies, each assigned a weight based on its size. An S&P 500 index fund takes your money and buys all 500 in exactly those proportions.

The manager has zero discretion. If the list changes, the fund changes.

And the manager is graded on a single number: tracking error, which is how far the fund’s return drifts from the index’s return. A fund that returns 9.98% in a year the index returns 10.00% is doing its job.

A fund that beats the index by 2% is just as broken as one that trails it by 2%, because the product being sold is the index itself.

Now, the wrinkle that creates this entire trade: when a new stock joins the index, the index itself incorporates it at the stock’s closing price on the effective date (the date the change officially takes effect).

So a fund that buys at exactly that closing price, whatever that price turns out to be, shows zero tracking error. Buying earlier and cheaper is riskier for the manager: if the stock dips before the close, the fund lags the index and someone has to explain why.

The career-safe move is to buy at the close, at the closing price, every single time.

Conveniently, there’s a venue built for exactly that. At 4:00 PM the exchange takes every order flagged market-on-close (an MOC order says “fill me at whatever the official closing price ends up being”) and matches all of them in one giant batch.

That batch has been swallowing more of the market every year. In Q2 2024, about 9.4% of all the dollar volume traded in American stocks went through closing auctions, a record, and on big rebalance days it runs closer to 20%.

So when an index changes, we know who has to trade (every fund copying the list), which direction, and precisely when: the closing auction on the effective date.

The Free Lunch Years

In the 1990s, the trade this created was almost insultingly simple.

S&P announces that a stock is joining the S&P 500. You buy it that evening. You wait the handful of days until the change takes effect (the median gap between announcement and effective date has been about 4.8 trading days), then you sell to the index funds who are forced to buy from you.

That earned roughly +7% in about a week, on average, for a decade.

None of this was a secret, either. Shleifer and Harris & Gurel documented ~3% announcement-day pops back in 1986, and academics gave it a name: the index effect.

The cleanest accounting of what happened next comes from Greenwood and Sammon’s paper, The Disappearing Index Effect. Additions to the S&P 500 returned, from announcement to effective date and adjusted for the market: +3.4% in the 1980s, +7.3% in the 1990s, +5.1% in the 2000s, and +0.8% in the 2010s, which is statistically indistinguishable from zero.

Deletions were even harsher on the way down: -4.6%, then -16.1%, then -12.4%, then -0.6%.

The free lunch was real for decades, and then it wasn’t (chart below).

So the naive version is dead. Which leaves a puzzle:

“The forced buying didn’t shrink. Indexed money is bigger than it has ever been. So where did the return go?”

Hold that thought.

To answer it, you first need to understand the flow.

The Whole Trade Is Arithmetic

Roughly $16 trillion is benchmarked to the S&P 500, and about $10 trillion of that is passively indexed, per S&P’s own survey of assets. That $10 trillion is the obedient money from earlier: it must buy whatever joins the list.

And the size of that forced buying is computable, in advance, by anyone:

indexed dollars x index weight = dollars to buy

Divide the dollars by the share price and you get shares to buy. Divide the shares by the stock’s ADV (average daily volume, how many shares change hands on a normal day) and you get the number that actually matters: days of ADV.

Days-of-ADV is the unit of violence. A stock where the forced buying equals 8 days of normal trading is a fundamentally different event from one where it equals 0.5 days. The first one has to move the price; the second gets absorbed without a ripple.

There is no forecast anywhere in that arithmetic.

Deletions run the same math in reverse. The catch on deletes is that profiting from one means shorting the stock, which means borrowing shares first (your broker locates shares from a lender, you sell them, and you buy them back later to return them). The borrow is a constraint as names everyone wants to short get expensive to locate, or impossible.

The data you need for all of this is free or nearly free:

Read more

There are well-established paths that are pretty linear:

1. Momentum

   1. Isolate a liquid universe that demonstrates strong investor appetite (e.g., weekly optionable, high average notional volume)

   2. Create a metric for describing each stock’s returns

      1. Combining the Sharpe ratio and beta over a lookback can be used to create a rank-able momentum score, of sorts.

   3. Long the n highest scores, short the n lowest.

2. Relative Value

   1. Long high-quality assets

      1. Large market cap, healthy balance sheet, strong free cash flow

   2. Short poor quality assets

      1. Tiny market cap, debt-laden balance sheet, declining/negative free cash flow

   3. Hedge out the vol

      1. A beta hedge is typically used due to higher volatility in smaller cap names, so you may often end up shorting $2 of the low-quality assets for every $1 of high-quality you long.

And the list can go on, and on, and on.

The combined result is a return profile so stable you can build an entire business on it: lever it, sell it to allocators, pay bonuses off it.

So, there was one question we kept coming back to:

Why isn’t anyone running this playbook in crypto?

The asset class is volatile, the average participant is expressing a hunch, and the perp market lets you short basically anything with a ticker. If there were ever a venue where the multi-strategy model should print, it’s this one.

