Odds on Open

In this episode of Odds on Open, TD Quant Matt Schrager discusses the microstructure of municipal bond market making and the technical challenges of extracting alpha from illiquid fixed income instruments. We analyze the shift from low-latency HFT frameworks to the probabilistic modeling and statistical pricing required for securities with fragmented liquidity. Matt details the mechanics of systematic inventory management, risk-adjusted P&L optimization, and the cultural integration of elite proprietary trading teams within institutional balance sheets.Schrager outlines a variant view on finding edge in "ugly," inefficient markets, focusing on the structural opacity of private credit and the electronification of commodities. The discussion covers the evolution of market efficiency, the role of LLMs in credit due diligence, and recruiting strategies for resilient quantitative talent. This episode provides actionable insights for hedge fund analysts, quants, and PMs on the relentless process required to maintain a competitive advantage in evolving market regimes.00:00 Intro00:01:29 Announcing OOO's Newest Sponsor00:02:20 Liquidity and latency differentials in the municipal bond market00:06:37 Probabilistic modeling and statistical pricing for low-frequency instruments00:10:50 Adapting HFT simulation and backtesting to illiquid fixed income00:20:33 Systematic inventory management and risk-adjusted P&L optimization00:27:36 Transitioning proprietary trading culture into a global bank infrastructure00:34:10 Scaling electronic market making into commodities and investment-grade credit00:41:24 Identifying edge in gnarly and inefficient corners of the market00:45:23 Structural opacity and the liquidity evolution in private credit00:56:21 Why elite trading organizations prioritize relentless process over magic01:04:16 Recruiting for resilience and the velocity of fundamental improvement01:11:02 How AI-native skillsets redefine talent in liquid market regimes

In this episode of Odds on Open, we go deep into the mechanics of edge, credibility, and the structural evolution of the hedge fund industry. Host Ethan sits down with Tom, a veteran Quant PM formerly of Tudor Investment Corp and Moore Capital, to deconstruct what separates the top-tier "pod shops" from the bottom 40% of funds that fail to preserve capital.Tom challenges the common perception of market randomness, arguing instead for a deterministic view of market structure where alpha is captured by modeling participant incentives rather than just price action. We discuss the "Unified Field Theory of Finance," the operational reality of running a billion-dollar book, and why the most dangerous trap for a PM is the "gamma trap"—trading steady returns for catastrophic tail risk.00:00 Intro01:18 Building institutional credibility for early-stage managers03:01 The Pareto distribution of hedge fund returns04:25 Applying the Unified Field Theory of Finance to fair value08:14 Trading against human incentives in a deterministic market13:54 Why allocators don’t steal alpha from prospective PMs18:26 Organizational advantages and risk management in pod shops25:16 Evaluating career edge in quantitative finance for 202630:48 Paul Tudor Jones and the art of game selection33:42 Analyzing the economic viability of starting a new fund35:16 Identifying common retail pitfalls: Mean reversion and arbitrage38:55 Why there hasn't been a new trading idea in 15 years43:22 Case study: Building NLP systems and managing strategy decay50:33 Managing tail risk: Physics vs. deterministic financial distributions55:33 Identifying the gamma trap in short-volatility strategies59:10 Career pathing for PMs after a fund blow-up1:07:53 SBF and FTX: Credibility vs. the "Founder-Genius" archetype1:13:44 Establishing proof-of-concept through audited multi-year returns

Sean Emory of Avery discusses the evolution of edge in liquid markets, specifically how to leverage alternative data—from App Store analytics to digital exhaust—to identify fundamental inflection points before they are reflected in the price. We dive deep into Sean’s underwriting process, exploring how institutional investors can use granular data sets to track thesis confirmation and identify a margin of safety in real-time. This conversation provides a technical breakdown of how to separate signal from noise in a market regime increasingly dominated by ultra-short-term microstructure and passive flows.Sean also breaks down his approach to portfolio construction, comparing the risk-return profiles of highly concentrated strategies versus diversified books. He explains why his firm prioritizes "the Six Ms" over standard volatility metrics to mitigate the risk of permanent capital impairment, offering a variant view on traditional risk management. The discussion concludes with the operational realities of the active ETF landscape, the impact of generative AI on market efficiency, and the psychological discipline required to maintain alpha when storytelling and euphoria distort traditional valuation frameworks.

In this episode of Odds on Open, quant trader Scott Phillips joins the pod to break down why crypto remains "the dumbest market in the world" and a goldmine for systematic edge. We dive deep into table selection and why the lack of institutional competition allows for Sharpe ratios exceeding 2.0 through basic trend following and momentum strategies. Phillips explains the mechanics of market inefficiencies, from the reflexivity of on-chain liquidity to the alpha found in tracking price-insensitive buyers and VC exit liquidity. For hedge fund analysts and quants, this is a masterclass in identifying liquid market anomalies that TradFi has long since arbitraged away.The conversation shifts to the technicalities of portfolio construction and risk management within the "dark forest" of DeFi. Scott details his transition from click trading to launching Hyper Trend, a tokenized on-chain hedge fund executing mid-frequency crypto strategies on Hyperliquid. We explore the microstructure of funding rates, the carry trade, and how to model counterparty risk when dealing with exchange-specific incentives and North Korean state actors. Whether you are a PM focused on factor analysis or a trader looking to exploit mean reversion in altcoins, this episode provides a raw, credibility-forward look at capturing beta-neutral returns in the world’s most volatile regime.00:43 Table selection and the math of competitive alpha06:21 Why basic trend following yields outsized Sharpe in crypto08:49 Why market inefficiency persists despite institutional inflows14:58 Price insensitive buyers: Cults, VCs, and North Korean hackers17:17 Factor analysis and the size-decay effect in shitcoins25:40 The structural edge in mid-frequency crypto strategies32:43 Tokenized DeFi vaults and on-chain hedge fund governance40:43 Designing a robust portfolio: Equal weighting vs. MVO44:21 Sourcing alpha from ghost chains and VC exit liquidity49:58 Exploiting market maker contracts and post-listing drift53:55 Operational alpha: Managing margin and manipulated funding rates01:01:13 Shifting from quant to CEO: Identity fluidity and mastery01:11:28 How to bridge the mentorship gap with elite traders01:22:38 Building network triads: The secret to compounding social capital01:29:23 Why 10x goals require total identity transformation

Get 10% off on Fundamental Edge: https://www.fundamentedge.com/odds-on-open-podcastIn this episode of Odds on Open, Ethan Kho sits down with Brett Caughran, founder of Fundamental Edge and a former Portfolio Manager at elite Tiger Cub and Multi-Manager (MM) firms.As generative AI and agentic workflows commoditize the "desktop research" layer of investing, the bar for generating idiosyncratic alpha has never been higher. Brett breaks down the specific frameworks—including ETIC (Everything There Is To Know) and the Focus 5—that top-tier pods use to identify mispriced securities and isolate key drivers before they are priced in by the market.We dive deep into the market microstructure shifts caused by the rise of indexers and factor-based quants, explaining why increased volatility is a gift for fundamental investors with the stomach for Bayesian updating. Brett also provides a roadmap for the "New Junior Analyst," shifting the focus from manual model-cranking to high-leverage primary research and AI orchestration.00:00 Intro01:29 Frameworks for developing a differentiated variant perception05:16 Financial drivers vs. narrative cycles: The Focus 5 framework08:29 Analyzing the stock vs. business: Bayesian updating in public markets12:52 AI as an intellectual power tool vs. consensus "alpha slop"17:21 Accelerating the hunch-to-hypothesis pipeline with AI sniff tests21:52 The evolution of junior analysts: From data entry to primary research28:46 Why market microstructure and behavioral alpha prevent index efficiency34:48 New meta-skills: Debugging models and the expectations gap muscle38:44 Training junior analysts: Earning the right to use power tools44:34 High-value workflows: CEO credibility analysis and guidance tracking48:28 LLMs as orchestration tools for human primary research54:55 Teachable scientific process vs. revealed investment judgment57:54 Common threads across Multi-Managers, Single Managers, and Tiger Cubs59:49 Curiosity as a meta-skill and the art of system thinking

In this episode of Odds on Open, Ethan Kho sits down with Derek Pilecki, founder of Gator Capital Management, to deconstruct his 20%+ annualized track record in the financial sector. While many generalist PMs view financials as a "sleepy backwater" or overly complex, Derek explains how he extracts alpha from regional banks, brokerages, and insurance companies by identifying fundamental business changes before they are reflected in the tape.The conversation moves from the microstructure of bank underwriting in a post-Dodd-Frank regime to the practicalities of portfolio construction, including why Derek has expanded his concentration from 25 to 40 names and his strict discipline against "averaging down" on losers. We also dive into the private credit narrative, the actual risk of systemic leverage in non-bank financials, and how generative AI is shifting the valuation multiples of moaty info-service businesses like Morningstar and FactSet.00:00 Intro01:06 Derek's +21% annualized return track record02:50 Fundamental business change vs market noise in Robinhood05:25 Portfolio construction: Concentration limits and adding to winners09:09 Sourcing alpha and identifying three-year doubles in financials12:44 Developing edge through repetition and management team cycles14:16 Why the post-GFC regime fundamentally changed bank underwriting17:07 Assessing tail risk and leverage in the private credit market21:23 AI-driven market dispersion and identifying moaty businesses24:11 Why shareholder base turnover matters for timing broken charts25:57 AI disruption vs trust-based moats in financial services29:37 Integrating AI into fundamental research and SEC filing analysis32:31 Scaling regional bank positions and managing liquidity constraints35:39 Risk management: Permanent capital loss vs mark-to-market volatility37:12 Capacity constraints: Optimizing for returns over AUM scale44:14 Behavioral edge and avoiding the "degree of difficulty" trap50:39 Career risk and the reality of active money management54:18 Breaking into the industry via public stock write-ups

