Ethan Kho
@ethanrkho • 20,260 subscribers
Host of Odds on Open (presented by @onyxcapgroup)
Videos
Inside the Billionaire Backed Prediction Markets Hedge Fund. Run by a 24-Year-Old. Camilo Saravia (camilo), founder of BlueWalker Capital, a systematic prediction markets fund backed by Daniel Howard of Halo Capital. "I don't want more capital. I'm extremely long our equity." We cover: - Why insider trading in prediction markets is terrible for liquidity and GOOD for society - Prediction markets as cash-backed truth in a world of AI slop and disinformation - Why he turned down the allocator question entirely, and the "Goldilocks zone" that makes a fund this size work - His research team's actual mission statement: "collapse the entropy of the internet into signal" - Trading Spotify streams and measuring how fast Mamdani viralizes vs Cuomo on TikTok - Why beating earnings has almost no correlation with the stock going up, and why only testing reveals that - Mention markets as literal next-word prediction, and how makers got sniped out - Hiring missionaries with a mercenary work style, and why every hire takes a pay cut vs Citadel, Jane Street, Wintermute - Daniel Howard's mandate: faster, more risk, more aggression. "They haven't backed me to print 7% APY" - The abundance mindset, from a kid with immigrant parents sitting across from generational wealth - A venture mindset applied to public equities: pulling the thread from free cash flow down to Glassdoor culture - Why you never need to be binarily right: buy at 20, sell at 40, never wait for resolution Highlights: (00:00) Intro (00:56) Taker vs maker, reflexive vs proactive: the strategy map (03:08) What makes an event contract different from an equity (04:53) Insider trading in prediction markets: bug or feature (08:18) Cash-backed truth in a world of AI slop (12:43) Where edge actually comes from (14:19) The dataset: billions of records a day (17:44) Building a money management business from an empty office (21:34) Why asset management competes with software as a business model (23:51) How to underwrite elite talent (25:50) Missionaries vs mercenaries, and why the tension is the point (30:54) Recruiting against Citadel money (and losing on salary every time) (37:32) "We can't compromise speed": the Daniel Howard mandate (44:45) The abundance mindset (48:28) "Why should I invest?" / "I don't want more capital" (50:12) Collapsing the entropy of the internet (51:04) Spotify streams, TikTok virality, and mention markets (53:17) The one data provider he'd long if he could (55:49) There's a business behind everything (01:00:07) How to find the real drivers in any market (01:04:04) A venture mindset applied to public equities (01:10:06) Prediction markets 101: where to actually start (01:12:05) Why you don't need to be binarily right (01:13:19) Final question: building personal edge against the models
Ethan Kho106,690 views • 7 days ago
Ex-Citadel PM Rich Falk-Wallace (Rich Falk-Wallace) on why 90% of hedge fund blowups are portfolio construction — not bad ideas. Rich Falk-Wallace (PM @ Citadel | Viking Global | Silver Point | Now founder & CEO of Arcana Arcana — risk & portfolio software used by ~7 of the 10 largest multi-manager hedge funds) "When they blow up, the story is never 'Shucks, I actually am not as brilliant as I was before.' What they got wrong was risk, portfolio construction." We cover: - Why 90% of PM failures come from sizing & portfolio construction, not thesis quality - The only two ways to survive long-term: extreme hit rate/slugging, or managing ex-ante correlation - The math of 10 pods long the same trade: factor bets compound, idio bets diversify - Why LTCM is the classic backward-looking correlation failure — and why ex-ante is the whole job - The paradox: "pure fundamental" concentrated funds take the biggest factor bets (up to 80% R²) - Why the best PMs treat every factor exposure like a stock position — same limits, same diligence - Sharpe ratio as a t-statistic against the null hypothesis that you have no skill - The tiger cub who moved to a pod seat and said it felt like playing a video game - Are junior analysts screwed? Dispersion, not extinction - His contrarian take: capital is opening up beyond the Big Four via SMAs Highlights: (00:00) Intro (01:10) The real job of a hedge fund PM: a product sold to allocators (02:52) The 90% failure vector: risk leakage, not bad theses (10:25) Two ways out: hit rate/slugging vs. managing correlations (18:17) Factor bets compound, idio diversifies: why 80% idio becomes 60% at scale (27:33) Factor models as the "perfect benchmark" for every stock at every moment (38:57) The old-school PM who calls factors bullshit — Rich's answer (44:48) Treat factors like stock positions: limits, diligence, sizing (55:29) Why concentrated "pure fundamental" books take the biggest factor bets (01:05:34) Are junior analysts screwed? AI, mock books, & dispersion (01:15:52) Contrarian take: SMA capital opening up beyond the Big Four (01:19:42) The #1 new-launch killer: trying to do too many things at once