So today, we’re going to walk through exactly how we built ours: the sleeves, the combination machinery, and live execution across roughly 150 tokens at a time.

These Aren’t Stocks, Kid

When we first started exploring trading crypto systematically, we did what everyone does: long/short momentum.

The universe was liquid USDT-perps with at least $1M in daily volume and a 30-day realized vol of 30% or higher, rebalanced weekly with a one-week hold. Sensible, boring, the same recipe that’s worked in every asset class for decades.

And it worked here too, technically. Long momentum (28-day, risk-adjusted) came in at a weekly Sharpe of 0.71. Short momentum did better, at 1.20.

Net profitable, sure, but nothing you’d want to stake an operation on: a sub-1 Sharpe in an asset class this wild means long stretches underwater waiting for the math to catch up.

The traditional move at this point is to grind. Tweak the lookback, add filters, keep optimizing until the backtest is beautiful and the live results are anything else.

We went the other direction. Instead of perfecting one strategy, we started collecting them.

First came shorts on tokens with increases in circulating supply (unlocks, emissions), the same dilution logic we run in equities: someone is about to sell, so get in front of it.

Now, in crypto, it’s insanely difficult to ascribe explanations to returns as there aren’t any earnings reports or fundamental comparables, and the idiosyncratic context lives on X threads you’d never follow.

So, in order to expand our catalog of possible strategies, we did something we’d never do in equities: pure math.

To keep things at a high level, this mostly looked like:

• Probability based: Under a basic assumption that daily return direction should be about 50/50 up or down, then in theory, if an asset has gone up n times in a row, it is probabilistically unlikely to have that same direction in the next sample.

  • Of course, the issue with this is that it ignores things like autocorrelation, volatility clustering and so on, but remember, the goal was just to expand our catalog of uncorrelated strategies.

  • The downward version of this (if sessions_up > n, short) had a weekly Sharpe of 0.35, while the upward version (if sessions_down > n, buy) had a weekly Sharpe of 1.20.

• Implied Information: If a token has a +/- n standard deviation move, just follow the direction.

  • For instance, if asset A has an annual vol of 30% and it suddenly goes up 30% in a single session, just long it, and vice versa.

  • This produced a weekly Sharpe of 0.96.

Every one of these was generally net profitable, even after fees. And every one of them had a Sharpe we’d never build a business around: the best single sleeve topped out around 1.24.

Alone, none of them justified the infrastructure.

So, we tested running them all at once.

Silo, Silo, Silo.

The first thing worth checking before combining anything is whether the sleeves are actually different bets, or just the same bet five times over.

If every sleeve is secretly long crypto beta, stacking them accomplishes nothing.

So, we ran the correlation matrix on weekly sleeve returns, and the numbers were genuinely better than we expected (chart below):

• Long momentum vs. short momentum: -0.34

• Down-streak buy vs. short momentum: -0.62

• Long momentum vs. up-streak fade: -0.48

Negative numbers are the good stuff. When the momentum shorts are bleeding in a squeeze, the down-streak buys tend to be getting paid, and vice versa. One sleeve’s bad week is routinely another sleeve’s good one.

The quiet star was gap follow (implied information): roughly zero correlation against everything else (-0.14 to +0.10 across the board). It has no opinion on what the rest of the book is doing, which makes it the cleanest diversifier in the stack.

Now, you can’t just run five strategies side by side and call it a portfolio. Two problems show up immediately.

The first is overlap.

At the weekly rebalance, two sleeves will regularly want the same token, sometimes in opposite directions.

A token that’s down 3 days straight is a buy for the streak sleeve and, often enough, a short for the momentum sleeve.

Paying fees to be simultaneously long and short the same token in two different sleeves is a great way to donate to the exchange. So, we wrote a hard rule:

1. Each sleeve builds its basket independently at the rebalance.

2. If two sleeves claim the same ticker, the sleeve with the historically higher Sharpe trades it.

3. The ticker is dropped from the losing sleeve’s basket entirely. The book never holds the same token long in one sleeve and short in another.

The second problem is weighting. Crude equal weighting across components sounds fair until one wild name with triple-digit vol is suddenly steering the whole operation.

So components are weighted by inverse volatility, computed walk-forward on trailing history only (no lookahead), with equal weights early on until a name has enough history to earn a real estimate. The sleepy names get size, the wild ones get trimmed.

With the collision logic and the weighting in place, we ran the combined book across all 61 weekly rebalances (chart below):

• Gross Sharpe: 2.68

• Net, at 30bps per side: 1.97

• Net, at a punitive 50bps per side: 1.50

That comes out to an annualized +70.5% on 26.1% vol, with a max drawdown of -9.9% and a realized beta to BTC of -0.05. Roughly double the best single sleeve, on a book that genuinely does not care which way crypto goes.