In this episode of Odds on Open, we analyze the technical architecture of the data science layer within fundamental hedge funds. Guest Matei Zatreanu, founder of System2, discusses the tension between generative AI and the search for outlier-driven alpha. We move beyond the hype of LLMs to discuss the practicalities of expert network automation, the causal mapping of second-order macro effects, and why the most successful PMs treat their investment process as a craft rather than a business operation. The conversation also explores the structural shift from single-manager funds to multi-manager platforms and the specific incentive alignment strategies used to retain quant talent in high-stakes environments.(00:00:00) Intro(00:00:53) Talent constraints and outlier detection in the data science layer(00:05:38) LLM customization: Differentiated alpha vs. the consensus echo chamber(00:10:18) Automating the mosaic: AI interview agents and qualitative data synthesis(00:20:33) Mapping causal relationships and second-order macro effects via graphs(00:26:33) Curiosity as the ultimate constraint for information-rich investors(00:31:43) Multi-manager platforms vs. the rise of independent single managers(00:37:58) Solving incentive alignment and analyst retention via internal fund-of-funds(00:44:03) Managing negative network effects and custom research one-offs(00:48:33) Whale hunting: High-ticket pricing and the billionaire value mindset(00:54:58) Zero-to-one incubation: Leveraging unique market access for business spin-outs(00:59:08) Romanian roots to billionaire circles: Mentorship and aiming high(01:07:48) PM as "Doctor": Why founders prioritize craft over business operations

This episode was filmed on Thursday, March 12, 2026.Greg Newman, founder and CEO of Onyx Capital Group and the largest liquidity provider in oil derivatives markets, on what it actually looks like to run a market-making book when liquidity breaks down entirely. What most participants misunderstand about oil vol is that the outright price — Brent, WTI — is a proxy, not the market; the real information lives in time spreads, regional diffs, and niche contracts that only a handful of firms have visibility into. Why fair value discovery in a dislocated market requires abandoning automation, reverting to manual process, and using physical market participant behavior — refiners, producers, airlines — as a real-time signal rather than a lagged one.Greg co-founded Onyx Capital Group in 2016 after a decade trading crude and refined products across increasingly niche oil derivatives contracts. Onyx built its position by stepping into the vacuum left by banks exiting commodities post-Volcker, becoming the dominant liquidity provider across European, Middle Eastern, and Asian oil markets with an estimated 20–50% market share in several key contracts. The firm now operates market-making desks across London, New York, Dubai, and Singapore, and has expanded into data services, a single-dealer platform, retail brokerage, and physical trade finance — building a vertically integrated oil markets infrastructure business from a pure prop-trading foundation.In this episode we cover:•⁠ ⁠Why trading oil outrights during the dislocation was a losing game — and where the real edge was•⁠ ⁠Fair value discovery on Sunday night: how Onyx priced contracts when every historical model broke•⁠ ⁠Physical market reflexivity: how refiners, producers, and airlines all become forced actors at key price levels•⁠ ⁠Geopolitical signal extraction: options open interest and off-hours order flow as an information edge over Polymarket•⁠ ⁠Regime-break risk: why government intervention in exchange mechanisms is the tail risk that keeps Greg up at night•⁠ ⁠Countercyclical talent investment and why Onyx's worst years built its best crisis infrastructure•⁠ ⁠From prop shop to platform: data, single-dealer, retail brokerage, and credit as extensions of liquidity edge•⁠ ⁠Why Onyx is building toward a hedge fund — and why track record discipline is holding them backTimestamps:00:00 Intro 00:48 Oil market volatility: making sense of the dislocation 04:05 Outright vs. spread positioning: where the real edge was 05:10 How Onyx manages process when liquidity breaks down 10:18 Pricing fair value on Sunday night with no precedent 16:48 Physical market participants and the reflexivity of hedging behavior 20:22 Prediction markets as an information signal — and why Onyx stopped using them 25:26 Options flow as the real tell for informed geopolitical positioning 28:17 What it feels like running a global market-making book through a crisis 33:05 Regime-break risk: when exchange mechanisms themselves fail 36:25 Countercyclical investment in talent and infrastructure 42:20 From prop shop to liquidity infrastructure: building a durable valuation 48:50 Why Onyx is building a hedge fund — and what's holding them back 57:50 Media, brand, and market disruption as compounding assets 01:05:47 The most surprising thing after 14 years of building Onyx

Legendary poker champion, decision scientist, and author of "Thinking in Bets," Annie Duke deconstructs the mechanics of decision-making under uncertainty, shifting the focus from high-variance outcomes to the rigor of positive expectancy and robust process. Leveraging her background in professional poker and cognitive psychology, Duke explores how loss aversion and resulting—the cognitive trap of equating outcome quality with decision quality—can degrade a trader's edge and lead to suboptimal portfolio construction. The conversation moves beyond theory into the practical application of base rates, reference classes, and mental time travel to combat temporal discounting, providing a masterclass for quants, PMs, and analysts on how to refine their probabilistic worldview and neutralize the noise of short-term volatility.00:00 Intro01:12 Defining bets as resource allocation under uncertainty04:52 Positive expectancy vs. outcome-based evaluation06:11 Resulting: Why outcomes are not proxies for decision quality15:19 Calculating expected value in high-variance career paths18:55 Moving from implicit intuition to explicit decision modeling24:27 Using base rates and reference classes for startups30:26 Psychological traits of elite risk takers and traders31:33 How prospect theory and loss aversion distort risk45:12 Deconstructing gut feel and the role of intuition49:36 Evaluating optionality and impact in fast-moving environments57:13 Mental time travel: Tools for managing temporal discounting01:01:31 Quantifying the intersection of luck and hard work01:04:43 Internalizing a probabilistic worldview for long-term edge

Zachary A. Levitt joins the pod to break down the architecture of a capacity-constrained multi-manager platform designed to harvest high alpha loads in niche, idiosyncratic markets. We dive deep into portfolio construction beyond the "Big Four" pod model, focusing on inverse-volatility weighting, discretionary risk overlays during regime shifts, and the mechanics of screening for relative value arbitrage strategies with minimal factor exposure. Zach explains his transition from a data-driven biotech alpha capture book to running a center book, detailing how he identifies micro-regime persistence and manages the microstructure of a lean, performance-aligned firm. This conversation is a masterclass for allocators and quants on building a non-correlated return stream by targeting the liquidity gaps and specialized incentives that larger, multi-billion dollar funds are forced to ignore.00:00 Intro01:02 The primary constraint for a young multi-manager03:13 Screening for niche strategies and consistent track records06:03 Maximizing idiosyncratic P&L through relative value arbitrage08:19 Tactical sizing and capturing micro-regime persistence12:43 Balancing inverse-vol weighting with discretionary risk overlays15:41 Case study: Rebalancing small-cap L/S during market corrections17:37 Distilling signal from noise in multi-manager portfolio oversight22:02 Coachability and removing emotion from the PM feedback loop25:52 Alpha capture in biotech via options market data30:20 Scaling the boutique multi-manager business model34:02 Disrupting the "Big Four" pods with capacity-constrained strategies42:21 Unit economics of a lean, performance-driven platform53:09 LP management and optimizing the business development funnel1:00:19 Moving from portfolio management to operational process efficiency1:05:10 Future of the industry: Consolidation vs. niche boutiques1:08:53 Roadmap for launching a niche multi-manager fund

Check out Carbon Arc here: https://www.carbonarc.co/Kirk McKeown, founder and CEO of Carbon Arc and former senior investor-facing operator across Glenview and Point72, on how alpha migrates as market structure, tooling, and competition evolve. What most investors misunderstand about “edge” is that it is rarely static and often lives in process design, information capture, and interpretation of small narrative inflections. Why hit-rate systems, decision trees, and data structure matter now as models commoditize and the marginal advantage shifts toward differentiated inputs and synthesis.Kirk started his career at Tudor Investments during the late-1990s cycle, then worked at Glenview Capital under Larry Robbins where he built and led primary research capabilities supporting a concentrated, long-horizon portfolio process. He later spent 8.5 years at Point72 supporting a multi-manager environment optimized around catalyst-driven, variant-view investing, high at-bat volume, and repeatable organizational process. Across these seats, he worked directly with investment teams on improving idea generation, hit-rate, and conviction through compliant information collection, supply chain and value chain work, and rigorous feedback loops.In this episode we cover:- Why alpha “moves” over time and how competitive advantage migrates with market structure and tooling- Hit-rate vs slugging frameworks across concentrated portfolios and multi-manager platforms- A research function’s only mandate: lift idea flow, hit-rate, or conviction without contaminating decision-making- Building edge via compounding domain knowledge, field research, and leading indicators before consensus data prints- “Main Street becomes Wall Street”: model-driven decisioning, data decimalization, and pricing data like a utility- Inventory as the core causal variable behind boom-bust cycles in fundamentals and supply chains- Factor frameworks as a scaling mechanism for research: market structure, business model, and decision-tree priorsTimestamps:(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 alphaFollow Kirk McKeown:LinkedIn – https://www.linkedin.com/in/kirk-mckeown-400607214/

My Substack: https://ethankho.substack.com/Alfonso Pecatiello — known as "Alf" and founder of The Macro Compass and founder of Palinuro Capital, a macro hedge fund— joins Ethan Kho to break down the frameworks behind global macro trading, real economy money creation, and what it truly takes to build a macro hedge fund from the ground up.Alfonso Pecatiello spent years as a senior portfolio manager at ING overseeing a multi-billion dollar fixed income portfolio before founding Palinuro Capital. In this episode, Alf shares the macro investing edge that drives his process: why central bank QE and bank reserves are largely irrelevant to real economic outcomes, how commercial bank lending and government fiscal deficits are the true engines of money creation, and why tracking the second derivative of real economy money printing is one of the most powerful signals in global macro trading today.But Alfonso Pecatiello doesn't stop at markets. The Macro Compass founder opens up about the brutal reality of launching a macro hedge fund with no seed money, no GP stake deal, and an 80% industry failure rate. He shares the moment Palinuro Capital nearly didn't survive — and the risk management mindset that carried him through.This episode covers global macro trading strategy, hedge fund position sizing, portfolio diversification, tail risk management, factor-neutral mandates, and the real process behind founding a hedge fund from scratch.If you're interested in macro investing, hedge fund careers, global macro strategy, money creation, central bank policy, or fund management — this is essential listening.