Ethan Kho523,392 views • 1 month ago
"Crypto is the dumbest market in the world" Scott Phillips (Temu Robot James) runs HyperTrend — $20M of his own capital, one losing year in six. His edge? Picking the table big firms can't sit at. "There's no second-best counterparty in crypto. You see crime, you run towards it — crime is the foundation of edge." We cover: - Why crypto still has edge in 2026 — even when your uncle is talking about Bitcoin at Thanksgiving - The simple rules (buy 20-day highs, top-20 coins) that print through any market - Why stacking trend + momentum + carry gets you there from a spreadsheet — no automation required - Price-insensitive buyers (Saylor), price-insensitive sellers (North Korea) & why both are permanent alpha - The 90-day Binance listing short — an edge hiding in plain sight in market maker contracts - Why most shit coins trend to zero — and how to trade the ones that don't - Building a tokenized, permissionless DeFi hedge fund on hyperliquid — 2 & 20, fully on-chain - Why the best quant firms are run by near-non-verbal autists with one translator Thank you so much Temu Robot James for coming on the pod! Highlights: 01:04 Table selection and the math of competitive alpha 06:21 Why basic trend following yields outsized Sharpe in crypto 08:49 Why market inefficiency persists despite institutional inflows 14:58 Price insensitive buyers: Cults, VCs, and North Korean hackers 17:17 Factor analysis and the size-decay effect in shitcoins 25:40 The structural edge in mid-frequency crypto strategies 32:43 Tokenized DeFi vaults and on-chain hedge fund governance 40:43 Designing a robust portfolio: Equal weighting vs. MVO 44:21 Sourcing alpha from ghost chains and VC exit liquidity 49:58 Exploiting market maker contracts and post-listing drift 53:55 Operational alpha: Managing margin and manipulated funding rates 01:01:13 Shifting from quant to CEO 01:11:28 How to bridge the mentorship gap with elite traders 01:22:38 Building network triads: The secret to compounding social capital 01:29:23 Why 10x goals require total identity transformation
Ethan Kho1,461,008 views • 4 months ago
"I haven't seen a real new idea in trading in at least 15 years." Tom Costello (Tom Coste) ran money at Tudor, Moore Capital, and Caxton. Built one of the first NLP-driven equity systems in 2003. 20 years managing capital, never had a down year. "Comparing what a retail trader does to what a quantitative hedge fund does is like comparing driving a bus on the New Jersey Turnpike to winning a Formula One race." We cover: - His hot take: no genuinely new trading idea in 15 years — only better people doing the same things faster - Why everyone in quant finance is a genius — and why that makes you ordinary, not special - Crypto is "super smart guys cosplaying at finance" — built for retail, which is exactly why it's the easiest money in finance right now - Why AGI won't beat the hedge fund industry — all the readily-capturable alpha is already captured - The status trap: why the path that made Paul Tudor Jones a billionaire won't work for the kid trying to copy it in 2026 - His friend the investment banker who'd quit it all to run a 10-employee ambulance supply company worth $150M - Why excitement is "wildly overbid" in finance — and why wanting an exciting trading job is itself a disqualifier - The most honest end of the financial industry — and why the media has it exactly backwards Thanks so much to Tom for coming on Odds on Open! Highlights: 00:00 Intro 01:18 Building institutional credibility for early-stage managers 03:01 The Pareto distribution of hedge fund returns 04:25 Applying the Unified Field Theory of Finance to fair value 08:14 Trading against human incentives in a deterministic market 13:54 Why allocators don’t steal alpha from prospective PMs 25:16 Evaluating career edge in quantitative finance for 2026 30:48 Paul Tudor Jones and the art of game selection 33:42 Analyzing the economic viability of starting a new fund 35:16 Identifying common retail pitfalls: Mean reversion and arbitrage 38:55 Why there hasn't been a new trading idea in 15 years 50:33 Managing tail risk: Physics vs. deterministic financial distributions 59:10 Career pathing for PMs after a fund blow-up 1:07:53 SBF and FTX: Credibility vs. the "Founder-Genius" archetype 1:13:44 Establishing proof-of-concept through audited multi-year returns