To be fair: 61 weeks was a thin sample and the cost model is a flat 30/50bps per side with no funding drag on the shorts.

Can We Actually Trade This?

When we’re running a research experiment, it’s never for a “hmm, that’s interesting”. We do this entirely to actually realize profits, so after quadruple-checking our calcs, we began setting up a live, production version.

As we’ve mentioned before, accessing crypto perps as a U.S. citizen can be a nightmare, but eventually, we set up an account at Bitunix (no affiliation) and integrated their API.

The book holds 150+ individual tokens long and short at any given time, which puts manual execution completely out of reach; nobody is clicking through 150 order tickets every week without eventually fat-fingering a short.

Now, the infrastructure is not sophisticated, and we mean that sincerely:

• At each weekly rebalance we loop through the valid ticker set, take each name’s weight, and submit a limit order for it.

That’s it.

Some of those orders rest unfilled for a few minutes, but since we’re not moving at a huge clip, this has worked fine so far. In general, the fills have landed close enough to the marks that we haven’t felt the need to build anything more clever.

A few notes from official launch:

• Inverse-vol weighting shrinks outliers so that no single blowup hurts you, but also means you don’t get the full kick of just equal-weighting.

• The book is often net short by 30% of notional, despite being “market-neutral”.

  • For instance, if gross risk is $100,000, the net short of the full portfolio is often ~-$30,000.

• Despite being net short, the uncorrelated and dispersed nature of the components results in an average beta of -0.05.

• Realized transaction costs have been 2-5bps; the exchange charges 0.02% for resting limit orders and 0.05% for taker orders.

  • Although we only send limit orders, when the bid/ask moves in the second before execution (orders sent via REST API), it ends up taking liquidity and auto-filling, resulting in the higher fee.

• Over the first 4 rebalancing periods, the net unlevered return has been ~6.4% across the entire book.

Final Thoughts

We’ll keep monitoring this over the coming months; hopefully by then, the real question will become scalability:

How much capital can these perp books absorb before our lazy resting limit orders stop getting filled at prices we like?

After all, we’re not using any leverage at all, but for each liquid token in our book, we can push things all the way to 20x on a cross-margin basis. At that point, we’d have to get actually serious about proper execution, but the upside is enticing.

While this was a great next step in a new-to-us asset class, the bigger lesson was a shift in orientation.

For years we were ultra-focused on making single strategies the absolute best they could possibly be: more models, better features, another round of tuning to squeeze a 1.1 Sharpe toward 1.4.

When switching to a portfolio management view instead, we start asking what a strategy does to everything around it. A 0.35 Sharpe streak fade looks like a waste of a slot until you notice its -0.62 correlation to the short book, at which point it becomes one of the most valuable things on the desk.

Historically, we would’ve just tossed strategies like that in the recycle bin.

Without getting too ahead of ourselves, it makes us wonder what would happen if we looped back through everything we’ve ever run/tested and just put it all together?

• For instance, in 0-DTE index options, what if we overlaid the short volatility strategy with an intraday momentum sleeve, then possibly another long volatility leg? What if we added in 3 more purely quantitative techniques and merged those in too?

  • Again, each leg might not have the 2+ Sharpe we need to feel safe, but if they’re legitimately uncorrelated to each other, what if combining them together actually does?

If we can have a multi-strategy desk in every asset class, running all the time, each balancing the other out… well, you can pretty much see the picture yourself.

As always, thank you for reading, and we hope to see you in the next one.

Traditionally, that’s true as such a profit mechanism naturally lends itself to a “if it was easy, everyone would be doing it” sort of defense.

However, it’s far from the only place where that kind of no-risk edge exists and more importantly, outside of HFT, this kind of edge is “easier”.

Being in the quantitative trading space, it’s largely your job to be at the very minimum, aware of these kinds of trades so that even if you don’t trade it directly, it can give you a baseline idea of where else you might apply it on your own.

So today, we’re going to be showing you first-hand what it takes to capture a specific type of arbitrage you might be loosely familiar with:

The Treasury Basis Trade

Now, we don’t expect everyone to be bond market experts, so to understand this idea, it'll help to walk through an example.

1. You see a 10-year treasury trading at a yield of 4% ($96)

2. At the same time, the CME 10-year treasury future is trading at a yield of ~3.99% ($96.05)

3. So, you short the future and simultaneously buy the bond, collecting a theoretical spread.

   1. Naturally, even with perfect execution, this spread isn’t worth much, so you borrow.

   2. Instead of running this trade with a million bucks, you hit the repo market with the bond you bought acting as collateral, then you lever it up 50-100x.

4. On the futures expiration date, you deliver the bond you bought and close out the trade.

5. Repeat.

With such an attractive risk-profile and relative simplicity, one naturally begs the question:

Could you actually run a version of this yourself?

Well, sort of.

To better answer that, we need to break the trade down into the pieces you’d actually have to assemble yourself.