Former Susquehanna International Group (SIG) Head Trader Andrew Courtney breaks down the reality of being a quant trader and market maker at one of the world's elite proprietary trading firms. He reveals what trading floors actually look like—multiple monitors covered with flashing numbers, signals, and price movements that traders analyze all day with zero lunch breaks and constant attention on market microstructure. Andrew explains how SIG's legendary poker training culture shapes traders' ability to think probabilistically, make decisions under uncertainty, and justify every bet both quantitatively and qualitatively. He shares candid insights about who should (and shouldn't) pursue trading careers, the transition from floor trading to electronic markets, and how the tight-knit network at prop trading firms differs dramatically from consulting or investment banking paths.Andrew now runs Kalshinomics, a prediction markets analytics tool, and writes The Whirligig Bear on Substack where he analyzes opportunities in Kalshi, Polymarket, and emerging prediction market platforms. He goes deep on finding edge in prediction markets—from identifying inefficient markets with liquidity incentives to using ChatGPT and AI tools for handicapping obscure Grammy categories. Andrew explains market efficiency frameworks, how to assess who you're trading against, and why some markets (like low-volume Grammy categories) offer better opportunities than hyped meme markets. He also tackles the casino-ification of America debate, insider trading concerns in prediction markets, and whether these platforms are a net good or bad for society.We also talk about...The real day-to-day of quant trading and market making at SIG: staring at screens all day, monitoring signals, and staying alert for when markets go off the railsWhy SIG's poker training program—playing for hours daily, turning over cards after every hand, and defending each decision quantitatively—builds world-class tradersHow thinking in bets becomes second nature and why Andrew now frames every decision (like private school vs public school) as an expected value calculationThe cultural differences between floor trading (loud voices, physical presence in the pit) versus upstairs electronic trading (surrounded by sharp peers and data)Why prop trading careers build narrow, dense networks compared to consulting or investment banking, and what that means for long-term career optionalityFinding edge in prediction markets: liquidity incentives, identifying who you're trading against, and why some markets are wildly inefficientTrading strategy and bet sizing: when to use Kelly criterion, how to scale into positions, and Bayesian updating based on how the market reacts to your tradesThe insider trading debate in prediction markets and why Andrew thinks it's corrosive to incentives, trust, and long-term market qualityRisk transfer opportunities: using prediction markets for insurance-like hedging (Florida hurricane risk, California earthquake exposure) rather than pure speculationWhether prediction markets are good for society: the value of probabilistic news context versus the risk of casino-ification and degenerate gamblingCareer advice for aspiring traders: evaluating if you can handle constant screen time, limited networks, and high-variance outcomesHow to apply expected value thinking to everyday life: insurance decisions, risk tolerance, and when not to over-optimize (don't EV calculate marriage)The future of prediction markets: institutional adoption, regulatory uncertainty, and whether amateurs can still compete before professionals crowd out edgeWhy Kalshinomics focuses on analytics and custom interfaces for serious traders rather than trying to be the "Bloomberg Terminal" of prediction marketsLessons from SIG on decision-making, probability, and building systems that extract signal from noise in high-frequency, high-stakes environments

Sinan Xin manages an emerging markets tech hedge fund from New York, investing across China, Latin America, Southeast Asia, and beyond. In this conversation, he shares how he builds edge in some of the world's most volatile markets.We discuss:Why conviction can become bias—and how to tell the differenceBuilding durable relationships across geographies you're not fromThe evolution of edge: from reading 10-Qs at the library to AIWhy understanding your own behavior matters more than any toolHow to think about career decisions when everyone's chasing the same thingPortfolio construction strategies for managing emerging market risksWhy the best English-speaking management teams often underperformSinan explains how his background—born in China, raised in the US, working in tech M&A at Lehman Brothers before it collapsed—shaped his investment philosophy. He reveals how standing up a dropshipping website taught him about e-commerce software, why he visits cattle farms at 4am, and how private market relationships help him spot public market inflection points.The conversation turns personal as we explore career alpha vs. beta. Sinan pushes back on the idea that smart people should simply pick "the most liquid market" (like AI today), arguing that true edge comes from self-knowledge, not chasing prestigious outcomes.For anyone thinking about investing, careers, or how to build differentiated views in efficient markets, this is a masterclass in independent thinking.

In this episode, hedge fund manager Alix returns to Odds on Open to tackle what he calls the most important problem facing young investors today: the complete loss of analog training skills that created the greatest investors of previous generations. Alix runs a hedge fund that deliberately avoids AI tools for analysts, believing they're "extremely dangerous" because they optimize analysts in ways that sub-optimize fund performance. He breaks down why creativity comes from constraints, not abundance—why getting to consensus faster with AI actually makes you worse at generating alpha. The core issues: Gen X is the last generation trained with analog tools, the "junior-senior problem" where hedge funds realize they don't need junior analysts anymore, the attention span crisis, and why Silicon Valley executives don't let their kids use the products they built. Alix introduces the "10K and a pencil" approach, inspectional reading techniques to extract 80% of a book's value, frameworks from Charlie Munger and Peter Kaufman, Art of War principles applied to predator-prey business dynamics, and why reading trains every critical investment skill—pattern recognition, visualization, reading between the lines, leaps of judgment.

Disclaimer: This is not a financial promotion and must not be seen as advice, but only as an educational pieceCláudia Quintela has spent 25 years connecting early-stage hedge fund managers with institutional capital. She's worked across FX, macro, and systematic strategies at State Street, UBS, Morgan Stanley, and Blenheim Capital, one of the world's largest commodity managers at its peak.In 2017, she founded Vibe Advisors, an independent advisory boutique focused exclusively on helping emerging managers—particularly systematic, CTA, and macro funds—navigate the hardest part of launch: raising that first $50 to $200 million when you have limited track record, tight capacity constraints, and institutional investors demanding day-one infrastructure.She specialises in the messy reality of early-stage fundraising: fee pressure, seed negotiations, managed account structures, positioning for allocators who need to sell your strategy internally, and translating complex quant models into language that gets you through the door. Her client base skews heavily toward liquid macro and model-driven managers.Today, Cláudia runs a portfolio career: advising fund managers and investors, writing weekly about entrepreneurship and capital raising, hosting webinars on AI tools for investor relations and marketing automation, and speaking on panels about women in finance. She's an advocate for the sisterhood and believes the next generation of emerging managers will look different from the last.Based in London and originally from Porto, she holds an MSc in Finance from LSE and is a CFA charterholder. She's here to talk about what actually works when you're trying to raise institutional capital at the hardest stage—and how emerging managers can build smarter, leaner operations using the tools that didn't exist when she started.

In November 2025, I hosted a fireside chat at Columbia University with Deepak Gurnani, founder of Versor Investments, a $1.4 billion [1] quantitative hedge fund based in New York with offices in New York and Mumbai. Deepak spent two decades at Investcorp, where he built and led the firm’s hedge fund division. In 2013, he stepped away to found Versor with a singular goal: to build a research-driven quantitative firm focused on leveraging alternative data. This conversation is a continuation of the story we began on Odds on Open with Nishant Gurnani and DeWayne Louis, two of Versor’s partners. In that episode, we explored the systematic strategies that Versor runs. In this fireside chat, we go upstream to understand how it all began.We talk about:- Deepak’s journey from IIT to Citigroup to Investcorp- How the hedge fund industry looked in the 1990s versus today- What it really takes to spin out and build a quant firm from scratch- Why Versor adopted cloud computing and alternative data years before most peers- How small firms compete with giants like Citadel, Millennium, and Jane Street- What Deepak looks for when hiring researchers- Why “value proposition” is the starting point for any new fund- The mindset required to build something that lastsVersor LinkedIn Page: https://www.linkedin.com/company/versorinvestments/Research Repository (“Athenaeum”): https://www.versorinvest.com/athenaeum/1. Data as of December 31, 2024. AUM as per SEC definition for the purposes of item 5F on the ADV Part 1a. For important disclosures, please visit: https://www.versorinvest.com/terms-and-conditions/

Ethan Kho interviews David Orr, a former professional poker player turned hedge fund manager. They discuss David's journey from poker to founding Militia Capital, his investment philosophy, and the lessons learned from both industries. David shares insights on risk management, the importance of finding asymmetric bets, and the challenges of the hedge fund industry. He also offers advice for aspiring investors and reflects on the future of hedge funds.

Discover how top multi-manager hedge funds like Citadel and Millennium attract the brightest minds, the evolving talent strategies in the industry, and the unique traits that set successful candidates apart. Whether you're an aspiring finance professional or just curious about the inner workings of hedge funds, this episode offers valuable insights into the competitive landscape of financial services.

What’s the difference between prediction markets trading and equities trading? On Odds on Open, the world’s #1 prediction markets trader Domer explains how prediction markets work as a form of information-based trading, where news and signals can arrive at any moment, forcing continuous price discovery and repricing. Unlike stock markets, where returns often depend on long-term growth, valuation multiples, and market beta, prediction market strategy focuses on information timing, news flow, and market reaction to new data. Rather than forecasting final outcomes, traders focus on event-driven trading, short-term price movement, and probability trading, exploiting mispriced probabilities and trading event contracts instead of holding positions to resolution. This approach allows traders to generate expected value (EV) and highlights the difference between active trading vs passive investing.Domer also explains how many participants concentrate on high-volume headline markets, while traders look for prediction market edge in event contracts trading across smaller markets. On platforms like Polymarket and Kalshi, opportunities exist in alternative markets and micro events that are less crowded and prone to pricing errors. By specializing in specific market categories and focusing on liquidity, volume, and time horizon, traders can adjust position sizing and holding periods to match their edge. This approach mirrors quantitative trading and event-driven strategies, where domain knowledge and execution outperform broad speculation.Other subjects discussed...How prediction markets trading focuses on short-term price movement and active trading rather than holding event contracts to resolution.Prediction market strategy is based on exploiting mispriced probabilities to generate expected value (EV).Prediction market edge is most common in micro markets and sub-events with lower liquidity and attention.Event contracts trading rewards traders who identify information-driven repricing before markets adjust.Information-based trading in prediction markets reacts to discrete news rather than continuous market noise.Probability trading requires distinguishing mean reversion from true regime shifts after breaking news.Losses in prediction markets are often caused by crowded trades and poor position sizing, not direction.Position sizing must scale with edge and uncertainty to preserve long-term expected value (EV).Platforms like Polymarket and Kalshi allow large traders to temporarily distort prices.Capital concentration in alternative markets can create opportunity for smaller traders.Long-term success depends on repeatable decision-making rather than individual outcomes.Prediction markets exhibit less random variance than equities because prices move on information.Poker develops risk tolerance and variance management applicable to prediction markets trading.Regulation is likely to limit influenceable event contracts while allowing large markets to grow.

