Olivia
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Helios
@0xHeliosMind
In pursuit of non-random walks
Joined August 2024
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One model. Built inside Goldman Sachs starting in 1986, published in 1990, still the industry standard for pricing interest rate derivatives today — thirty-six years later.
The man who built it spent the second half of his career warning everyone not to trust it too much.
Emanuel Derman was a theoretical physicist before he was a quant. He joined Goldman Sachs in 1985, co-built the Black-Derman-Toy model alongside Fischer Black, and by 1997 was running the firm's Quantitative Risk Strategies group.
In 1996 — two years before Long-Term Capital Management proved him right — Derman published an essay called "Model Risk." His argument: every model is a cartoon of reality dressed up as an equation. Traders start trusting the costume.
Nobody listened. In 2008 the same mistake, at a much larger scale, helped take down the global financial system.
In January 2009, Derman and mathematician Paul Wilmott published the "Financial Modelers' Manifesto" — structured, deliberately, like a political manifesto — with a Hippocratic Oath attached: "I didn't make the world, and it doesn't satisfy my equations."
This keynote is that same argument, live, from the person who built the textbook case and then spent two decades trying to stop people from misreading it. Financial engineering programs charge tuition to teach both halves of that story.
Derman gave Wall Street the model for free. Nineteen years later, he gave it the warning label too. Only the first one got read.
https://t.co/dxBoFLaNIK
67 million people have watched a "day in the life of a trader."
Almost none of them have watched the 2 hours where the man who actually invented quant trading explains how he did it.
His name barely comes up. That's the tell.
Edward Thorp. Math PhD, UCLA, 1958. Taught alongside Claude Shannon at MIT.
He proved blackjack was beatable and casinos rewrote their rules over one paper. Then he ran Princeton/Newport Partners at roughly 20% a year after fees for two decades — no down quarter — and priced options his own way before Black-Scholes was ever published.
First market-neutral fund. First quant fund. First outside check into Ken Griffin's Citadel.
His entire system fits in one sentence:
Find an edge you can measure, then size the bet to that edge — no bigger, no smaller.
The card counting made headlines. The options math made headlines. But the weapon underneath both was the Kelly criterion — how much to wager when the odds are yours. Shannon pointed him to it. Thorp turned it into a fortune twice, in two different arenas.
The trader with a real edge who bets too big blows up before the edge pays. Variance takes the account first. The one who bets too timid turns a career-defining setup into pocket change.
Thorp compounded for 30 years because he treated sizing as the strategy, not the afterthought. That's the whole difference between the people who have an edge and the people who keep it.
The interview is free. He walks through all of it — Vegas, the fund, Kelly, calling Madoff a fraud years early.
Most people who say they want the edge won't finish the two hours. That was always the real filter. Not the math. The willingness to sit down and do the work.
67 million people have watched a "day in the life of a trader."
Almost none of them have watched the 2 hours where the man who actually invented quant trading explains how he did it.
His name barely comes up. That's the tell.
Edward Thorp. Math PhD, UCLA, 1958. Taught alongside Claude Shannon at MIT.
He proved blackjack was beatable and casinos rewrote their rules over one paper. Then he ran Princeton/Newport Partners at roughly 20% a year after fees for two decades — no down quarter — and priced options his own way before Black-Scholes was ever published.
First market-neutral fund. First quant fund. First outside check into Ken Griffin's Citadel.
His entire system fits in one sentence:
Find an edge you can measure, then size the bet to that edge — no bigger, no smaller.
The card counting made headlines. The options math made headlines. But the weapon underneath both was the Kelly criterion — how much to wager when the odds are yours. Shannon pointed him to it. Thorp turned it into a fortune twice, in two different arenas.
The trader with a real edge who bets too big blows up before the edge pays. Variance takes the account first. The one who bets too timid turns a career-defining setup into pocket change.
Thorp compounded for 30 years because he treated sizing as the strategy, not the afterthought. That's the whole difference between the people who have an edge and the people who keep it.
The interview is free. He walks through all of it — Vegas, the fund, Kelly, calling Madoff a fraud years early.
Most people who say they want the edge won't finish the two hours. That was always the real filter. Not the math. The willingness to sit down and do the work.
https://t.co/eUBl51hbTt
https://t.co/H8Q9gCcRhl
Charlie Munger invested with exactly one outside manager in his entire life. The full lecture where that manager explains his method, in a Columbia classroom, is sitting on YouTube for free.