Strip away the jargon and the professional version is really just three things stacked on top of each other:

1. You own a specific Treasury.

2. You short the matching future against it.

3. You finance the trade so the tiny spread is worth the effort.

The first two, you can do from your couch. The third is where the whole thing lives or dies, so we’ll save it for last.

One Account, Two Markets

The first hurdle is boring but real: you need a single account that can touch both the cash bond market and the futures market.

That rules out most of what a normal person already has. Your TreasuryDirect account can buy the note, but it can’t short a future. Your average stock app can’t do either leg properly.

What you want is a broker that clears futures and gives you access to secondary-market Treasuries, with Interactive Brokers being the obvious pick for a retail setup.

Leg One: Own The Bond

Now, you don’t just buy any 10-year and call it a day.

The CME future doesn’t track “the 10-year” in the abstract. Whoever is short the contract has to deliver a specific bond from an eligible basket at expiration, and they’ll always hand over whichever one is cheapest to deliver (the CTD).

The eligible basket holds a handful of different notes, each with its own price, coupon, and maturity. The contract puts them on a level playing field using a conversion factor for each one, and the short simply runs the math on all of them: buy the bond today, deliver it into the contract, and see which one leaves the most money in your pocket.

The winner of that little contest is the cheapest to deliver.

So if you want your long leg to actually mirror what the future is pricing, you buy the CTD issue itself, not a random note off the run.

Practically, that means pulling up the deliverable basket for the contract you care about (/ZN for the 10-year), identifying the current CTD, and buying that exact CUSIP in the secondary market.

You’re now long the same bond the entire futures market is implicitly quoting.

Leg Two: Short The Future

This part is genuinely a few clicks.

You short one 10-year Treasury future, each contract representing $100,000 in face value. The margin the exchange makes you post is tiny relative to that notional, usually a couple thousand dollars, which is your first taste of the leverage that makes this trade work.

The only real subtlety is how many contracts to short, because it isn’t one future per $100k of bonds.

The bond and the future don’t move dollar-for-dollar, so you size the short to match the dollar duration (DV01). You scale by the CTD’s conversion factor so that a one-basis-point move in yields moves both legs by roughly the same dollar amount:

contracts to short ≈ (face value of your bond / 100,000) × conversion factor

Get that ratio right and the two legs cancel on rate moves. Get it wrong and you’ve accidentally put on a directional bet on yields, which is exactly the thing to avoid.

At this point, congratulations!: you are long the cash bond, short the future, and pocketing that theoretical ~0.05% spread.

Can You Even Get the Data?

That word “theoretical” is doing a lot of work, and the first reason is almost embarrassingly basic: can you even see the number?

You need four things to compute the basis, and three of them are refreshingly public:

• The deliverable basket and its conversion factors come straight from the CME.

• The financing rate you’ll benchmark against is SOFR, sitting on FRED and updated daily.

• The futures prices are on whatever quote feed you already have.

The fourth input is where it gets hard: live, accurate cash Treasury prices.

The cash Treasury market is over-the-counter (OTC), so there’s no consolidated tape like equities have.

The good pricing lives on Bloomberg terminals and dealer runs, while the retail-accessible quotes tend to be wide, stale, or both.

This matters because the entire edge lives in a spread of a few cents. If your cash bond price is off by more than the basis you’re trying to capture, that number you’re calling an edge is just your own data error.

Reading the Signal

So, how do you actually know when the basis is worth putting on?

A couple of terms you’ll bump into here, and they’re simpler than they sound.

The gross basis is the raw price gap between the cash bond and the future, adjusted by that conversion factor.

Net that against the carry you earn from holding and financing the bond, and you get the net basis.

Take the same idea and express it as a rate instead of a price, and you get the implied repo rate: the return you’d lock in by buying the cash bond, delivering it into the future, and collecting the convergence.

Now here’s the whole intuition, and it’s the one thing to walk away with.

Set that implied repo against your actual cost of financing the bond.

→ Implied repo above your financing cost = the trade pays you to carry it.

When the market hands you a higher implied return than it charges you to borrow, you capture the difference, geared up dozens of times. When those two rates cross, the printer quietly turns into a slow bleed.

That single comparison is the entire signal and it’s exactly why this isn’t free money for everyone. The implied repo you can actually capture depends on the repo you can source.

A prime-brokered relative-value fund and a retail account are looking at two different trades, because they finance at two very different rates.

Now, About That Leverage

Here’s where the couch version runs into the wall.

Remember, that spread is tiny. On an unlevered basis, capturing five hundredths of a percent is a rounding error that commissions will happily eat for lunch.

The pros solve this in the repo market. They take the bond they just bought, post it as collateral, borrow back almost all the cash they spent, buy more bonds, and repeat until they’re levered 50 to 100x.

You, sitting at home, might have a bit of trouble doing this.