❗ACCESS OPPORTUNITIES EXCLUSIVE TO OOO VIEWERS: https://app.youform.com/forms/e2jpsj4z ❗How do top hedge funds actually hedge trades? At SIG, traders were often told not to hedge. Most assume elite trading firms hedge every position, but Susquehanna (SIG) built its edge by avoiding hedging when trades had positive expected value (EV). Kris Abdelmessih—who later moved into portfolio management roles in energy-derivatives trading outside SIG—explains the firm’s model: centralized risk, large position size, and no hedging unless residual exposure distorted EV or created excessive P&L variance. He shows how this framework let SIG capture options market making edge, tighten spreads, and outcompete firms that hedged mechanically.This episode is definitely a new treat for viewers, linking options pricing, trade sizing, and execution to concepts used in algorithmic trading, alpha generation, portfolio optimization, multi-strategy hedge funds, pod shops, and even sports betting edge.We also discuss...How early exposure to the Amex options pits revealed structural options market making edge from wide bid-ask spreads and fragmented exchanges.Why observing senior traders shaped his understanding of expected value (EV), risk of ruin, and edge compounding in proprietary trading.How late-1990s retail flow and weak market microstructure created abnormal options edge before competition increased.The shift from easy trading to tougher environments as order flow toxicity rose and spreads tightened.Why measuring growth in trading comes from recognizing past mistakes, skill vs. luck, and the paradox of skill.SIG’s belief that markets are mostly efficient and why uncovering inefficiency requires serious labor, pattern recognition, and deliberate practice.Why traders must assume customer order flow is informed, reinforcing humility and disciplined risk models.How SIG’s structured recruiting and education flywheel accelerated mastery of options pricing and liquidity provision.Lessons from working with elite performers like Jason McCarthy and seeing extreme competitive drive and work capacity.Why outlier PMs excel across domains, linking athletic intensity to portfolio management and hedge fund strategies.How choosing between discretionary and systematic trading requires understanding personal strengths and cognitive style.Why young traders must tune out status games in high-prestige quant trading, pod shops, and multi-strategy hedge funds.How moving from SIG to a Chicago prop trading firm introduced him to backer models, P&L splits, and strict risk budgets.Managing natural gas options risk through inventory risk, vega exposure, and shifting volatility regimes.How designing options-themed games for his kids teaches expected value, open outcry trading, and real-world decision-making.

In this episode, former Tower Research and Flow Traders quant Annanay Kapila breaks down the reality of high frequency trading, HFT strategies, and top automated trading systems. He explains what quant researchers actually do inside elite firms—where world-class math talent competes in a true zero-sum market. Annanay goes deep into market making strategies, latency engineering, alpha generation, and how quant trading teams iterate models nonstop to stay competitive. It’s one of the clearest looks at quantitative finance, execution risk, and the real mechanics behind profitable automated trading systems.Annanay also gives a rare inside look at Tower Research crypto trading during the 2022 crypto winter, breaking down crypto trading strategies, API latency edge, and structural weaknesses across early exchanges. He shares first-hand insights from the FTX collapse explained—how withdrawals froze, how APIs failed under stress, and how liquidity vanished in real time. These experiences shaped how he thinks about exchange design, risk controls, and the future of fintech startups. All of this leads into QFEX, a new platform aiming to bring crypto-style perpetuals to traditional assets with real infrastructure and institutional-grade reliability—what Annanay calls “FTX without the fraud.” We also cover hedge fund strategies, automation, and the next generation of trading platforms.We also talk about:How high frequency trading, low latency trading, and market microstructure really work inside Tower and FlowThe two schools of quant trading: Chicago-style traders vs MIT-style systematic tradingHow firms build market making strategies, order book dynamics models, and short-horizon alpha signalsWhy HFT strategies have low capacity and how firms try to scale into longer-term systematic tradingThe shift from pure spread capture to predictive statistical arbitrage and directional signalsWhy prop firms want to launch hedge fund strategies and fee-based businesses like Citadel and Two SigmaWhat quant researchers actually do: feature engineering, execution algorithms, and avoiding overfitting in machine learning for tradingHow QFEX is built: exchange architecture, matching engines, and API latency for 24/7 automated trading systemsHow Annanay hires: identifying real contributors, evaluating technical depth, and filtering candidates for quantitative finance rolesInside Tower Research crypto: crypto trading strategies, API edge, and managing exchange riskFTX collapse explained: liquidity freezes, failed withdrawals, halted APIs, and real-time risk management failuresThe future of trading: fintech startups, algorithmic trading, perpetual futures, and next-generation investing platforms

Former Citadel MD Michael Watson gives a direct look inside a multi-strategy hedge fund and what it was like working under Ken Griffin. He breaks down Citadel’s pod-shop model, its high-intensity capital allocation frameworks, and how Ken built a concentration of talent across quant trading, quant research, engineering in quant, discretionary equities, Python/C++, data engineering, and alternative data. Michael explains how Ken’s recruiting philosophy and performance expectations turned Citadel into a benchmark for the entire hedge fund industry.He also outlines the reality of day-to-day portfolio management and hedge fund engineering at scale: long hours, translating PM mental models into production systems, and building infrastructure for systematic investing. Michael discusses compensation dynamics, pass-through economics, and the skills required for tech jobs in quant trading, breaking into quant finance, and high-impact quant research roles. He closes with how Hedgineer — an all-in-one technology stack for hedge funds he launched after Citadel — uses generative AI for finance to bring multi-manager-grade technology, research pipelines, and analytics to emerging managers.We also discuss...How Citadel evaluates talent across quant trading, discretionary equities, data engineering, and front-office technology within modern hedge fund infrastructureThe role of engineering in quant and why understanding PM mental models is as important as writing code for consistent edgeDeep technical dives into Python internals, performance bottlenecks, and production-grade data pipelines for researchThe difference between systematic investing and discretionary research, and how engineers support both through advanced risk modelingWhy multi-managers dominate through capital allocation frameworks, diversification, and risk controls in pod-based structuresThe economics behind pass-through compensation, PM slope, and how front-office compensation is structured inside the pod-shop modelHow Citadel organizes research, data, and infrastructure across pods in the pod-shop modelThe benefits and limitations of alternative data and how PMs convert domain expertise into alpha generationThe rise of generative AI for finance and its impact on research, risk, portfolio analytics, and model deploymentHow Hedgineer deploys forward-engineered technology stacks for hedge funds and emerging managersBuilding knowledge graphs, MCP servers, unified data pipelines, and research platforms for portfolio management and risk modelingCareer advice for breaking into quant finance, quant engineering career paths, and building leverage through engineering skillsUnderstanding allocator sophistication vs. multi-manager scale in modern hedge-fund ecosystemsThe tension between consolidation (Citadel/MLP/Baly) and the democratization of quant tooling for smaller managersHow startups and emerging managers use AI, infra modernization, and research automation to compete with large multi-managersWhy tech jobs in quant trading increasingly require both business literacy and deep engineering intuitionLessons from eight years inside Citadel’s operating model, performance expectations, and front-office compensation structuresWhat separates elite PMs and analysts in quant research, idea generation, and alpha generationHow Python for finance careers gives engineers leverage across research, analytics, and production systems

What’s it like being a trader at SIG? At Susquehanna International Group, Todd Simkin has trained some of the world’s best traders using poker strategy, probabilistic games, and decision-making under uncertainty that mirror real-world quantitative trading. In this episode, Todd breaks down how SIG teaches trading interns Bayesian updating, asymmetric information, market microstructure awareness, and communication under pressure. From reading opponents at the poker table to interpreting order flow as a market-making trader, he explains how these game-theoretic models build trading intuition, strengthen probabilistic judgment, and sharpen the edge required for systematic trading and derivatives trading.Todd also dives into the traits that distinguish exceptional traders from simply intelligent ones — humility, truth-seeking, and the ability to update beliefs quickly when new information arrives. He explains how SIG screens for these qualities in interviews, what it’s like working or interning at SIG, and why technical skill alone isn’t enough for options pricing or market-making. Todd breaks down how real meritocracy works inside a flat trading desk, how traders collaborate to refine ideas, and how the best quants learn to think critically, debate openly, and iterate their decision process rather than operate alone.How market-making works at SIG and how traders interpret order flow, liquidity, and real-time signalsWhat Bayesian updating looks like in practice during a live trading sessionHow trading systems, not just individuals, drive performance in modern quantitative tradingThe structure of SIG’s pods and how traders collaborate inside a flat trading deskWhy communication and idea-sharing matter more than hierarchy in quantitative researchInsights into SIG’s interview process, including probabilistic reasoning, game situations, and ambiguity testsWhy SIG prioritizes truth-seeking culture and “attacking ideas, not people” in decision-makingWhat it’s like interning at SIG and how long-term projects reveal real trading aptitudeHow SIG evaluates technical skill vs. judgment vs. adaptability in new hiresWhy systematic trading requires parameter tuning, model monitoring, and rapid belief updatesHow traders combine options pricing, market microstructure, and private information to form an edgeThe role of sports trading, insurance risk, and prediction markets inside SIG’s broader ecosystemHow SIG thinks about risk transfer, volatility events, and pricing uncertaintyWhy Susquehanna moves into new businesses only when it can be best-in-classThe philosophy behind SIG’s expansion into prediction markets (Kalshi, PolyMarket, etc.)The economics of risk indemnification, NIL deals, promotions, and event-driven insuranceHow traders apply game-theoretic optimal reasoning beyond poker — in pricing, hedging, and model design