Ethan Kho1,187,584 views • 3 months ago
Victor Haghani helped build LTCM & watched it collapse — with winning trades still on the books. The lesson was never what to buy. It was how much. Victor Haghani (Co-founder @ LTCM | Founder @ Elm Wealth | Author of The Missing Billionaires) "It wasn't on the selection of the trades. It was on the sizing." We cover: - The two decisions every investor makes: what to own and how much, and why everyone fixates on the harder one - The biased-coin game that bankrupted Wall Street PMs and finance grads: a 60/40 edge handed to them, and they still blew up - Why the cost of risk is a fee you pay yourself, plus the napkin rule to price it (15% vol = 2.25% a year) - The Elon problem: 50% vol on your net worth means a ~90% chance of little left in 10 years, before anyone's even bearish - "The right answer to the wrong question," and why chasing billionaire money wrecks the plan - The crystal-ball game: hand someone tomorrow's WSJ front page and watch 1 in 6 still go bust - Claude, GPT, Gemini and Grok play the same game, and the two AIs that actually lost money - His 92-year-old mother, who day-trades every day and won't hear a word of it Highlights: 00:00 Right & ruined — the LTCM paradox 01:40 The two decisions: what to invest in vs. how much 02:50 The 60/40 coin & why max-EV bankrupts you 04:00 The experiment: PMs & PhDs sizing it all wrong 07:00 Kelly in plain English — a constant 10–20% 08:20 Why sizing isn't zero-sum, but beating the market is 10:30 Why even pros don't optimize sizing 12:50 The cost of risk is a fee — paid to yourself 15:20 Pricing your own risk: variance as the charge 16:30 Concentrated stock: 30% vol = a 9% toll 20:50 Elon, 50% vol & the log-normal trap 22:45 The right answer to the wrong question 23:35 The real objective: smooth lifetime spending & giving 27:35 The crystal-ball / WSJ front-page game 33:25 Claude, GPT, Gemini & Grok step up to trade 37:40 Claude's 66% hit rate — & the two AIs that lost money 40:35 Can anyone actually beat the market? 50:25 How much risk a young person should take 58:00 Estimating your human capital 1:02:40 The mom who won't stop day-trading 1:07:30 The one rule: if you don't save, nothing else matters
Ethan Kho625,579 views • 2 months ago
Ex-Point72 Proprietary Research Head Kirk McKeown on building edge, alpha decay, & why everything that happened on Wall Street is about to happen on Main Street. Kirk McKeown (8.5 years @ Point72 under Steve Cohen | Built primary research at Glenview under Larry Robbins | Now founder of Carbon Arc Carbon Arc) "Alpha rewards those who value assets in a cold way. You want to get it right — not be right." We cover: - How alpha creation differs across multi-manager vs. concentrated shops - The 3 vectors every middle office function must move to justify its existence - Why he worked 6-hour Sundays from 2006-2020 — and the math behind it - The TSMC call that signaled semiconductor cancellations before anyone else knew - What the quant revolution on Wall Street tells us about the AI economy today - His framework: 4 market structures, 9 business models, & why they have rules - The MIT beer game & why every business problem is really an inventory problem - His hot take: a top hedge fund launches an enterprise AI lab in 2026 Highlights: 00:00 Intro 04:47 Tutor vs Glenview vs Point72: how edge differs 12:29 How to build “lift” for PMs: at-bats, hit-rate, sizing 18:44 Building research edge: outwork, read, fieldwork 27:16 Personal moat in 2026: analogs, history, decision trees 40:08 “Main Street becomes Wall Street”: what that actually means 44:30 Carbon Arc thesis: “decimalization” of data market structure 46:43 Why the edge migrates to data plus domain context 51:00 How to win in commoditized research: sample size beats anecdotes 01:03:26 Factorizing everything: themes, market structure, business models 01:08:37 Pruning decision trees: signals, scale points, inventory dynamics 01:14:18 Contrarian 2026 take: hedge funds launching enterprise AI labs 01:23:32 Final question: one habit to build career alpha