Li Lu was a student leader at Tiananmen Square in 1989. He fled China with nothing - no money, no family in the country, a mountain of debt. A guest lecture at Columbia changed the trajectory. Warren Buffett, speaking to Bruce Greenwald's class in 1993. Li Lu was in the room by accident. Thirteen years later, in 2006, he came back to teach the same class himself.
The framework is a filter, not a formula. Own the business, don't trade the paper. Think like the owner of the whole company, not a renter of shares. Demand a margin of safety wide enough that being wrong doesn't ruin you. And start looking where the price already reflects the worst case - the new-low list, not the new-high list. Most people can't operate this way. It requires being comfortable as the only person in the room who thinks the stock is a buy.
In the lecture, he walks through two of his own trades, Timberland and a Hyundai department store chain, both multi-baggers, using nothing more exotic than that filter. A few years later he ran the same filter on a Chinese battery and car company almost nobody in the West had heard of. He put Munger into it. Berkshire's stake in BYD went on to net over a billion dollars in profit.
The lecture is free. The filter is four sentences long. What was never free is doing the unnatural thing it demands - buying exactly where everyone else has already looked away.
https://t.co/YoNbtFby2V
In 1963, an IBM physicist ran the numbers on cotton prices and found something that should have ended modern portfolio theory before it started.
Benoit Mandelbrot. Sterling Professor Emeritus of Mathematical Sciences at Yale, IBM Fellow Emeritus, the man who coined the word "fractal." Before any of that made him famous, he was the first person to actually test the assumption every pricing model still runs on - that markets move like a bell curve.
They don't.
Mandelbrot found cotton-price changes followed a fat-tailed distribution. Extreme moves weren't rare accidents way out at the edge of the curve. They were a structural feature of the data. Ten years before Black-Scholes. Forty-five years before 2008.
Every VaR model on every trading floor is still built on the Gaussian assumption Mandelbrot broke in 1963. Every risk-management certification still teaches you to build on top of it anyway.
In 2001, he gave a public lecture at MIT tying it all together - coastlines, mountains, market behavior - under one theory of roughness. Full lecture. No paywall. Sitting free on MIT's own archive.
The financial engineering programs that still teach the model he disproved charge six figures for the degree.
He handed you the counter-evidence decades early, for nothing.
Most people still won't press play.
In 1963, an IBM physicist ran the numbers on cotton prices and found something that should have ended modern portfolio theory before it started.
Benoit Mandelbrot. Sterling Professor Emeritus of Mathematical Sciences at Yale, IBM Fellow Emeritus, the man who coined the word "fractal." Before any of that made him famous, he was the first person to actually test the assumption every pricing model still runs on - that markets move like a bell curve.
They don't.
Mandelbrot found cotton-price changes followed a fat-tailed distribution. Extreme moves weren't rare accidents way out at the edge of the curve. They were a structural feature of the data. Ten years before Black-Scholes. Forty-five years before 2008.
Every VaR model on every trading floor is still built on the Gaussian assumption Mandelbrot broke in 1963. Every risk-management certification still teaches you to build on top of it anyway.
In 2001, he gave a public lecture at MIT tying it all together - coastlines, mountains, market behavior - under one theory of roughness. Full lecture. No paywall. Sitting free on MIT's own archive.
The financial engineering programs that still teach the model he disproved charge six figures for the degree.
He handed you the counter-evidence decades early, for nothing.
Most people still won't press play.
https://t.co/YoNbtFby2V
Jeffrey Rosenthal. BSc from Toronto at 20. PhD in mathematics from Harvard at 24. Tenured professor by 29. Fellow of the Institute of Mathematical Statistics. Fellow of the Royal Society of Canada.
In 2006, CBC investigators handed him a data set: over 200 Ontario lottery retail clerks who'd personally cashed winning tickets worth $50,000 or more. Rosenthal ran the numbers.
The odds of that many "insider" wins happening by chance were vanishingly small. He wasn't proving any single theft. He was proving the aggregate distribution couldn't be luck.
That's the entire edge. Not catching one bad actor. Catching the shape of the data before anyone else thinks to look.
The investigation led to criminal charges, prison sentences, and the firing of the lottery corporation's CEO. Every safeguard the industry runs today on retailer payouts traces back to this exact method.
Casinos and lotteries now pay people real money to run this check quietly, in the background, forever.
Most people betting money on chance never learn the math. Most people who could learn it in an hour don't bother.