John Knorring spent over a decade on the Goldman Sachs trading floor, leading natural gas trading through the 2000s—a period defined by trading in a financial crisis, Hurricane-driven volatility, the Amaranth blow-up analysis, and trading during 2008 when bank desks had to price massive option books overnight. He explains how bank trading desks, pit traders, handwritten tickets, and early prop trading shaped risk management in trading, how hedge fund risk systems evolved under stress, and why trading psychology mattered in fast-moving energy commodities trading.John then breaks down the transition to electronic markets, the rise of algorithmic trading, and how the broader electronic trading evolution compressed spreads but expanded opportunity for strong discretionary trading strategies. He contrasts Goldman’s flow-driven environment with DRW trading strategies, explains why some investment strategy decisions still require human judgment in regime shifts, and shows how his commodities background led to building Green Tiger Markets—a new platform transforming the Philippines energy market.We also discuss...Hedge fund trading on early bank trading desksHurricanes, volatility spikes, and the Amaranth blow-upPricing massive books during financial crisis tradingOpen-outcry pits, voice execution, and price discoveryHow Goldman built risk systems for huge positionsFundamentals of natural gas trading and energy marketsStorage cycles, weather models, and pipeline flow dataHow paradigm shifts shape trading psychologyEvolution of algorithmic trading and market microstructureWhen bid-ask compression increased trader P&LWhy discretionary traders lost edge to commodity algosLessons from discretionary vs systematic trading careersThe path from Goldman to DRW prop tradingBuilding Green Tiger Markets for PH electricity hedgingHow electricity forward markets unlock investment in emerging economies

How do you start a hedge fund—and where should you launch it? Daniel Xystus has done both. From Los Angeles quant to Chicago portfolio manager to CIO in Hong Kong and the Middle East, Daniel now helps new hedge fund managers navigate fund setup, regulation, and operations. We break down what it really takes to launch a hedge fund—choosing your fund domicile, building professional infrastructure, and avoiding the operational mistakes that quietly kill most funds. Daniel explains how fund structures like Cayman, UCITS, and Singapore’s VCC differ, and why getting operations, compliance, and risk management right often matters more than alpha generation itself.We also explore how global macro and quantitative trading strategies adapt across regions—from Asia ex-Japan markets to Dubai and Abu Dhabi investment funds. Daniel breaks down how Asia hedge funds deal with high shorting costs, liquidity issues, and regulatory complexity, and why Middle East family offices are emerging as powerful allocators. From Hong Kong’s finance hub to the rising Singapore hedge fund industry, Daniel shares lessons from running billion-dollar books and advising allocators worldwide—and what aspiring quants should understand about risk, execution, and building something durable in global markets.We also discuss...Why most hedge funds fail because of operational issues, not bad tradesHow to pick the right hedge fund domicile for your investorsWhat to know about hedge fund regulations and compliance when launching a fundCommon hedge fund mistakes made by first-time managersHow to evaluate fund administration, legal structure, and prime broker supportThe real difference between long-only, market-neutral, and global macro investingHow liquidity, FX exposure, and regional risk shape Asia ex-Japan strategiesWhy Middle East family offices are allocating more to alternative investmentsHow quant funds integrate portfolio construction, risk models, and execution systemsBuilding quantitative trading strategies that survive real-world transaction costsThe role of backtesting strategies in validating hedge fund modelsWhat global allocators look for before investing in Asia hedge fundsThe rise of the Dubai finance hub and Singapore hedge fund industryHow Hong Kong’s finance hub is evolving post-COVIDCultural and regulatory differences between running funds in the U.S., Asia, and the Middle EastLessons from Daniel’s transition from astrophysics to finance and global fund management00:00 Intro & special request01:49 How to start a hedge fund02:49 Why hedge funds fail operations and structure04:29 Common hedge fund mistakes new managers make05:49 Hedge fund operations and regulation explained07:19 Asia hedge funds shorting costs and liquidity08:49 Quantitative trading strategies and backtesting systems10:19 Choosing your fund domicile Cayman vs VCC12:19 Hedge fund structure explained for allocators13:49 Launching a fund in Asia ex-Japan markets15:29 Portfolio construction and risk management insights17:19 Building an Asia-focused long short strategy18:49 Emerging markets liquidity Philippines case study20:49 From astrophysics to quant hedge fund career23:19 Running billion-dollar portfolios across global markets24:49 Global macro investing in Asia and MENA26:49 Inside Hong Kong’s post-COVID finance hub28:49 Dubai and Abu Dhabi investment fund growth31:19 Middle East family offices and capital flows33:19 Comparing hedge fund regulation across regions34:49 Dubai and Abu Dhabi as finance centers36:49 Cost of living and taxes for quants38:49 Best cities for hedge fund opportunity40:19 Quant trading lessons on risk and psychology42:49 Closing thoughts building global hedge funds

How do top quantitative trading firms use generative AI?  @versorinvestments , a $1.4B[1] quantitative investment boutique in the asset management industry, reveals how human ingenuity drives its AI-powered investment research and machine learning in finance pipeline. Partners DeWayne Louis and Nishant Gurnani explain how they combine supervised machine learning, natural language processing, and alternative data—from credit card receipts to job postings—to generate investment insights and forecast returns across global equity markets. We discuss why strong quant trading strategies start with clean data, how to avoid data-mining traps, and why top quantitative researchers think like market scientists, not model-builders.We dive into Versor’s flagship hedge fund strategies, from its quant merger arbitrage framework that predicts competing bids to its global equities tactical trading (GETT) strategy capturing dislocations in global equity markets. Nishant and DeWayne unpack what “positive convexity” means in practice, how to design market-neutral quant trading strategies uncorrelated to CTAs, and how Versor’s 30-year research lineage from Investcorp reflects true capital markets innovation. They share lessons on quant research culture, hiring IIT-trained talent, and how disciplined portfolio construction and human-guided AI define the next generation of machine learning in finance and algorithmic trading.We also discuss...How alternative data investing drives alpha in the modern AI quant hedge fund ecosystemBuilding models for event-driven investing strategies and predicting competing bids in merger arbitrage hedge funds – read more here.How Versor’s managed futures strategy achieves diversification and positive convexity investment performanceIdentifying global dislocations through global equity index futures trading and relative value signalsConstructing market-neutral portfolios through advanced market neutral quantitative strategies – read more hereWhy Versor’s success as a research-driven hedge fund comes from blending data science with human intuitionTurning unstructured data in finance — from job postings to credit card data — into tradable insightsDesigning an algorithmic trading platform that scales across multiple asset classes and geographiesApplying machine learning hedge fund strategies to model complex market behaviorsHow disciplined portfolio construction quant strategies optimize risk-adjusted returnsThe evolution of data-driven investing hedge funds and how AI is reshaping portfolio managementThe future of quant talent recruitment in finance and why deep research skills beat brainteasersLessons from 30 years of capital markets innovation and systematic alpha generationFuture of AI in hedge funds — read more here: https://www.linkedin.com/pulse/quant-intel-agentic-ai-quantitative-investing-versorinvestments-cygxf/ Why human-guided AI remains critical in building resilient, high-Sharpe machine learning hedge fund strategies

Doug Garber, former Citadel hedge fund analyst and Millennium Management portfolio manager, joins the show to unpack what it’s really like inside two of the world’s top multi-manager hedge funds — and how each approaches portfolio construction, risk management, and hedge fund culture. Drawing from his years working at Citadel and working at Millennium, Doug explains why Citadel operates more like a finely tuned multi-strategy fund — centralized, structured, and process-driven — while Millennium functions more like a decentralized network of entrepreneurial pods designed for uncorrelated alpha generation. He breaks down how each environment shapes hedge fund analysts and PMs, how competition and transparency fuel performance, and what it takes to thrive in the high-performance world of hedge fund careers.We also dig into the fundamentals of long/short equity investing and hedge fund strategies — from building variant views through deep equity research and mastering the stock picking process, to balancing market neutral strategies with conviction-driven ideas. Doug shares how the best PMs train analysts, manage exposure, and develop consistent alpha generation through disciplined feedback loops and a data-informed financial markets education. We also discuss...The Citadel vs Millennium comparison: centralized discipline vs decentralized autonomy in multi-manager hedge fundsHow sell-side to buy-side transitions build domain expertise for hedge fund analystsWhy deep equity research and sector mastery are the foundation of a strong stock picking processUnderstanding IDEO (idiosyncratic risk) and how top PMs manage exposure in long/short equity portfoliosThe role of risk models, factor exposure, and quantitative overlays in multi-strategy fund frameworksHow hedge fund culture and competition drive performance and shape hedge fund careersThe differences in risk management philosophy between Citadel’s structured systems and Millennium’s entrepreneurial freedomThe analyst–PM relationship: communication, credibility, and building trust inside a hedge fund analyst teamLessons from alpha generation failures — avoiding blow-ups through discipline and post-mortem learningWhat it takes to move from analyst to PM: curiosity, resilience, and ownership of the stock picking processHow working at Citadel trains risk awareness vs how working at Millennium empowers independent thinkingBuilding market neutral strategies that hedge factor risk and emphasize true alpha generationWhy grit and curiosity can matter more than pedigree in landing top hedge fund careersBalancing ambition, burnout, and family — Doug’s reflections on life after multi-manager hedge fundsHis new podcast Pitch the PM: real hedge fund industry insights and financial markets education for the next generation