Repo is an institutional counterparty market, so the minimum sizes are enormous, and nobody is opening a tri-party repo line for $100k. That single door being closed is the real reason this trade “belongs” to funds and doesn’t get spoken about much.

However, not all hope is lost.

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The volume is insane. Fin-Twit is calling for a 5-bagger. You’re up big and feeling like a genius.

Two trading days later, a filing for new shares hits the wire.

The company is selling 7 million new shares into exactly the strength you just bought. By the close, you’ve given back the entire move and then some.

You got diluted.

If you’ve spent any time trading small-caps, you’ve likely lived some version of this. When it happens, most attribute it to bad luck, a rug pull, or a thing that just happens.

What they don’t see is that the whole sequence is one of the most repeatable, predictable, and mechanically extractable edges in the entire market.

So today, we’re going to take it apart from first principles: how dilution actually works, why price always moves the same way after it, and then we’ll walk through a real-world trade example from catalyst to close using the same dilution data we run at Alphanume.

With that covered, let’s get right into it.

They Sold A Little Stock, What’s The Big Deal?

The first stock market officially opened in 1602 and since then companies have devised countless ways of raising more capital to fund and grow their operations. Whether it’s tapping the bond market, issuing commercial paper, taking on a line of credit, whatever.

However, when all of those more respectable avenues of capital are closed, there exists a worst-case, emergency option: Dilution.

In sum, share dilution is basically just creating new shares that didn’t exist before, then selling them on the open market.

This is the absolute last-ditch option for a few reasons:

• It tells the market you’re out of options.

  • Bonds, credit lines, commercial paper, those all require someone on the other side to look at your books and decide you’re good for it. When a company skips all of that and just prints shares to sell, it sends the message that nobody else would give them the money on reasonable terms.

• It punishes the people who already believed in you.

  • Every new share that didn’t exist before makes every existing share worth a little less. Same company, same business, but more slices of the pie. Shareholders who bought in early get quietly watered down to fund a raise they had no say in.

• It signals management thinks the stock is expensive, not cheap.

  • A company buys back its own shares when it thinks they’re undervalued. Selling new shares is the exact opposite trade. When insiders choose to issue stock instead of repurchase it, they’re effectively telling you the current price is a good price to sell at, and they should know better than anyone.

• There’s no obligation to do anything good with the cash.

  • Debt comes with covenants, schedules, and people watching how the money gets spent. Dilution proceeds come with none of that. The company can burn it keeping the lights on for another two quarters and owes no explanation, which is usually exactly what’s happening.

As we’ve covered in our A Junior Quant’s Guide to Event-Driven Trading, this structure is the lifeblood and oxygen for a systematic event-driven trading approach:

That arrow diagram is abstract, so here’s what it looks like on a real name. We’ll go deeper into the data later, but to start:

On June 1st, 2026, AEVEX Corp. (AVEX), an $8b drone maker submitted an S-1 filing indicating that they’d be issuing 5 million new shares after a 50% rally in the prior week.

Not even one week later, 40% of the company’s value was erased:

Now, one textbook example doesn’t make a strategy. So before we go shorting every S-1 that crosses the wire, there’s a wrinkle in how these filings actually work.

The Nuance

To trade this properly, you first have to understand how those new shares actually travel from a niche filing to the tradeable open market.

First, it starts with the registration.

When a company wants to sell new shares to the public, it can’t simply dump them on the tape. It first has to register them with the SEC, and that registration is the document you’re watching for:

• Bigger names with a long reporting history use an S-3, a reusable shelf they sell from over time; the small and micro-cap names often don't qualify, so they file an S-1 instead. Either way, the filing is the company raising its hand and saying, on the record, that more supply is coming.

The moment a dilution filing hits the wire, sophisticated participants know the float is about to expand, and they start selling ahead of it. You’ll often see the initial leg down within minutes of the document posting, well before a single new share has actually changed hands.

However, and it’s the part you have to know:

Not every S-1 is dilutive.

At root, an S-1 is just a registration form.

Plenty of them are completely benign: registering shares for an employee plan, allowing an early investor to resell an existing stake, or covering a routine shelf that may never get used. None of that creates new supply hitting the open market.

If you short every S-1 that crosses the wire on reflex, you’ll get run over by the ones that were never going to dilute anyone. So the filing has to actually be read.

You’re looking for the specifics:

• Are these newly issued shares or existing ones being resold?

• Is the company itself the seller?

• How many shares relative to the existing float?

A company with a billion shares outstanding registering 50,000 of them is inconsequential, but a nano-cap registering 3 million new shares against a tiny existing float is a trade.

Next, once you’ve confirmed it’s genuinely dilutive, there’s a second clock running: the effective date.

A registration alone doesn’t let the company sell anything the instant it’s filed. The SEC has to declare it effective first, and only then can the shares legally be sold into the market.