Rosenthal lays out the whole framework, lottery odds, why the house always wins, how to actually reason about unlikely events, in one public lecture. Free. No institute, no tuition, no NDA.
The math has been sitting there since 2006.
Nobody's stopping you from using it.
@Myrqen everyone screenshots the win, almost nobody archives the reasoning that produced it
Jeffrey Rosenthal. BSc from Toronto at 20. PhD in mathematics from Harvard at 24. Tenured professor by 29. Fellow of the Institute of Mathematical Statistics. Fellow of the Royal Society of Canada.
In 2006, CBC investigators handed him a data set: over 200 Ontario lottery retail clerks who'd personally cashed winning tickets worth $50,000 or more. Rosenthal ran the numbers.
The odds of that many "insider" wins happening by chance were vanishingly small. He wasn't proving any single theft. He was proving the aggregate distribution couldn't be luck.
That's the entire edge. Not catching one bad actor. Catching the shape of the data before anyone else thinks to look.
The investigation led to criminal charges, prison sentences, and the firing of the lottery corporation's CEO. Every safeguard the industry runs today on retailer payouts traces back to this exact method.
Casinos and lotteries now pay people real money to run this check quietly, in the background, forever.
Most people betting money on chance never learn the math. Most people who could learn it in an hour don't bother.
Rosenthal lays out the whole framework, lottery odds, why the house always wins, how to actually reason about unlikely events, in one public lecture. Free. No institute, no tuition, no NDA.
The math has been sitting there since 2006.
Nobody's stopping you from using it.
https://t.co/s7CSVJZhBN
@0xDominiqq Which means for decades the house edge wasn't hiding in the rules of the game at all - it was sitting quietly in the shuffle count, and every counter who noticed just took it.
you should expect a miracle about once a month.
not a metaphor. a literal claim from a statistician whose day job is finding edges for one of europe's largest hedge funds.
david hand. emeritus professor of mathematics, imperial college london. twice president of the royal statistical society. chief scientific advisor to winton capital for eight years. he spent his career pricing risk for people who cannot afford to be wrong about "unlikely."
his claim: events we call miraculous aren't rare. they're inevitable. the lottery winner who wins twice. the dream that "comes true." the friend you bump into in a foreign city. none of it is magic. it's five overlapping laws of probability nobody taught you.
inevitability. truly large numbers. selection. the probability lever. near enough.
put weight behind each one and the "impossible" stops being impossible. it becomes the expected outcome of enough tries, enough people, enough time.
he gave this entire framework away, free, to a room of maybe forty people at a humanist society meetup in dorset.
no paywall. no course. no keynote fee.
the video is sitting right there. almost nobody has watched it.
you should expect a miracle about once a month.
not a metaphor. a literal claim from a statistician whose day job is finding edges for one of europe's largest hedge funds.
david hand. emeritus professor of mathematics, imperial college london. twice president of the royal statistical society. chief scientific advisor to winton capital for eight years. he spent his career pricing risk for people who cannot afford to be wrong about "unlikely."
his claim: events we call miraculous aren't rare. they're inevitable. the lottery winner who wins twice. the dream that "comes true." the friend you bump into in a foreign city. none of it is magic. it's five overlapping laws of probability nobody taught you.
inevitability. truly large numbers. selection. the probability lever. near enough.
put weight behind each one and the "impossible" stops being impossible. it becomes the expected outcome of enough tries, enough people, enough time.
he gave this entire framework away, free, to a room of maybe forty people at a humanist society meetup in dorset.
no paywall. no course. no keynote fee.
the video is sitting right there. almost nobody has watched it.
Un profesor del MIT les ofreció a sus alumnos una apuesta simple:
Cara: ganas $125 dólares.
Cruz: pierdes $100.
Matemáticamente es un robo. De media sales +$12.50 por cada vez que la juegas. Es lo que los economistas llaman “más que justa”.
La mayoría de la clase dijo que no.
No eran tontos. Eran humanos.
El profesor lo llevó más lejos. Les dijo: “Los voy a obligar a hacer esta apuesta… a menos que me paguen para librarse”.
¿Cuánto estaban dispuestos a pagar?
$43 dólares.
Casi la mitad de su dinero… para escapar de una apuesta que está a su favor.
Eso no es debilidad.
Se llama aversión al riesgo.
Y es exactamente la razón por la que existe el seguro, las garantías extendidas y casi todas las decisiones “seguras” que tomamos con el dinero.
No estás siendo irracional cuando rechazas una buena apuesta.
Estás poniendo precio al miedo de perder.