Why do smart investors lose money? Alix Pasquet, Managing Partner of Prime Macaya Capital Management, breaks down the paradox at the heart of hedge fund investing psychology—why high IQ often hurts investors more than it helps. Drawing on decades of experience allocating to top quant funds and running capital, Alix explains how hedge fund managers fall into classic strategy mistakes, why competing against other smart people is a losing game, and how temperament, meta-rationality, and emotional intelligence determine long-term returns. He shares lessons from poker, backgammon, and behavioral finance investing, showing how overconfidence, overfitting, and complexity bias cause even the most analytical investors to underperform—and what it really takes to develop a resilient hedge fund manager mindset that consistently outperforms.We also dive into how AI in finance 2025 is changing the rules of the game. Alix argues that the rise of LLMs and financial markets automation is amplifying investor laziness and creating “fantasy stocks,” where hype replaces deep work. He reveals how algorithmic trading and AI are reshaping competition, why quant fund blowups from 2007 still hold lessons today, and how complexity and systems thinking in markets help investors avoid repeating those same errors. From overreliance on automation to cognitive bias in quant funds and artificial intelligence, Alix explains how to adapt your process—combining analog judgment, data discipline, and humility—to truly understand how hedge funds make money and how smart people keep losing it.- Why smart investors lose money and how behavioral finance explains repeated hedge-fund blow-ups- Cognitive biases in investing and how even seasoned managers misread probability and risk- Investor temperament and success: why emotional discipline matters more than IQ or pedigree- Risk management lessons from hedge funds drawn from two decades of allocation experience- Quant finance insights from studying how data access, cleaning, and market impact shape alpha- Quantitative trading psychology and what separates disciplined quants from over-fit models- Why quants lose money: the hidden behavioral alpha that algorithms can’t replicate- Market microstructure investing and how execution, liquidity, and leverage drive performance- Complexity and systems thinking in markets—how to simplify chaotic systems into tradable edges- Behavioral alpha in quant strategies: exploiting human errors embedded in data- Intelligence vs wisdom investing: when deep knowledge clouds judgment and kills returns- IQ traps in decision making that cause overconfidence and portfolio blow-ups- Intellectual arrogance in hedge funds and how meta-rationality builds long-term humility- Generative AI in markets and how narrative feedback loops distort valuations- AI amplifying investor mistakes: when automation removes human judgment- Machine learning investing: where predictive models add value—and where they fail- Data-driven investing strategies and the limits of backtesting without context- Automation in portfolio management and the danger of delegating conviction to code- Network theory in investing: building multiple networks to uncover leading indicators- Analog training vs digital distraction: why reading, reflection, and deep work still create edge- Emotional self-regulation for investors—habits, routines, and recovery to sustain performance- Lessons from poker and backgammon for investing: strategy, variance, and position sizing- Mentorship and triads networking strategy—how to create compounding social capital- How to build diverse networks for success across geography, sector, and generation- Stoicism and finance mindset: developing calm under uncertainty and volatility

How do you find trading edge in electricity markets? Cory Paddock, co-founder of GBE, explains how real alpha generation in power trading comes from anticipating paradigm shifts before the market sees them. In a renewable energy trading market shaped by constant regime change—coal replaced by gas, wind and solar reshaping grid topology, and data centers driving new load volatility—edge belongs to those who read the grid, not the price charts. His approach blends energy infrastructure insight with algorithmic trading discipline: track locational marginal prices, study market data pipelines, and build conviction around where power will actually flow. In fast-moving electricity markets, where historical data decays quickly, the strategy is simple—trade clean, understand risk management deeply, and position early for the next market shift.Cory’s incredibly bullish on Gen Z in quant finance. He’s betting on Gen Z quants. They’re Python- and LLM-native, fluent in building tools and models that turn raw market data into live trading infrastructure. Their exposure to open-source research and self-directed learning creates a new kind of trader—one who codes faster, questions conventions, and finds alpha in overlooked niches of energy and power trading. At GBE, he builds an environment where Gen Z trading talent can experiment, own ideas, and learn risk management through real positions, not simulations. The result is a new generation of algorithmic traders redefining what edge means in modern markets.- Building a trading strategy for electricity markets and finding edge through data-driven alpha generation- Anticipating paradigm shifts in markets and adapting trading models to regime change in power trading- How renewable energy trading and grid congestion reshape price discovery and risk management- Designing a market data pipeline for real-time energy infrastructure analysis and trading execution- Why electricity markets differ from traditional quant finance and what makes power trading unique- Using algorithmic trading frameworks to process market data and identify short-term dislocations- Risk management frameworks for volatile energy markets and five-minute tick data decision-making- Recruiting Gen Z trading talent fluent in Python, machine learning, and market data engineering- How Gen Z quants approach trading edge differently—experimentation, automation, and fast iteration- Structuring incentives for traders to align P&L ownership, discipline, and long-term performance- The psychology of running a trading firm with personal capital and managing downside risk- Why historical backtests fail in energy markets due to infrastructure evolution and topology change- Market structure and locational marginal pricing (LMP) as the foundation of energy trading strategy- How physical constraints in grids create alpha opportunities for quantitative trading teams

Can you start a hedge fund as a college student? Christina Qi, co-founder of Domeyard, did—and later built Databento, a modern market data API used by top algorithmic trading and quantitative trading teams. We get into how high-frequency trading (HFT) actually works, why clean order book/tick market data matters for robust trading strategies, and how a product-led model beats “talk-to-sales.” Christina shares what it takes to compete with Bloomberg/Refinitiv, where AI in finance is headed, and how better data unlocks faster research, reliable execution, and scalable quantitative trading workflows.Christina also breaks down hedge fund fundraising as a first-time manager—what allocators look for, how to structure fees/lockups/redemptions, and why your track record is everything. We talk about 2025 algorithmic trading: easier tools, tougher alpha, and how to find edge with high-quality market data, disciplined backtesting, and strong risk management. She closes with career advice for aspiring quants: master market structure, build real trading strategies in Python, and apply machine learning trading where it truly adds value—not as hype, but as part of a rigorous AI in finance toolkit.We also discuss...Founding Domeyard in college and turning a summer strategy into an HFT hedge fundUsing high-frequency trading to attract day-one allocators in hedge fund fundraisingWhy a verifiable track record matters more than terms when raising capitalHow to set fees, lockups, and redemptions as a first-time managerWhen investor relations and performance diverge and how to keep LPs during drawdownsWhy Domeyard shut down and the scalability limits of HFTBuilding Databento as an API-first market data/market data API platform for algorithmic and quantitative tradingSolving data licensing and usage rights with clean tick data, order book data, and better market microstructure coverageCompeting with Bloomberg and Refinitiv by focusing upstream on raw market data (not dashboards)Winning with product-led growth and self-serve checkout instead of talk-to-salesA bottom-up purchase at a major AI company as proof that PLG works for market data APIsAdoption by options market makers, quant funds, and AI in finance teams for research, alternative data, and NLP for markets use casesCheaper backtesting and better trading infrastructure but tougher alpha generation in 2025A public roadmap and user upvotes to prioritize datasets that matter to quants and quantitative trading workflowsAdvance commitments that de-risk new exchange integrations and ensure day-one usageIncumbents copying features as validation that Databento leads in market data APIsThe AI-in-finance arms race and why data quality decides machine learning trading, risk management, and Sharpe ratio outcomesHow macro conditions change fundraising outcomes for startups and hedge fundsCareer advice for aspiring quants: learn market structure/market microstructure, data engineering, rigorous backtesting, portfolio construction, and build real trading strategies

Should you invest in the S&P 500, or look for smarter ways to beat the market? Jason Hsu, Co-Founder of Research Affiliates ($159B AUM) and now CIO of Rayliant, explains why simply buying the index or asking “should I invest in ETFs” isn’t enough. In this episode, he breaks down smart beta vs S&P 500, systematic investing, and how factor investing strategies and fundamental indexing can deliver some of the best long-term investment strategies for investors who want to know how to beat the market beyond traditional index funds.Asian markets are less efficient than the US, Jason says. With higher retail speculation, governance risks, and volatility, opportunities open up for quant investing through Asian ETFs, China stock market investing, and emerging markets investing strategies that capture inefficiencies. As CIO of Rayliant, Hsu shows how his team builds factor-based portfolios across China, Japan, Korea, Taiwan, and other emerging markets to turn inefficiency into alpha.We also cover:- How Jason Hsu cofounded Research Affiliates, scaling systematic strategies to manage $159B AUM- Launching the PIMCO All Asset Fund in 2002 and bringing multi-asset investing strategies to retail investors- The origin of smart beta ETFs and why fundamental indexing offers a better alternative to cap-weighted indexes- How the tech bubble exposed flaws in traditional indexing and set the stage for factor investing strategies- Why governance factors and valuation discipline are especially important in emerging markets- Building Rayliant’s smart beta 2.0 products using multi-factor models and machine learning in investing- How factors in investing reveal the “nutrients” of a portfolio for long-term compounding- The difference between risk premia and behavioral biases as drivers of factor returns- Examples of behavioral investing mistakes in Asia and how professionals can capture alpha from retail flows- Why low-frequency quant strategies align better with pension funds and sovereign wealth funds than high-frequency trading (HFT)- The future of quant investing explained: machine learning, non-linear models, and portfolio construction- Jason’s career advice for young professionals navigating the hedge fund and asset management career path00:00 Intro01:42 Founding Research Affiliates and early startup days03:02 Launching the PIMCO All Asset Fund in 200204:26 Smart beta ETFs explained and how they started09:19 Spinning off Rayliant and focus on Asia10:26 Why Asian markets are less efficient than the US11:43 Opportunities from inefficiency and alpha in China13:38 Gambling analogy and retail speculation in Asia16:53 Liquidity challenges in smaller emerging markets20:41 Rayliant’s product offerings and smart beta 2.020:57 What factors reveal about markets and portfolios23:34 Risk premia vs behavioral biases in factors25:39 Governance, valuation, and smart money factors in Asia28:27 Using machine learning in Rayliant’s strategies34:05 Can discretionary managers still have edge today38:39 Poker, luck, and systematic investing advantages41:00 Future of discretionary managers and pod firms42:44 Are high-frequency trading firms sustainable long term46:22 Rayliant’s mission and value to society50:00 Career advice for young finance professionals53:14 Closing thoughts