You can think of the two filings as:

1. “We’re going to do something hugely -EV soon, maybe, probably.” (S-1)

2. “You know that bad thing we mentioned? It’s official now.” (EFFECT notice)

So with dilution, you’re effectively trading a sequence with two distinct punishment points.

The Part Where We Make Money

Alright, now that you have the base foundations, we can finally get into actually putting on a trade.

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“There’s no edge in vanilla options anymore. The surfaces are too clean, the market makers are too good, and you’re not finding anything in 2026 that a desk in Chicago didn’t price five minutes ago.”

If you spend any time around trading discourse, you’ve likely heard some version of this take. It’s confidently delivered, usually by someone who once tried selling weekly strangles and got tagged.

And to be fair, it’s not totally wrong.

SPX options in 2026 really are as close to efficient as it gets. The days of catching obvious mispricings on a Bloomberg terminal at lunch are over, and they have been for a while.

What gets lost in this take though, is that “mispriced” was always the wrong word for it.

At the end of the day, professional options trading isn’t about hunting quotes that are wrong against some objective truth, but rather about taking the public price, comparing it to your own internal price, and putting on a trade if the difference is big enough.

So, it’s less “this option is objectively priced wrong” and more “this option is cheap/rich relative to this specific assumption”.

Unfortunately however, the whole process of pricing your own options around a bespoke view is extremely vague if not totally inaccessible to most non-MM traders.

So today, we want to crack-open that process and show you first-hand how you can intuitively use sophisticated options models to not just price options “better” than what you see on screen, but also how you generate some of these views and assumptions to begin with.

Without further ado, let’s get right into it.

It Goes Way Deeper Than Black-Scholes

To begin, we have to understand why anyone even bothers to use anything other than Black-Scholes in the first place.

An option model, at root, is a story about how the underlying behaves between now and expiration.

Black-Scholes tells the clean version where markets are basically random walks and implied vol tends to stay the same throughout its life.

As a description of how markets actually behave, though, it’s not really true:

• What about stocks with a momentum factor, where the forward returns are more predictable than just a random walk?

• What about those currently undergoing extreme realized volatility, where implied vol is either way too high or way too low?

So, over the last fifty years the industry has constantly innovated to create new models that better reflect reality in ways the textbook Black-Scholes doesn’t.

To see why this matters, here’s the same 90-day 105-strike put on a $100 underlying, priced under five different models with identical observable inputs (5% rate, no dividends, 25% vol):

Two commonly used industry models, Barone-Adesi-Whaley and Merton Jump-Diffusion, are demonstrable standouts compared to the typical Black-Scholes price, and for a good reason:

• Barone-Adesi-Whaley model

  • Put simply, this is the Black-Scholes model, but with a small premium to cover the early-exercise risk in American Options.

    • SPX and index options can use BS since they’re cash-settled and have no early exercise risk, but single stock options have the added risk of being able to be exercised for shares at any time.

• Jump-Diffusion model

  • In sum, this model is used when prices trade discontinuously rather than in a smooth drift like assumed in Black-Scholes. The left-tail risk is also priced higher, so OTM options carry a higher than average premium.

    • If there is an upcoming event (earnings, regulatory ruling, etc.), the price can have 1 day where it abruptly goes in either direction, so this model comes in handy.

So, right off the bat, in professional options trading, the model used is often very case-dependent.

When pricing American options, you might use the BAW model, but when pricing those options knowing that a huge event is on the horizon, you might layer on a Jump-Diffusion model.

Now, this might still seem a bit theoretical, so to really cement the understanding, let’s walk through an actual trading example.

How Do You Make Money With This Stuff?

Okay, it’s Wednesday at 12:00 PM and we’re discretionary vol traders looking for cheap options.

First, we need a name.

Scrolling through a screener, we land on CVNA.

A high-quality short report dropped on it three days ago, we've read the report, we think most of it is fair, and we're roughly aligned with the thesis. The stock is down about 2% on the week and volume is unremarkable.

The options market is pricing things like nothing happened.

That last part is the trade.

The short report is sitting in plain view, and either the market digests it over the next quarter and the stock leaks lower, or the next earnings call poses a tough enough question that the bid evaporates faster than that.

Either way, we think realized vol on a 6-month horizon is going to come in higher than the surface is pricing.

The cleanest way to express this without taking a directional view is the 6-month at-the-money straddle. We don’t need to be right about which way; we just need to be right that something happens.

So, we pull up our Options Pricer, select the Merton jump-diffusion model (we think a large sudden move is likely), and input the metrics as it’s trading now:

Quick clarification on some terms:

• Jump intensity λ: how often jumps happen, in jumps per year on average. λ = 2 means we expect two material discontinuous moves per year, on average. Higher λ means jumps are more frequent.

• Jump mean μ_J: the average size of a jump given that one occurs, expressed in log terms (so roughly a percentage move). μ_J = −0.10 means jumps tend to be downward, around −10% on average. A symmetric view would set μ_J = 0; an upside-biased name (think a biotech awaiting an FDA decision) might set it positive.