En esta clase del MIT se aprende cómo funciona realmente la utilidad esperada.
Por qué la aversión al riesgo no es un defecto, sino una característica humana.
Y cómo eso explica casi todas las decisiones económicas que tomamos a diario.
Guárdala para ver mas tarde 🔖
MIT's Laboratory for Financial Engineering spent two decades building a framework for why markets crash. It's sitting on a public library's YouTube channel, barely watched.
Quant funds pay people seven figures to think this way.
Andrew Lo. Charles E. and Susan T. Harris Professor at MIT Sloan. Director of MIT's Laboratory for Financial Engineering. Named one of TIME's 100 most influential people on earth. Not a fund manager selling a system. An academic who spent 20+ years testing one.
His framework, stripped down: markets aren't perfectly rational, and they aren't randomly irrational either. They evolve. Investors behave like species competing for scarce resources. Loss aversion, herding, panic selling. The behaviors behavioral economists call "biases" are adaptations that worked in one environment and fail in the next.
Lo's own description of it: it "reconciles the two competing schools of thought in financial economics." That single sentence is why quant desks pay people to build proprietary versions of exactly this reasoning. Knowing which regime you're in beats knowing which theory is "right."
Bet that markets are always efficient, and 2008 blindsides you. Bet that markets are just irrational, and you have no model at all. Just vibes.
Lo gave his away, in full, at a public library.
The lecture is free. Building the discipline to check your regime before every trade is not. That's the only part nobody can hand you.
MIT's Laboratory for Financial Engineering spent two decades building a framework for why markets crash. It's sitting on a public library's YouTube channel, barely watched.
Quant funds pay people seven figures to think this way.
Andrew Lo. Charles E. and Susan T. Harris Professor at MIT Sloan. Director of MIT's Laboratory for Financial Engineering. Named one of TIME's 100 most influential people on earth. Not a fund manager selling a system. An academic who spent 20+ years testing one.
His framework, stripped down: markets aren't perfectly rational, and they aren't randomly irrational either. They evolve. Investors behave like species competing for scarce resources. Loss aversion, herding, panic selling. The behaviors behavioral economists call "biases" are adaptations that worked in one environment and fail in the next.
Lo's own description of it: it "reconciles the two competing schools of thought in financial economics." That single sentence is why quant desks pay people to build proprietary versions of exactly this reasoning. Knowing which regime you're in beats knowing which theory is "right."
Bet that markets are always efficient, and 2008 blindsides you. Bet that markets are just irrational, and you have no model at all. Just vibes.
Lo gave his away, in full, at a public library.
The lecture is free. Building the discipline to check your regime before every trade is not. That's the only part nobody can hand you.
https://t.co/mvYfX85y61
A Nobel Prize winner gave away the entire foundation of professional portfolio management in a 90-minute lecture.
It's sitting quietly on a university channel, watched by almost nobody.
Wealth managers charge you 1% of your assets, every year, forever, to implement roughly what's in this talk.
William F. Sharpe. 1990 Nobel Prize in Economic Sciences. Co-built the Capital Asset Pricing Model. Invented the Sharpe Ratio. It's the single number every professional fund on earth still gets graded against.
In 2009, he walked into a lecture hall at Stanford and told a room full of ordinary investors exactly why almost nobody beats the market, and why almost nobody should try.
His framework, stripped to the studs: risk and return are chained together. Any "shortcut" to higher returns is just higher risk in disguise. Diversification is the only free lunch that exists. It cuts your risk without cutting your expected return. Everything sold to you past that point is either luck, or a fee.
The people who lose: everyone chasing the hot fund manager, the newsletter with a "system," the friend who's up huge this year.
The people who win: anyone boring enough to hold a diversified portfolio and stop paying for stories.
Sharpe wasn't selling anything. He built the math, published it decades ago, then stood on a stage and explained it again for free.
The lecture costs nothing to watch.
Ignoring it is what costs money.
A Nobel Prize winner gave away the entire foundation of professional portfolio management in a 90-minute lecture.
It's sitting quietly on a university channel, watched by almost nobody.
Wealth managers charge you 1% of your assets, every year, forever, to implement roughly what's in this talk.
William F. Sharpe. 1990 Nobel Prize in Economic Sciences. Co-built the Capital Asset Pricing Model. Invented the Sharpe Ratio. It's the single number every professional fund on earth still gets graded against.
In 2009, he walked into a lecture hall at Stanford and told a room full of ordinary investors exactly why almost nobody beats the market, and why almost nobody should try.