Ethan Kho1,536,469 views • 5 months ago
Ex-Balyasny PM Ying Hua (Ying Hua) on why automation will increase demand for hedge fund talent, the quant/fundamental convergence, & why quant is blackjack but fundamental is poker. Ying Hua (PM @ Balyasny — built & led a quantamental team covering US insurance, capital markets & fintech | ~5 yrs @ Citadel running a long/short insurance book | Equity research @ Goldman Sachs | MS in Data Science @ UC Berkeley | Now founder & CEO of Implied Implied) "One of the best-kept secrets: fundamental investors are not good at sizing. Quant funds are really good at sizing." We cover: - The only real line between quant and fundamental: historical pattern matching vs. "how is this time different" — and the alpha neither group is looking at - Why she rebuilt her process so every model updated within 2 minutes of a print - Scraping highway patrol data from 15 states to track auto insurance losses live, every single day - The Malibu wildfire: mapping burned mansions from celebrity tweets to estimate losses before any industry consultant published a number - Her automation math: data gathering ~100% automatable, processing ~80%, judgment still 100% human - AI is quant for words — next-token prediction is pattern matching, which makes this just the next automation wave after quant and indexing - The proof differentiated views pay more: insurance stocks moved 2-3% on earnings in 2010; by the time she left, 15-20% intraday - Why "hook Claude Code up to data and let it rip" fails: BloombergGPT losing to a smaller open-source model, & why horizontal models are college grads - Quant is blackjack with card counting; multi-manager investing is poker — your hand, others' perception of it, your seat, everyone's stack - Most PMs are playing the wrong game: the positioning game hiding inside "fundamental" sectors with no new money coming in - Her hiring bar at BAM: every fundamental analyst learns Python — and the one skill she says can't be trained - The only two truly meritocratic jobs: hedge fund PM & sales Highlights: (00:00) Intro (00:40) How a quantamental PM actually puts on a position (02:05) The only real line between quant and fundamental (04:45) Why quantamental lowers the burden on your brain (06:40) Scraping 15 states of highway patrol data to nowcast insurance losses (09:25) The Malibu wildfire: estimating losses from celebrity tweets (11:55) How much of fundamental investing can be automated (13:45) Quantifying intuition: when a CFO's filler words jump 8% to 20% (16:25) The contrarian case: automation expands demand for talent (18:15) Earnings vol exploded — differentiated views pay more (20:25) Why Claude Code can't run your book (23:40) Horizontal models are college grads with no domain knowledge (30:50) Why chat is the wrong interface for investors (36:20) Will AI make markets more or less efficient? (39:10) Two things every fundamental PM should do today (41:50) The moat that expands: talent, redefined (45:50) Sometimes the game is positioning, not fundamentals (48:25) Blackjack vs. poker vs. surfing: matching the game to your horizon (52:00) Should young analysts chase the hottest sector? (57:55) Munger vs. Musk: two philosophies of wealth (1:01:05) Self-awareness in investing is bimodal (1:07:05) The only two truly meritocratic jobs: hedge funds & sales (1:08:50) The one skill for every regime: reconstruct the narrative
Ethan Kho244,449 views • 28 days ago
You can pump a $50M coin to $1B with $1.5M of real buying. Scott Phillips (Temu Robot James) on how new token listings actually work: "There's a huge industry of market makers operating out of Dubai. Their business model is: we're gonna pump your coin, and we want 30% of the profit when we dump it on exit liquidity day." "They run the ball up. Buy, sell, buy. Create momentum ignition. Retail finally buys in the middle of a bull run — and then it dumps." "Buying 20-day highs works best on the top decile. By the fourth decile, you get negative momentum effects." "Anything in the bottom 20% of market cap on Binance perps that makes a 20-day high — short it. That is a very strong edge." "The market maker contract runs 90 days. It's priced as a call option on the 7-day VWAP after launch." "Once the strike kicks in, delta hedging from market makers drives these coins down." "Wait 7 days. Short it for 90 days. I kid you not." "That's not an edge big enough for institutional. That's an edge for retail guys with a small account."
