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Knowledge over noise
Finance | Marketing | AI

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@IMundyk @ITORRE Sure)

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Peter Kempthorne's research advisor helped build a volatility formula 6.2 times more efficient than the one most of Wall Street still runs on.

Standard volatility just compares yesterday's close to today's close. It throws out everything else the market showed you that day - the open, the high, the low all get discarded for one number.

Kempthorne's advisor, Michael Klass, and his classroom instructor, Mark Garman, co-authored the fix while Kempthorne was still a grad student at Berkeley - before he was teaching it, he was sitting a few feet from the people building it. Their estimator hit 6.2x efficiency by their own math. A later version pushed that to 8.4.

The Yang-Zhang model used today goes further still.

Here's the part that actually matters for anyone using this stuff: a 6x more efficient estimator means you can replace 20 days of price history with about 7 and land on the same precision.

That's not a rounding improvement - that's a third of the data doing the same job, which matters enormously if you're trying to react to a regime change before your competitors' slower estimators catch up.

But the more interesting failure isn't in the formula - it's in how people misuse it. When Kempthorne's own class ran a modern version through diagnostics, they found a spike in the residual noise sitting at exactly lag 21.

It looked like a signal.

It wasn't.

It was the 21-day window they'd chosen for the estimator, leaking back into their own results.

That's the trap efficiency doesn't fix: a better formula still can't tell you when you're reading the market versus reading your own assumptions back at yourself.

Every volatility model has a window size baked into it somewhere, and that window will eventually show up in your output pretending to be information.

[dnap @dnapway]

Sequoia partner Doug Leone reveals he got a root canal with no Novocaine to see if he was really a badass

"I had to get a root canal. And I had to go to a meeting right after that. I told the dentist, drill without any Novocaine.”

“My whole life, I wanted to see if I was a badass or not. Am I a fake badass that has this envelope nobody could tell, or am I really a badass on the inside?"

"He drilled and I jumped. And I told the dentist, I won't move again. And I didn't move again. I used the trick of thinking what it must be like to go to war and lose an arm or two legs. That's pain with consequences. Mine was pain with no consequences."

“It’s just nerve endings from there to my brain to my spine, f*ck it I’m not moving.”

"I had to go home and change my shirt because I was a puddle of sweat. But I didn't move. And so I love fear. It does wonders for me."

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@Svenchipo In practice, that 6–8x theoretical jump usually shrinks to maybe 1.5–2x. On thin stuff, plain realized vol off clean mid-prices is usually just safer

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@Svenchipo Markets run the same tax. The number is public. The flinch is the edge

[MoneyLore @Svenchipo]

Fold nine-six offsuit heads-up with 2.5 big blinds and you just lost 253 chips. Not by playing the hand badly. By folding it.

An MIT professor graphed the expected value of shoving all-in with that exact hand against every calling range a human opponent could realistically construct. The line never dipped negative. Not against tight players. Not against loose ones. Not even against someone calling with the mathematically optimal range.

Here's the part that breaks people's brains.

Folding nine-six offsuit in that spot loses you the exact same number of chips as voluntarily calling all-in with three-four against pocket aces. One of those decisions feels like patience. The other feels like a death wish. The math says they're identical.

Will Ma teaches this in MIT's Poker Theory course, Lecture 4. He breaks the expected value of a semi-bluff into two pieces: pot size times your probability of getting a fold, plus your probability of getting called times your equity when that happens. Simple enough to write on one line.

Then he maps real hand rankings onto a logarithmic curve — and gets an R-squared of 98%. That's a cleaner regression than most Wall Street quant models ever produce on real market data.

His students sat in that room and learned, with total mathematical certainty, that shoving garbage hands was a guaranteed long-run profit. Most of them still couldn't pull the trigger later that evening when a real tournament put real chips on the line.

Knowing the expected value was never the hard part. Acting on it while every instinct in your body is screaming "this hand is trash" — that's where almost everyone breaks, in poker and in every market that ever existed.

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@ITORRE agree man

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@aerockrose Hard to blame him for bowing out when the fun got squeezed out of it

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@GeFerion_ he doesn’t hesitate because the software is the lesson. The market is just the backdrop. That’s what a top looks like from the inside: the crash is already on the screen and the room is still teaching you which button to press

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@ITORRE The part that actually survives contact is the size. Everything else in that lecture is just showing why the printer lies

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$30 oil in 2009 gave Trafigura its best year ever.

Oil had peaked at $159 a barrel in 2008. The crash to $30 wiped out anyone who'd bet on higher prices. One trading house, sitting on 50 to 60 million barrels of storage, called it the best year in its history.

The futures curve explained it. Spot oil sat at $35. Oil for delivery 13 months out sat at $60. Borrow the $35, buy the barrel, pay for storage, sell the future at $60. Profit: $25 a barrel, minus about $3.50 in interest. Multiply that by tens of millions of barrels, and the math does the rest.

Alexander Eydeland, a Morgan Stanley quant teaching the trade at MIT, gave his students a smaller version of the same puzzle: storage for 1 season, spread worth $3. A trader bids $2.99 for the rights and pockets a penny.

Eydeland didn't bid on the storage directly. He sold an option on the same spread instead, one that only pays out if the gap moves against him. Priced with real energy volatility built in, that option came out worth $447, roughly 149 times the plain spread.

Nobody would ever buy an option like that. No market existed for it. It didn't matter. The same math behind Black-Scholes let Eydeland hedge his way to the $447 without ever finding a buyer.

He won the storage auction. His competitor was still counting pennies.