His framework, stripped to the studs: risk and return are chained together. Any "shortcut" to higher returns is just higher risk in disguise. Diversification is the only free lunch that exists. It cuts your risk without cutting your expected return. Everything sold to you past that point is either luck, or a fee.
The people who lose: everyone chasing the hot fund manager, the newsletter with a "system," the friend who's up huge this year.
The people who win: anyone boring enough to hold a diversified portfolio and stop paying for stories.
Sharpe wasn't selling anything. He built the math, published it decades ago, then stood on a stage and explained it again for free.
The lecture costs nothing to watch.
Ignoring it is what costs money.
https://t.co/PbfztEwWL7
Most fintechs bolt AI onto their compliance stack and hope nobody asks how it decides. MIT has a free lecture from someone who used to be the one asking.
Gary Gensler. Chaired the SEC from 2021 to 2025. Spent 18 years at Goldman Sachs before that. Chaired the CFTC and rewrote the rules for a $400 trillion swaps market.
He doesn't teach "AI use cases." He teaches where a regulator's eyes go first.
Explainability.
If a model can't say why it made a decision, in plain terms, to someone who didn't build it, the firm using it is exposed. It doesn't matter how accurate the model is.
Sounds like a compliance checkbox? That's exactly why trading desks and product teams skip it. Until the enforcement letter arrives.
Same model. Same accuracy. Same backtest results. Different outcome. One firm's product ships clean. The other gets a consent order.
Now think about which one you'd rather be.
You shipped a black-box model. Somewhere, a competitor mapped this exact framework first and never got the call.
The class before this one covers where AI actually lives inside a financial firm: trading, underwriting, fraud detection. This one is where he switches hats, puts the regulator's questions back on the table, and walks through fairness, explainability, privacy, and who's accountable when the model is wrong.
I've sat through vendor AI-compliance webinars that cost four figures a seat. None of them made me as nervous as watching a former SEC chair map out exactly where enforcement goes next.
Save this before your compliance team needs it. Watch the back half twice. That's where the regulatory questions live.
Most fintechs bolt AI onto their compliance stack and hope nobody asks how it decides. MIT has a free lecture from someone who used to be the one asking.
Gary Gensler. Chaired the SEC from 2021 to 2025. Spent 18 years at Goldman Sachs before that. Chaired the CFTC and rewrote the rules for a $400 trillion swaps market.
He doesn't teach "AI use cases." He teaches where a regulator's eyes go first.
Explainability.
If a model can't say why it made a decision, in plain terms, to someone who didn't build it, the firm using it is exposed. It doesn't matter how accurate the model is.
Sounds like a compliance checkbox? That's exactly why trading desks and product teams skip it. Until the enforcement letter arrives.
Same model. Same accuracy. Same backtest results. Different outcome. One firm's product ships clean. The other gets a consent order.
Now think about which one you'd rather be.
You shipped a black-box model. Somewhere, a competitor mapped this exact framework first and never got the call.
The class before this one covers where AI actually lives inside a financial firm: trading, underwriting, fraud detection. This one is where he switches hats, puts the regulator's questions back on the table, and walks through fairness, explainability, privacy, and who's accountable when the model is wrong.
I've sat through vendor AI-compliance webinars that cost four figures a seat. None of them made me as nervous as watching a former SEC chair map out exactly where enforcement goes next.
Save this before your compliance team needs it. Watch the back half twice. That's where the regulatory questions live.
A broke Italian gambler in 1560 wrote a short manual on how to win at dice. Nobody in finance read it for four hundred years.
The nine-trillion-dollar insurance industry runs on his equation.
His name was Girolamo Cardano. The book was called Liber de Ludo Aleae. He scribbled it in Milan to settle a card debt. Every dollar of premium ever collected on Earth is a footnote to that scribble.
Nobody connected the dots until 1996. A ninety-year-old man in New York wrote a book called Against the Gods and traced every modern risk model back to Cardano's manual. Wall Street called him the historian of risk.
His name was Peter Bernstein. In 2008 a small production company filmed him for thirteen minutes. He walked through the entire five-hundred-year arc. Cardano to Pascal to Fermat to Black-Scholes.
Then he stopped and said the industry had built glass towers on the back of an idea a broke gambler scribbled to shave the house edge.
He died the following summer. Age ninety.
There are only four ways to make money. Labor. Capital. Arbitrage. Insurance. Insurance is the oldest and the least visible. Every actuary on Earth still prices catastrophe risk with Cardano's framework.