Can you trade the stock market with AI? Yes: Renee Yao launched Neo Ivy Capital, a billion-dollar** AI hedge fund that uses AI in trading and investing to generate alpha. In this episode of Odds on Open, she explains how she built a quant hedge fund from scratch, scaling to over $1B AUM** with advanced AI hedge fund strategies that adapt to markets in real time and show how to trade stocks with artificial intelligence at scale.Unlike traditional firms that rely on armies of quant researchers and static machine-learning models, Renee (who used to work as a QR Analyst at Citadel and Portfolio Manager at Millennium) reveals how machine learning in trading has evolved into true self-learning AI. She breaks down why most funds still depend on crowded factor bets, and how her fund’s approach delivers uncorrelated returns — a real edge in the hedge fund career path and a blueprint for the future of systematic investing.**Note: According to a recent Form ADV filing, Neo Ivy Capital oversees about $1.02 billion in assets under management. This figure represents total regulatory AUM, which is broader than the ~$313M reported in 13F-disclosed securities and may include additional holdings or leverage.We also discuss...- Citadel hedge fund strategy and risk management lessons after 2008- Why diversification and breadth of edge matter in a quant hedge fund explained- How Neo Ivy uses AI in trading and investing to generate uncorrelated returns- Why machine learning in trading has evolved into true self-learning AI- The three barriers to entry for AI hedge funds: modern AI, infrastructure, portfolio design- Why large funds rely on crowded factor bets while Neo Ivy delivers pure alpha- How fund size impacts scalability and alpha opportunities- What it’s like moving from Citadel and Millennium to founding a fund- How to start a hedge fund and build infrastructure from scratch- How self-evolving AI models adapted during COVID market shocks- The role of modern tools like LSTMs and transformers in AI hedge fund strategies- Career and life lessons from the hedge fund career path and staying disciplined00:00 Intro01:14 Renee Yao’s journey to founding Neo Ivy02:28 Joining Citadel after the financial crisis04:13 Hedge fund diversification and breadth of edge04:45 Why Neo Ivy trades with AI strategies07:50 How self-learning AI adapts to markets09:40 Causation vs correlation in AI hedge funds10:33 Barriers to entry for AI hedge funds14:47 Risks of crowded factor bets explained16:39 Why big funds struggle with AI talent17:29 From PM at Citadel to hedge fund founder18:47 Challenges of launching a quant hedge fund20:25 Biggest constraint for AI hedge fund startups22:08 How AI hedge funds adapted during COVID24:04 Modern AI tools used in quant trading25:13 Building hedge fund infrastructure from scratch26:26 Career advice for aspiring quants and traders28:55 Adapting career goals to changing job markets31:57 Life lessons from trading and risk management32:51 Staying disciplined while running a hedge fund34:38 Obsession and belief in AI hedge funds35:41 Closing thoughts on hedge funds and life

In this episode of Odds on Open, Ethan Kho sits down with Joe Mezrich, Founder of Metafoura LLC and former Managing Director at Nomura Quant Strategies, to reflect on nearly 40 years in quant finance. Joe’s career spans the early days at Salomon Brothers—where he helped pioneer factor models, risk modeling, and even early machine learning in finance—through senior sell-side research roles at Morgan Stanley, UBS, and Nomura.Joe shares how the sell side effectively built modern factor investing, why models like the Barra risk model failed in crises such as the Tech Bubble (2000) and the Quant Crisis (2007–08), and how market-neutral strategies and algorithmic trading continue to shape today’s buy-side. He also explains why interpretability, from CART decision trees to today’s LLMs for trading, is critical for robust risk management.We cover:- Origins of quant finance on the sell side at Salomon Brothers.- Early factor models, the Barra risk model, and portfolio risk modeling.- Use of robust statistics and CART decision trees in machine learning for finance.- Why risk models failed in the Tech Bubble (2000) and Quant Crisis (2007–08).- Growth of market-neutral strategies and interaction between sell-side research and the buy side.- Crisis lessons: liquidity concentration, model speed, and explainability.- Evolution of factor investing into overlays and ETFs.- How quant researchers balance complexity vs. interpretability with LLMs for trading.- Role of alternative data, point-in-time datasets, and data visualization in alpha.- Wall Street culture: Liar’s Poker-era Salomon, Morgan Stanley, UBS, Nomura.- Impact of interest rates, earnings vs. sales growth, and macro regimes on factors.- Sustainability of multi-manager pod shops (Citadel, Millennium) and implications for quants.- Career lessons: curiosity, humility, and finding beauty in quant models.Whether you’re a quant researcher, an aspiring algorithmic trading professional, or an allocator seeking to understand systematic funds, give this a listen.00:00 Intro and Episode Overview00:46 Origins of Quant Finance at Salomon Brothers02:56 Early Factor Models and Barra Risk Model05:51 Robust Statistics and CART Decision Trees08:58 Machine Learning in Finance 1990s Experiments12:06 Why Risk Models Failed in Tech Bubble15:31 Lessons from the 2007 Quant Crisis18:51 Rise of Market Neutral and Sell-Side Research22:26 Evolution of Factor Investing to ETFs26:01 Balancing Complexity and Explainability for Quants29:16 Alternative Data and Point-in-Time Datasets32:46 Wall Street Culture Salomon Morgan UBS Nomura38:08 Interest Rates Macro Regimes and Factor Drivers41:51 Are Multi-Manager Pod Shops Sustainable?46:04 What Makes Exceptional Quant Researchers Last49:26 Curiosity Humility and Risk Management52:56 Finding Beauty in Quant Models and Data56:16 Final Lessons from 40 Years in Quant Finance

Former Cargill Global Trading & Risk Management Director Kristine Engman Hochbaum explains how commodities trading strategies and quant trading strategies really work. On Odds on Open, she breaks down the sources of trading edge and alpha generation in today’s commodities markets, from agriculture trading to energy and metals.We cover why physical vs. financial commodities trade differently, how systematic trading and traditional players (hedge funds vs. ABCDs) approach markets, and the lessons of the 2022 commodities boom. With Ethan, Kristine unpacks real-world risk management strategies around delta, liquidity, forecast accuracy, and headline shocks — from Russia–Ukraine war trading impact to weather, inflation, and supply chain disruptions.Key topics in this episode:- How relationships and brokers create trading edge in commodities- Why capital allocation matters for building positions- Headline risk and the impact of social media on commodity prices- Managing liquidity risk and delta in physical books- Weather risk and forecast accuracy in agriculture and energy trading- The role of supply chain disruptions in commodities markets- Differences in hedge fund vs. ABCD trading strategies- Lessons from the Russia–Ukraine war trading impact and inflation and commodities- Stories from real trades: negative power prices and long oil during crisis- Career lessons: what makes a great trader and how to keep learning commodities marketsAlong the way, she shares what separates good traders from great ones, why inflation and commodities are inseparable, and how to keep learning in fast-changing markets. If you’ve ever wondered what makes a great trader or how to start learning commodities markets, this episode is for you.00:00 Intro01:15 Edge in commodities trading? Relationships, capital, information04:40 Commodities market efficiency, information flows, and AI08:32 Hedge funds vs. ABCDs, commodity trading strategies13:34 When commodities outperform equities, the 2022 boom17:45 Headline risk, social media impact on markets19:08 Risk management strategies in physical commodities trading26:14 Probability, forecasting, and scenarios for trading decisions31:00 What makes a great commodities trader today37:53 Contrarian trading strategies, alpha generation in commodities42:24 Russia–Ukraine war impact on commodity markets, trading45:35 Life and career lessons from commodities trading51:30 Careers, uncertainty, and learning in commodities markets

This week, Ethan Kho sits down with Dr. Ernest P. Chan — former quant at Millennium and Morgan Stanley, and now founder of PredictNow.ai and QTS Capital Management. Ernie is one of the best-known voices in quant finance, author of Quantitative Trading, and a pioneer in systematic trading strategies.We cover:- When machine learning trading models work in markets — and when they fail- Why financial markets suffer from data sparsity, and how regime shifts and black swan events in finance break models- How quants use AI in trading for risk management and portfolio optimization- The promise of LLMs for financial markets and how generative AI can overcome data scarcity- Semi-supervised learning explained, with real examples from analyst reports and Fed speeches- Where quants can still find alpha generation when new technologies become widely available- How PredictNow helps banks and hedge funds apply AI risk management at scale- Lessons from launching QTS Capital and running independent quant trading strategies such as crisis alpha- The role of alternative data in hedge funds and what actually drives performance post-2008- What it was like working alongside quants at Millennium, Morgan Stanley, Credit Suisse — and how Renaissance Technologies influenced Ernie’s career- The traits that make a great quant, and why creativity still matters in quantitative trading strategies= Advice for students and professionals entering quant finance in the age of financial big data and generative AI- How to spot overfitting in backtests and apply the scientific method in systematic trading strategies- Why risk awareness separates long-term success from blow-ups in post-2008 quant strategies

In this episode of Odds on Open, Ethan Kho sits down with Vinesh Jha, founder of Extract Alpha and former director of PDT Partners, to unpack lessons from the 2007 Quant Quake and how systematic investors can adapt in today’s crowded landscape.We cover:- What really happened inside PDT Partners when the firm lost $500M during the Quant Quake- Why so many quant hedge funds blew up in 2007 — and the key financial crisis lessons still relevant today- Inside the culture at PDT Partners vs the siloed world of other hedge funds- Why Vinesh Jha left the buy side to start Extract Alpha — and how alternative data reshaped quant finance- The rise of earnings transcript models, analyst accuracy signals, and Estimize’s crowdsourced forecasts- Will today’s LLMs and NLP models in finance get commoditized like old factor strategies?- The trade-offs between running a hedge fund and building a data company- How smaller systematic funds can still compete with giants like Citadel, Millennium, and DE Shaw- What it’s really like to work as a quant — and the traits that make a good quantBonus: - How quant hedge funds find alpha using alternative data and NLP- How hedge funds use earnings expectations and post-earnings drift to trade- What lessons can quants learn from market crashes and black swan events?00:00 Intro01:00 Inside PDT Partners during the Quant Quake05:11 How quants decide when models fail08:49 Culture at PDT vs other hedge funds10:38 Why Vinesh founded Extract Alpha15:25 Financial crisis lessons: crowded quant trades16:20 Will LLMs and NLP in finance get crowded?18:53 Best alternative data sets for alpha24:54 Do Estimize crowdsourced forecasts make money?28:19 Can buzzwords like AI predict returns?32:02 Why Vinesh didn’t start a hedge fund35:37 How quants should reinvent mid-career38:51 AI disruption vs creativity in quant finance40:48 Can small funds compete with Citadel, Millennium, DE Shaw?43:30 What makes a good quant stand out46:54 Closing thoughts on longevity in quant financeWhether you’re deep into quant finance, researching hedge fund strategies, or simply curious about what makes a good quant, this conversation offers rare insight into how edge is found—and lost—in modern markets.