Ethan Kho865,217 views • 4 months ago
"85% accuracy on Wall Street will get you 100% fired." Brett Caughran (Brett Caughran) has managed analysts at Citadel, D.E. Shaw & tiger cub funds. His take on AI and the future of junior analysts: "There's almost no better time to be starting a career as a fundamental investor. These tools let junior investors get to the juicy part of the investment process more quickly." We cover: - Why the junior analyst role is transforming faster than any point in the last 20 years — and why the juniors who adapt will reach the real work of investing years sooner - The old grunt work that's already dead — and what's replacing it - Why billion-dollar funds won't cut analyst headcount, but the job description is changing dramatically - “Should I still learn Excel modeling?" — yes, because you can't debug what you don't understand - Why creativity & tenacity are becoming the new differentiators over raw quantitative skill - How multi-manager alpha factories scaled from $10B to $60B+ while sustaining double-digit returns — proof that more information hasn't compressed alpha - Why reading a 10-K with pen and paper still matters even when AI can summarize it in seconds - The most underrated skill in the best investors he's worked with: genuine curiosity Thanks so much to Brett (Brett Caughran) for coming on Odds on Open! Highlights: 00:00 Intro 01:29 Frameworks for developing a differentiated variant perception 05:16 Financial drivers vs. narrative cycles: The Focus 5 framework 08:29 Analyzing the stock vs. business: Bayesian updating in public markets 12:52 AI as an intellectual power tool vs. consensus "alpha slop" 17:21 Accelerating the hunch-to-hypothesis pipeline with AI sniff tests 21:52 The evolution of junior analysts: From data entry to primary research 28:46 Why market microstructure and behavioral alpha prevent index efficiency 38:44 Training junior analysts: Earning the right to use power tools 48:28 LLMs as orchestration tools for human primary research 54:55 Teachable scientific process vs. revealed investment judgment 57:54 Common threads across Multi-Managers, Single Managers, and Tiger Cubs 59:49 Curiosity as a meta-skill and the art of system thinking
Ethan Kho881,215 views • 4 months ago
"He's got 50% vol on his entire net worth. It's insane." The richest man alive — and the math nobody around him is running: Victor Haghani co-founded LTCM. Now runs Elm Wealth. Victor Haghani explains: "I guarantee Elon isn't thinking about any of this." "Not bearish, not bullish — just assume everything he owns is fairly priced." "Now draw the log-normal distribution." "Almost all of the probability mass sits below where he is today." "There's a ~90% chance he has very little wealth in 10 years — from the math alone."
Ethan Kho350,991 views • 2 months ago
A former Citadel PM on why funds actually blow up. Feels relevant these days: "It's much more common that people say: I had this incredible thesis, incredible differentiation." "But I screwed up on portfolio construction and risk — I blew up that way, not the other way around." "Walk into Midtown, throw a dart, you'll find somebody who can generate a positive, idiosyncratic return." "There's a good number of people who can post a positive hit rate on alpha." "Downstream of that, you can build attractive products on the back of that alpha." the real number: "90% of hedge fund failures — PM failures, even analyst failures — are bad portfolio construction. Mis-sizing. All of it." Alpha is common. Surviving your own sizing not so much.