The video is thirteen minutes long. Free on YouTube. Twenty-nine thousand people have watched it.
Some of the safest looking hedge funds in the world are not actually safe. They just haven't been priced honestly yet.
A fund holding illiquid assets doesn't get a fresh market price every day. When nobody is trading the thing you own, the reported value gets smoothed, marked closer to last month's number than to what it would actually fetch if you tried to sell it today. The reported returns look calm. The real returns underneath are not.
In 2004, Mila Getmansky, Andrew Lo, and Igor Makarov published the paper that measured exactly how much. Working from 908 hedge funds in the TASS database, they found monthly returns where serial correlation explained up to 30 to 50 percent of the pattern, far beyond what an efficient market should produce, and traced most of it to illiquidity, not skill. They built an actual econometric model for it: a smoothing profile you can estimate fund by fund, and a corrected number they called the smoothing-adjusted Sharpe ratio.
Run a fund's real Sharpe ratio through that correction and the number that made it look brilliant can come back down to ordinary. The skill wasn't gone. It was never fully there. The volatility was just being hidden by the pricing, not the strategy.
Lo runs MIT's Laboratory for Financial Engineering and founded AlphaSimplex, an actual quant hedge fund. In 2008 he testified before Congress on hedge fund systemic risk, on a panel with George Soros and Jim Simons.
The lecture where he walks through this exact method, using it to analyze hedge funds and systemic risk, is sitting on YouTube from an NBER Summer Institute methods session. Business schools teach a simplified version of this to MBA students who pay full tuition for it.
A calm-looking track record and an actually low-risk one are not the same thing. Now you know the number that tells them apart.
Some of the safest looking hedge funds in the world are not actually safe. They just haven't been priced honestly yet.
A fund holding illiquid assets doesn't get a fresh market price every day. When nobody is trading the thing you own, the reported value gets smoothed, marked closer to last month's number than to what it would actually fetch if you tried to sell it today. The reported returns look calm. The real returns underneath are not.
In 2004, Mila Getmansky, Andrew Lo, and Igor Makarov published the paper that measured exactly how much. Working from 908 hedge funds in the TASS database, they found monthly returns where serial correlation explained up to 30 to 50 percent of the pattern, far beyond what an efficient market should produce, and traced most of it to illiquidity, not skill. They built an actual econometric model for it: a smoothing profile you can estimate fund by fund, and a corrected number they called the smoothing-adjusted Sharpe ratio.
Run a fund's real Sharpe ratio through that correction and the number that made it look brilliant can come back down to ordinary. The skill wasn't gone. It was never fully there. The volatility was just being hidden by the pricing, not the strategy.
Lo runs MIT's Laboratory for Financial Engineering and founded AlphaSimplex, an actual quant hedge fund. In 2008 he testified before Congress on hedge fund systemic risk, on a panel with George Soros and Jim Simons.
The lecture where he walks through this exact method, using it to analyze hedge funds and systemic risk, is sitting on YouTube from an NBER Summer Institute methods session. Business schools teach a simplified version of this to MBA students who pay full tuition for it.
A calm-looking track record and an actually low-risk one are not the same thing. Now you know the number that tells them apart.
One model. Built inside Goldman Sachs starting in 1986, published in 1990, still the industry standard for pricing interest rate derivatives today — thirty-six years later.
The man who built it spent the second half of his career warning everyone not to trust it too much.
Emanuel Derman was a theoretical physicist before he was a quant. He joined Goldman Sachs in 1985, co-built the Black-Derman-Toy model alongside Fischer Black, and by 1997 was running the firm's Quantitative Risk Strategies group.
In 1996 — two years before Long-Term Capital Management proved him right — Derman published an essay called "Model Risk." His argument: every model is a cartoon of reality dressed up as an equation. Traders start trusting the costume.
Nobody listened. In 2008 the same mistake, at a much larger scale, helped take down the global financial system.
In January 2009, Derman and mathematician Paul Wilmott published the "Financial Modelers' Manifesto" — structured, deliberately, like a political manifesto — with a Hippocratic Oath attached: "I didn't make the world, and it doesn't satisfy my equations."
This keynote is that same argument, live, from the person who built the textbook case and then spent two decades trying to stop people from misreading it. Financial engineering programs charge tuition to teach both halves of that story.
Derman gave Wall Street the model for free. Nineteen years later, he gave it the warning label too. Only the first one got read.
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