If this was useful - Like, Bookmark

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@Create_Profit Classic case of a high-income earner getting played by the illusion of expertise. Index and chill really is unbeatable

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@onryxxx welcome!

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MIT's Tarek Mansour, founder of Kalshi, explaining why a prediction market isn't a betting site.

An institution wants to hedge against Brexit. It never touches a betting market. A bookie sets the price alone, and no serious desk builds a hedge on a number one party controls.

The institution goes to an investment bank instead. Same flaw, different shape. The price gets negotiated bilaterally, one-on-one, over the counter. Still not a market price.

Tarek Mansour's exchange skips both. Traders quote against each other, an order book fills the same way it does for a stock, and the price comes from many independent positions instead of one bookmaker's number.

Then the distinction that decides whether the CFTC calls this a casino or a financial market.

A dice roll at a casino is risk manufactured for entertainment. Nobody was exposed to that risk before the dice left the hand.

Brexit isn't manufactured. The risk already sits on companies, currencies, and portfolios whether anyone bets on it or not. An exchange just lets someone holding that risk hand it to someone willing to take it.

Manufactured risk stays a casino. Risk that already exists, priced on an open exchange, becomes a financial instrument.

That's the whole legal argument for why Kalshi exists.

[Superior @andreysuperior]

https://t.co/rUN1cPDY65

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@onryxxx Pulled out of school at seven. Taught in a car by a glassblower. Ends up a professor at MIT, and the class is free for anyone. Wild

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@LeopoldTracker_ Leverage turned a drawdown into a fire sale, and Citadel got paid to be the bid of last resort, then dumped most of the risk before the bounce even finished arguing about who was "right"

[Lee Roach @leevalueroach]

Warren Buffett's greatest returns didn't come from Coca-Cola or Apple.

They came when he was a microcap investor. And his favorite stock had a $1.25M market cap.

In 1953, Buffett sells his ENTIRE GEICO stake for $15,259 and puts half his net worth into a stock no broker would ever pitch him: Western Insurance Securities of Fort Scott, Kansas.

The company was earning $16 a share while the stock traded between $12 and $20. One times earnings. With only 50,000 shares outstanding, the whole company was valued around $1.25 million, while sitting on a $22M bond portfolio and a book value of $86 a share. Buffett was paying roughly 37 cents on the dollar of book.

He found it flipping through Moody's manuals page by page. It was so illiquid he ran ads in the local newspaper to buy shares off farmers and townspeople.

By 1955 the stock hit $95. Roughly a 6x from his cost.

Buffett said his 50%+ annual years in the 1950s came from exactly this kind of hunting: "You have to find the companies that are off the map, way off the map."

But the story doesn't end there.

In 1976, when GEICO nearly went bankrupt with a $126M loss, the stock down from $61 to $2, and regulators circling, he came back. He saw the one thing that hadn't broken: GEICO was still the low-cost producer in auto insurance. No agents. Direct to customer. A moat that a few bad years couldn't kill.

He met new CEO Jack Byrne, started buying at ~$3, and backed the rescue financing when Wall Street wouldn't. GEICO's buybacks quietly grew Berkshire's stake to half the company. In 1996, he bought the rest for $2.3 billion.

The kid who knocked on GEICO's door in 1951 ended up owning the whole building.

He wasn't born a blue-chip investor. He was a microcap hunter first.

[Zorepad @zorepad]

$5 trillion in retirement money never once checks what a stock is worth.

Stock prices swing far more than the earnings behind them ever do.

That mismatch has a name: the excess volatility puzzle. Robert Shiller helped win a Nobel Prize for charting it with John Campbell back in the 1980s.

A second puzzle sits right next to it. Trend-following beats almost every strategy built on fundamentals, across markets and across decades.

Victor Haghani, CIO of Elm Wealth, calls that result genuinely disturbing. A rational market shouldn't reward chasing the recent past.

His new working paper, built with researcher Richard Dewey, tries to explain both puzzles with 3 kinds of investors.

Fundamental investors size their bets off expected cash flow. Extrapolators size theirs off the last few years of returns. Static investors do neither.

They hold a 60/40 stock-and-bond split forever, and $5 trillion sits inside exactly that kind of balanced and target-date fund.

They didn't reach for more equations. They wrote a few hundred lines of code and simulated thousands of 40-year markets instead.

The extrapolators alone were enough to reproduce the excess volatility and the long momentum runs, with no news event required.

Then they ranked the players. Extrapolators lose money. Short-term dip-buyers lose money. Static, do-nothing investors do fine.

Fundamental investors, the ones actually pricing off earnings, barely beat them. A trend an extrapolator builds can run for years before earnings ever catch up.

US stocks have pulled away from the rest of the world for 2 to 3 decades, partly on exactly that kind of run.

Two-year Treasury notes carry almost no extrapolators. Nobody chases momentum on a bond that matures in 2 years.

Gold has the opposite problem. With no clean cash-flow value to anchor it, extrapolators are nearly the only ones left pricing it.

The strongest player in the whole model isn't real. Haghani calls it the anticipatory investor, someone who holds the actual model, knows every input, and runs every outcome before making a bet.

Nobody gets to be that investor. It only marks the ceiling.

Haghani has run his own firm on this idea for 15 years. He compares the whole project to two professors, a blackboard full of proofs, and a toaster sitting on the table between them.

It already works. Now they're checking if it's true.

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@TheICHpodcast He’s drawing a line most people never think about. Net worth is a headline. Cash is a fact

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@InvestingCanons Most people do it backwards. They wait for the crowd, then call it a thesis