Ethan Kho114,292 views • 20 days ago
This guy beat the market for 17 straight years trading a sector many investors have written off post-2008 Derek Pilecki (Derek Pilecki) runs a financials-only fund. 21%+ annualized. His edge? A corner of the market many investors moved away from after the GFC. We cover: - Why he expanded from 25 → 40 positions and returns went UP - His counterintuitive rule: buy higher, not lower (positions get LESS risky as they rise) - The Robinhood call — bought late 2023, rode it to a multibagger - Why he's quietly watching FactSet, Morningstar & Verisk right now - His view on private credit risk (and why he disagrees with Jamie Dimon) - How he uses AI to analyze more stocks without losing his edge - Why markets chronically underreact to good news — and how to exploit it - The brutal career reality no one tells young PMs about Highlights: 00:00 Intro 01:06 Derek's +21% annualized return track record 02:50 Fundamental business change vs market noise in Robinhood 05:25 Portfolio construction: Concentration limits and adding to winners 09:09 Sourcing alpha and identifying three-year doubles in financials 12:44 Developing edge through repetition and management team cycles 14:16 Why the post-GFC regime fundamentally changed bank underwriting 17:07 Assessing tail risk and leverage in the private credit market 21:23 AI-driven market dispersion and identifying moaty businesses 24:11 Why shareholder base turnover matters for timing broken charts 29:37 Integrating AI into fundamental research and SEC filing analysis 35:39 Risk management: Permanent capital loss vs mark-to-market volatility 37:12 Capacity constraints: Optimizing for returns over AUM scale 50:39 Career risk and the reality of active money management
Ethan Kho800,227 views • 4 months ago
Ex-WorldQuant Head of Data Strategy Matt Ober on why quants don't care about the stock market, & why prediction markets become a new asset class. Matt Ober (Ex-Chief Data Scientist @ Third Point | Ex-Head of Data Strategy @ WorldQuant | Now GP @ Social Leverage) "Nobody actually really cared about the stock market. I bet if you asked people there when Nvidia was reporting, nobody had a clue." We cover: - The WorldQuant thesis: out-consume the world on data, out-manage the world on money - How a Chico State grad found the job on Craigslist — then walked into a room of PhDs - Revere, the alt-data goldmine only quants understood - Tracking CEOs' private jets to front-run M&A - WorldQuant's factory vs Third Point's fine-art boardroom - Selling data science to analysts who thought it was a joke - Why you sell beta, not alpha — alpha erodes, beta is sticky - The "degenerate economy," and why he can't hire interns anymore - Prediction markets as the next asset class — bigger than options and futures - The only edge left in an AI world: your network Thanks a ton Matt Ober for coming on the pod! Highlights: (00:00) Intro (01:18) The WorldQuant thesis - more data, more money (02:00) Building the alt-data stack (03:00) Revere - the dataset only quants understood (04:50) A finance major among PhDs (09:40) Trading datasets like stocks (10:30) Why quants don't watch the market (12:30) WorldQuant Ventures is born (16:30) Factory vs. boardroom (18:30) Data's wild west at Third Point (23:15) Tracking private jets to front-run M&A (24:20) Igor vs. Dan & the power of network (32:10) The degenerate economy (36:25) Patience & outworking everyone (38:55) Alpha vs. beta, markets & careers (46:10) Somebody's gotta sell (51:20) Prediction markets, the new asset class (53:30) The DoorDash earnings call (56:35) Going mainstream (01:03:10) The single source of all edge
Ethan Kho432,870 views • 2 months ago
One agentic workflow now does 1,000 hours of hedge fund analyst work. Aakarsh Ramchandi founded the data team @ Third Point, built screening engines @ FactSet, & now builds agentic research tools @ RavenPack. "There's gonna be a full convergence of quant and qual. Most discretionary analysts I know are somewhere in their Claude journey — and the quants are going the other way around." We cover: - Year one at Third Point: onboarding 100 data sets with a team of 4 — & why they kept point-in-time copies of every vendor feed to catch panels that silently changed overnight - The Dan Loeb pitch story — a 45-page deck, six weeks of work, he stops at page 26, asks one question, & the whole thesis breaks - "Kind but not nice" — the zero-politics office where everyone gets corrected by elite people daily - Why analysts don't want your forecast — they want facts in Excel, red-green-blue, formatted their way - Hedging a concentrated activist book with alt-data short baskets built from a 400-500 factor model - Why Nvidia broke the Barra model — & building custom semiconductor factors instead - The agentic earnings preview: 8-9 step workflows, 35M tokens per run, ~1,000 hours of analyst work encoded - Self-improving loops — agents reviewing their own last 10 traces & patching their mistakes - The WorldQuant hackathon: 7,000 quants turning unstructured text into 35M unique time series Highlights: (00:00) Intro (01:38) Founding Third Point's data team in 2017 (03:55) Six months building point-in-time data infrastructure (06:20) How an event-driven fund actually uses alt data (12:40) Team structure & the original forward deployed engineer (17:10) Nobody wants your forecast — just give it to them in Excel (19:35) Measuring signals: direction, point estimates & confidence intervals (24:05) Working with Dan Loeb — the elite bullshit detector (26:05) The page-26 "Why?" story (28:55) 5AM Saturdays & discipline that compounds (32:05) Kind but not nice: the zero-politics office (33:55) How an activist creates alpha by re-running the business (43:10) Hedging the book with alt-data short baskets (50:40) Why Nvidia broke standard factor models (56:25) From search to RAG to agents (1:04:20) Opus 4.5 changes the game: 70% → 90% accuracy (1:11:00) Anatomy of an agentic earnings preview — 35M tokens per run (1:17:20) Ambient agents: the always-on Jarvis (1:19:40) Self-improving loops & encoded judgment (1:20:20) Finance in 10 years: the full convergence of quant & qual
Ethan Kho303,243 views • 2 months ago
A quant fund that's returned 20% a year for 20 years claims it has no informational edge. It doesn't know anything the market doesn't. The CEO on what the edge actually is: Suhaimi Zainul-Abidin, CEO of Quantedge, explains: "There are two ways you can invest well. You can have skill in the bet you make, or you can have more bets, even if you don't have as good skill." "There's such a thing as an information ratio: the skill of the bet times the square root of the number of independent bets you take." "We certainly don't have all the information. I don't think we know things above and beyond everybody else. And we can't predict the markets." "What we can do is operate one of the most diversified portfolios in the world. Close to 300 distinct markets. Forty to fifty in equities. Fifty different commodity types." "That diversification means your idiosyncratic risk goes down tremendously. And if portfolio risk drops, you can make a decision: do I want to ramp the risk back up? That's exactly what we do." "If you run a concentrated book at high levels of risk, you're asking for trouble. We run high risk, yet a level that ensures there's no risk of ruin." "20% annualized for 10 years, well done. For 20, that's what we've done. But we're going to do it for 50." Many in the industry bid up the skill term. Asia's top quant fund spent 20 years compounding the term underneath the square root.
Ethan Kho70,267 views • 16 days ago
Ex-Man AHL ($70B firm) fixed-income head on breaking into quant: "You've got a much better chance of being hired by the world's best hedge fund from a non-target school with no qualifications than by trading your own money." Rob Carver (Rob Carver) — ex-Man AHL, ran a multi-billion systematic fixed-income book | now a one-man shop across 200+ futures markets "I don't really believe I've found any inefficiency — basically all the money I make is risk premia." We cover: - The career myth that won't die — getting "noticed" by trading your own money is a one-in-a-billion event - Why he insists he's found zero market inefficiencies — it's public risk premia anyone can harvest - Skepticism as the #1 trait — every career error he's seen traces back to overconfidence in a backtest - Why "the best quants come from physics" is mostly path dependence — the Yale-historians thought experiment - His actual process: ~1 new strategy a year, a 1-in-5 strike rate — & he thinks more research would lower it - The real innovation of his last decade — running 200+ futures on a small account, not finding edge - Why he'd never join a pod shop — even though he reckons he could land an offer every couple of weeks Highlights: (00:50) The $1B loss week — why the desk stayed calm (02:45) Why he turned off his P&L email (06:40) Do the best quants really come from STEM? He pushes back (09:55) The Yale-historians trap — path dependence in quant hiring (11:35) The one trait that matters most — skepticism (13:00) The Sharpe ratio's blind spots — & the LTCM case (15:55) Geometric return vs Sharpe — the leverage catch (17:40) Avoiding overfitting — explicit vs implicit fitting (21:55) Why an honest backtest should look worse (23:05) Alpha decay by trading speed — HFT vs slow systems (25:35) Inside the portfolio — the full 10-rule suite (30:30) The career myth — trading your own money won't get you hired (31:55) Why he calls his returns risk premia, not inefficiency (33:55) His real innovation — 200+ futures on a small account (35:40) How Man AHL reviewed, vetoed & shipped new strategies (39:30) Will capital consolidate at Citadel & Millennium? (41:45) "My DMs are open" — & why he'd still never join a pod shop (42:45) The AI talent-war parallel — who you actually want to hire