• Jump dispersion σ_J: how much variation there is around that mean jump size. σ_J = 0.20 means that when a jump happens, it’s not always exactly −10%; it could be −5% or −25% or +5%, with the spread around the mean governed by this parameter. Larger σ_J means jumps are more variable in magnitude.

So, right off the bat, our fair view of that 6-month call option is a price of ~$14.60.

Now, we check what the option is currently trading at on the open market:

At the time of capture, the 6-month call was trading at around $14.15 against our fair value of $14.60. Not a marked difference, and on its own, not enough to put on a trade.

But there’s a major caveat before we stop here.

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What makes this case notable beyond the existing popularity of the founder, is that multi-strategy funds aren’t supposed to do this.

The entire goal of a multi-strategy approach is to run multiple uncorrelated strategies to create a steady stream of returns in any given regime; long momentum here, short junk bonds there, long some vol over there, whatever.

When news of this first broke out, Eifert, the face of this, gave his full perspective on what happened:

It was shockingly honest and transparent, but for the large swaths of people not familiar with esoteric vol arb strategies, it did sound a bit jargon-y.

The entire story is actually pretty fascinating, so today, we want to peel back a few layers and walk you through what actually happened, in a way that you can intuitively grasp.

We’ll start with a high-level look at what volatility arbitrage even is, go into the strategies the desk ran, then show you exactly where things went wrong.

By the end of this you’ll not only learn more about a fascinating, but under-mentioned corner of quantitative trading, but you’ll also get a clear view on what exactly not to do, should you be in a similar position.

So, with that said, let’s get right into it.

WTH Is “vol arb” Anyway?

The birds-eye view of vol arb is essentially selling expensive derivatives and simultaneously buying cheap derivatives.

We know that sounds vague, so let’s walk through an example.

1. You spot two related instruments priced differently

   1. Two stocks around $100, same industry, similar products, historically similar movement.

   2. A one-month at-the-money option on Stock A implies 40% volatility; the same option on Stock B implies 28%. The market is forecasting far more turbulence for A than for B.

2. You check whether the gap is justified

   1. A difference like that usually exists for a reason: earnings, a lawsuit, a pending FDA decision, a takeover rumor. If a real catalyst explains it, there is no trade. If you check and find nothing, then the gap might be a genuine dislocation.

3. You sell the expensive option and buy the cheap one

   1. Short the Stock A option, collecting the rich 40% premium. Long the Stock B option, paying the cheaper 28%. You are now short expensive volatility and long cheap volatility, betting only that the 12-point gap converges.

4. You hedge out the directional exposure

   1. Selling options leaves you with unwanted exposure to which way the stock goes. You buy or short shares of each name until the position has no directional tilt (delta-neutrality) and re-hedge as prices move. What remains is a clean bet on volatility itself.

5. You collect the spread as the gap closes

   1. With no catalyst to justify the spread, Stock A drifts like a normal stock and its realized volatility comes in well below the 40% you sold. Stock B does roughly what its 28% implied, so the option you bought costs you little.

   2. The expensive side was overpriced and you were short it; the cheap side was fair and you were long it. The spread converges, and the convergence is the profit.

Of course, this was a simplified example, but it’s the core basis of what’s generally referred to as volatility arbitrage.

Eifert’s account is unusually detailed as it names the exact trades they took.

So with that covered, the exact strategies they were running will be a lot easier to understand.

What On Earth Were They Actually Doing?

Term Structure Trades

“long cheap gamma at the front of the curve, short expensive volatility in the belly of the curve, and long again at the back.”

Protection has a term structure, like bonds. Plot implied vol against time-to-expiry and you get a curve: cheap and jumpy at the front, richer and more stable at the back.

QVR was short “the belly”: the two-to-four-month region where structured-product hedging oversupplies volatility, and long the cheaper wings further back.

Being short the belly pays two ways: the rich vol you sold decays, or the market moves and your long front-month gamma fires.

So you’re covered in a quiet market or a volatile market, in theory.

What became a problem was that the belly they were short went, in Eifert’s words, “turbo-bid”, and spiked away from the rest of the curve, driven by inflows into VIX-linked products.

The front-month gamma leg (the hedge) only pays off on movement, so with realized volatility on the floor it sat there doing nothing. The core leg lost, the other was inert, and the hedged trade was just a loss.

Skew

“long the massively over-supplied long-term downside on the back of autocall issuance... short medium-term downside against it, and long short-dated crash puts.”

Skew is the price difference across strikes: downside puts trade richer than upside calls because everyone wants crash protection.

QVR’s skew book had three legs across maturity: long long-dated downside, short medium-term downside, long short-dated crash puts. Long the wings, short the middle. The same shape as the term structure trade, just in a different product.