Ethan Kho179,709 views • 1 month ago
A 4-Sharpe crypto fund. 27 of 28 months positive. Leigh Drogen (Leigh Drogen) is CIO of Starkiller Capital, a crypto quant fund running momentum and market-neutral crypto strategies. His edge is diligence — and a "never lose more than 1%" position rule. We cover: - Why block space is worthless — and the fiber-optics-in-1999 analogy that explains every L1 collapse since blobs launched on Ethereum - Starkiller's "never lose more than 1%" position sizing rule (how it compounds into 27 of 28 positive months) - The Ripple / RLUSD / USCC trade — borrowing at 2.5% against a 5-8% yielding tokenized basis fund, hidden in plain sight on Aave Horizon - How Starkiller dodged the Kelp DAO hack, lending USDC at 17-18% APR while the rest of DeFi was on fire - Why momentum is the only actual persistent alpha (it's the only persistent behavioral characteristic of humans) - "F*ckery risk" on the short book — why Drogen runs a more diversified short book than long book, even when his thesis screams short - Why "sales is way overcompensated" relative to the difficulty of the job — and what that means for ambitious young quants - The 2019 DM from a 21-year-old that became Drogen's biggest career miss — and how Polymarket's Shane Coplin (Shayne Coplan 🦅) actually solved the SEC problem ("USDC and VPNs") Highlights: 00:00 Intro 01:09 Mechanics of a 4 Sharpe market neutral DeFi strategy 03:24 Quantifying protocol risk and code provenance 06:40 Case study: Exploiting incentivized spreads in carry trades 10:53 Three primary sources of alpha in liquid crypto markets 14:28 Capacity constraints and institutional yield compression 18:54 Position sizing via the 1% max loss rule 21:38 Pro-cyclical returns and the risk modulation framework 26:44 Compounding capital through trend following and cross-sectional momentum 33:35 Why momentum is the only persistent behavioral alpha 48:39 Extracting alpha from token unlock schedules and market structure 51:20 Lessons from building Estimize and the SEC/ForceRank fight 55:00 The Polymarket origin story: Arbitraging regulatory hurdles 01:01:45 Career risk premia and the value of "eating sh*t" 01:05:34 Table selection: Positioning your career on the right macro curve
Ethan Kho261,657 views • 3 months ago
"Once you're off the track, you're done." — a fund manager with an 18-year track record on what nobody tells you about a career in money management: Derek Pilecki (Derek Pilecki) runs a financials-focused fund, 21%+ annualized, entire net worth in his own fund. Derek explains: "It's harder for small funds. A lot of the seats are quant based versus fundamental based. It's a more difficult industry." "You have to love investing. You have to want to wake up and read the Wall Street Journal as soon as you wake up." "If you lose your job anytime after the age of 32, it's hard to get another job. The business doesn't hire mid-career analysts." "There's a lot of people who get into the business and then in their thirties or forties have to go find another career." "There are things that people don't talk about. I've had a lot of friends who lost their job in their late thirties or forties and weren't able to find a new seat." "While the rewards can be great on the upside, it can really mess with you mid-career, midlife."
Ethan Kho368,108 views • 4 months ago
How a Point72 research head actually read China: Kirk McKeown explains: "I did a ton of work studying China over the years." "The best way to get a read on China is to call Australia. 45% of Australia's GDP is China." "Signals aren't binary. They're on a spectrum — low grade, medium grade, high grade." "Going into Lehman in August '08, China shut all the ports for the Beijing Olympics." "Pre-buy of Caterpillar equipment to fund construction after the Olympics. Ports closed for three or four weeks — nothing could get in, so everybody bought ahead." "Once the ports opened back up, there was nothing to buy. It creates an air pocket."
Ethan Kho305,747 views • 3 months ago
"In 35 years of institutional finance, I've had one original idea. And I kind of stole it from Joe Stiglitz." Tom Costello (Tom Coste) explains: "Right now there are 8 or 10 million super smart engineering students thinking they're going to write a program trading system, connect it to a Robin Hood API, and poof — a Lambo appears in their driveway." "This is not real life." "They build mean reversion. It doesn't work. Every mean reversion strategy that works has been running for a decade. Or 40 years." "They build SP500 arbitrage. Everybody's doing that too, and they're co-located. They're getting the trade and you're not." "Momentum. PCA. Factor deconstruction. None of it works." "They assume that because they have a new idea for them, it's a new idea for everyone. That's absolutely not the case." "I've only ever done three things. I've bought, I've sold, and I've waited. That's all anybody else is doing either."
Ethan Kho255,142 views • 3 months ago