The leg that needs explaining is the long-dated one, because of why that downside was cheap: autocalls.

• An autocall is a structured product banks sell to yield-hungry investors; you get a fat coupon, say 10%, as long as an index holds a level: above SPX 7000 by Dec 2027 and the deal pays out.

• Below it, the deal rolls another year and tries again. Fall through a deeper barrier, though, say -30%, and you eat the full loss like you owned the index outright. The investor is selling crash protection for yield, which leaves the bank holding a mountain of long-dated downside it has to hedge.

That hedging floods cheap long-dated downside into the options market, which is exactly the leg QVR was buying.

These were different parts of the surface, in different products entirely, but both were short medium-term volatility, and both leaned on autocall-hedging flow staying calm.

When medium-term vol went bid, it hurt both the term structure book and the skew book.

Short Vol, Short Delta

“short volatility (via put spreads on VIX) versus short delta (via ES futures).”

Short vega is short volatility; short delta is short the market. The pairing is historically beautiful: when the market sells off, vol spikes, so one leg pays while the other takes the hit. The hedge depends entirely on vol and direction staying linked.

Then, the Iran-Israel conflict escalated:

Implied vol surged as hedges got bid. But the market didn’t sell off, not really.

Investors panic-bought protection while holding their equities, so volatility rose without the market falling.

Short vega lost on the rising implied vol and short delta lost on a market that drifted up.

Reverse Dispersion

“We started building a reverse dispersion position at all time high spread levels around 17.5 (3-month tenor). That spread went as high as 22.”

Dispersion is a trade around correlation. Index volatility depends on how much single stocks move and how much they move together.

The vol spread, single-name vol over index vol, is wide when correlation is low. QVR shorted that spread at a record-high 17.5, betting correlation would rise and the spread would compress.

It ran to 22 and a new record became a deeper record.

The painful part is what the loss meant:

• A short-spread position is a long-correlation bet, and rising correlation normally travels with cheap index volatility, which would have made the term structure and skew books print.

• Reverse dispersion was supposed to be the diversifier that paid when the others struggled, but instead it lost alongside them.

So, What Actually Went Wrong?

Now, these strategies are clearly very sophisticated, and most of the time, they work well enough to keep these shops in business. Each has a hedge on top of another hedge, where that hedge acts as a hedge against something else.

So, with all this hedging, one can naturally ask how a -28% loss just happens.

Well, it wasn’t a single bad trade gone wrong, but rather the same trade going wrong, sizing up, having it go wrong again, sizing up even more, having it go wrong again, and eventually having investors run out of patience.

Every position QVR held was a convergence bet: some spread had stretched away from its normal level, and QVR was positioned for it to snap back.

That phrase, snap back, is where the trouble started: the assumption that dislocations have to snap back eventually.

When a convergence trade loses money, it often looks like an opportunity for a better entry. A spread at 17.5 was attractive; the same spread at 22 is, on the same logic, even more attractive. So when there’s a losing month, you don’t cut, you just add more.

The bet got bigger as it lost, then it lost again, so the bet got bigger again.

No single month was a disaster, the fund just went down 7-9%, four times in a row.

Just a string of increasingly large bets that spreads would tighten, made into a market where spreads kept widening instead.

It’s entirely possible, even likely, that QVR’s positions will eventually be vindicated, but similar to a martingale strategy, no one has the privilege of infinite time or infinite capital.

So, What’s The Big Picture?

It’s tempting to file this under a risk management failure and move on; Eifert uses the phrase himself. But he is also careful to say it was “a much more nuanced one than just having a stupidly risky trade on and blowing up”.

He saw the correlation across his strategies shifting, attributed it to the right causes, and chose to “hold and increase positions,” waiting for “the reversion that would take us from down 15-20% on the year to up 20%.” He just needed the fund to survive long enough to flip it, and “four months of down 7-9% in a row” is, in his own words, “too much for investors to reasonably handle.”

The trades may yet be right and the spreads may still converge; it just won’t be QVR’s flagship collecting the payoff, as the redemptions came first and “the economics of a small/medium sized hedge fund business” stopped working.

So, if there was only one thing to take away from this, we can sum it up to:

Being right about the destination does not help if you get liquidated before it arrives.

or, put colloquially,

“The market can stay irrational longer than you can stay solvent.”

Interestingly, QVR is not the only desk exposed to this same risk. The same structured-product machine that distorted QVR’s belly is feeding the same suppressed-volatility, short-premium picture into a great many portfolios at once: autocall and QIS issuance, bank dispersion swaps sold to institutions with, as Eifert puts it, “very limited knowledge of the strategy themselves”, covered call ETFs, and so on.

A large share of the volatility world is holding some costume of the same trade, just on different desks, in different tenors, and through different marketing language.

So, if you came here to see how these more opaque corners of the market actually work, hopefully the term structure, the skew book, dispersion, and the rest feel a little less like black boxes now.

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