What’s it really like working as a quant in fundamental research at Two Sigma—and how will AI, LLMs, and agentic workflows change quantitative trading strategies? Bill Mann, former Two Sigma fundamental researcher and founder of Harmonic Insights, joins Ethan Kho to break down how hedge funds build edge from widely available data and why “hacker” creativity still matters in systematic investing.Bill shares insights from AQR and Two Sigma, including how proprietary data pipelines become alpha generation engines, how to avoid crowding in popular factors, and what makes a great hedge fund strategy and the best work environment for quants. He also explains how LLMs, algorithmic trading, and automated research pipelines will transform research, engineering, and trading—and why mastering data engineering for quant finance is critical for junior quants.We answer questions like:– How do hedge funds find edge using fundamental vs. quantitative analysis?– What is point-in-time data and how does it prevent look-ahead bias?– How do proprietary data pipelines create alpha generation?– How can quants avoid crowding in value factors?– What’s it like working as a quant? What’s the culture like inside Two Sigma’s quantitative trading strategies team?– How do LLMs and AI agents change systematic investing workflows?– Which quant research tasks will be automated first?– What skills will junior quants need in the AI era?– How should aspiring quants practice creativity and problem-solving?– How does algorithmic trading intersect with data-driven investing?– What role will high-frequency trading (HFT) play in the future?– How do fintech startups work with hedge funds?– What’s the *New Barbarians* podcast about?#quantfinance #twosigma #aiinfinance #largelanguagemodels00:00 Intro00:57 Life as a Quant at Two Sigma02:36 Finding Edge in Fundamental Data07:04 Creating a Creative Quant Research Culture11:19 How LLMs Change Quantitative Trading15:52 AI’s Impact on Junior Quant Careers22:56 Using AI Tools for Learning23:57 Harmonic Insights: Advising Fintech Startups30:47 The New Barbarians Podcast Explained33:26 Crypto and Market Makers vs TradFi34:54 Career Advice for Aspiring Quants38:46 Final Takeaways

Can you analyze social media for investment decisions? How do hedge funds use Reddit posts, earnings calls, and SEC filings to find alpha? What’s the role of LLMs for financial analysis in 2025? Chris Kantos, Head of Quantitative Research at Alexandria Technology, joins us to explain how the buy side uses natural language processing (NLP), AI for investing, and text-based sentiment data to generate AI alpha signals across all asset classes—from equities to commodities.We dive deep into how Alexandria builds quantitative trading strategies from unstructured data like Reddit posts, news articles, earnings calls, and 10-Ks. Chris explains how most hedge funds get NLP wrong, why Alexandria’s document-specific classifiers give them an edge, and what makes a good social media for stock analysis dataset in a crowded and noisy world. He also tackles the myth of data commoditization and explains why alpha decay isn’t always inevitable.We answer questions like:– How do hedge funds use Reddit and social sentiment in trading?– What makes a good NLP model for financial data?– Has alternative data become commoditized?– What separates FinBERT from other finance-specific LLMs?– What’s the best way to train a sentiment model for earnings calls?– How is ChatGPT used in finance and investing workflows today?– How do professional quants cut through the noise on Twitter, Reddit, and X?– What’s the future of AI in systematic investing?– How does news sentiment impact trading strategies?– How do hedge funds use alternative data beyond equity alpha?– How do professionals use Reddit data for stock analysis without getting fooled by noise?– What’s the best path to become a quant researcher today?– What skills and experience matter in quant finance careers?Chris also shares quant finance career advice, why he left risk modeling for alt data, and what really happened in the office when Bernie Madoff got caught.

Financial markets are a game, says Grant Stenger. So how do you win in the financial markets? Grant, a former Jane Street intern and current crypto founder, believes markets are the most competitive game on earth—and he's been training to beat them since high school.From card counting in middle school to landing a hedge fund internship at QuantRes before university, Grant shares how a lifelong obsession with games, poker, and mathematical edge led him to build Kinetic—a decentralized crypto exchange and DEX aggregator on Solana trading millions of tokens.We talk about his quant trading internship experiences at QuantRes, Numerai (a crypto hedge fund), and Jane Street, and why he left it all to start his own crypto founder story building next-gen trading infrastructure.He breaks down:- His Jane Street internship experience and why they make you play Figgy- Why gambling and trading the market are similar- Poker and trading—and how poker is better prep than chess for decision making under uncertainty- Numerai’s bet-staking model and encrypted data thesis- How decentralized crypto exchanges actually make money- Trading on Solana vs Ethereum and why Solana is built for high-frequency crypto trading- Building a platform that can support 10 million tokens- Why being early in a new asset class gives you an edge—and how that edge resembles the one in gambling vs trading- And how he figured out how to get a hedge fund internship in high schoolWe discuss the collapse of FTX, the fall of Sam Bankman-Fried, the risks of centralized cryptocurrency exchanges, and why trading isn’t just gambling—but a game of edge in both gambling and trading.

How do you become a solo quant trader and build your own systematic trading business? Robert Carver, ex-head of fixed income at Man AHL—a $63 billion systematic trading hedge fund—shares how he went from managing institutional capital to becoming an independent, full-time quant trader.He reveals the key skills, mindset, and tools needed to succeed in quant trading without working at a big firm, how to create your own quant trading strategies, and why a PhD isn't required to break into systematic trading. Also, he shares how to manage risk and how he runs 200+ futures trading strategies as a solo trader with a small account.He breaks down:- How to become a quant trader without working at a hedge fund- The skills and background needed to succeed in quant trading without a math degree- Whether STEM is required for quant jobs- Why the Sharpe ratio is flawed and what to use instead- What separates top performers: traits of successful quant traders- How to build a quant trading career path solo vs going the institutional route- What investing strategies to use- The best quant trading strategies for individual traders- How to properly backtest trading strategies without overfitting- How to deal with alpha decay and determine when a strategy stops working- Inside the research culture of a top hedge fund strategies team- How to get into hedge funds in today’s competitive environment- What it’s like to work at a quant fund versus a more traditional hedge fundPlus: why most quant trading strategies rely on public, simple rules—and how to apply them profitably with a skeptical mindset.Robert is also the author of several acclaimed trading guides, including Systematic Trading, Advanced Futures Trading Strategies, Smart Portfolios, and Leveraged Trading. They’re what got Ethan (our host) into quant finance to begin with.00:00 Intro00:45 How to Handle Losing $1 Billion04:05 What It’s Like Working at Man AHL06:39 Why Quant Funds Hire STEM Grads08:43 High Frequency vs. Low Frequency Quants10:44 The Most Important Trait in Quant Research12:53 Is Sharpe Ratio a Good Metric?16:41 Geometric Returns vs. Sharpe Ratio17:45 How to Avoid Overfitting in Backtests21:28 Dangers of Implicit Fitting in Models23:02 How Robert Deals With Alpha Decay25:29 Robert’s Current Quant Trading Strategies28:45 Should You Trade Independently or Join a Fund?31:19 Why Trading Your Own Capital Is Hard32:09 Does He Look for Market Inefficiencies?33:45 His Biggest Trading Innovation: Execution35:45 How Hedge Funds Approve New Strategies39:27 Will Big Quant Firms Dominate Forever?42:44 Pod Shops vs. Collaborative Quant Culture43:48 Final Thoughts + His Books on Quant FinanceFind links to Robert's books here:1. http://www.systematicmoney.org/systematic-trading/2. http://www.systematicmoney.org/smart/3. https://www.systematicmoney.org/leveraged-trading4. https://www.systematicmoney.org/advanced-futures

What is edge in trading—and how do hedge funds and quant traders find edge in 2025? According to Agustin Lebron, former quant trader at Jane Street and author of The Laws of Trading, edge is what separates average traders from those who thrive at a top hedge fund. But today, finding edge requires more than just a good model—it demands judgment, adaptability, and a deep understanding of how quant trading firms actually operate.In this episode, Agustin breaks down exactly how quant trading works, how elite firms like Jane Street maintain their edge, and how quant funds make money in both calm and chaotic markets. He shares hard-earned insights on how to become a quant trader, the realities of trading internships at hedge funds, and what it really takes to get hired at Jane Street.He breaks down:– What is edge in trading and how to know if you have it– How elite quant firms like Jane Street develop and defend edge– Why edge is statistical—but also deeply judgment-based– How to get hired at Jane Street and succeed in a Jane Street intern experience– What makes a good trader (hint: it’s not just math)– Why some trading models fail during market crises– How small quant shops compete with large firms– How to stand out in a hedge fund or quant internship– Should I become a quant? Questions every student should ask– Why quant trading firms 2025 will look different—and whether AI will replace traders in some rolesAgustin also shares how young people can find their personal edge in a world transformed by AI, automation, and rising inequality. His advice? Don’t chase status. Follow curiosity. Learn where real value comes from.

If venture capital underperforms the public markets, is it still worth the investment? For Avik Ashar, the answer is yes—but not for the reasons you think. Avik, a Principal at Riverwalk Holdings, an India-focused VC firm, argues that most people misunderstand how venture capital works and how to fairly evaluate VC fund performance.Venture capital, he explains, isn’t just about chasing unicorns or short-term IRRs. It’s a strategic investment tool, especially in Asia, used by family offices and conglomerates not only for returns but also for M&A, R&D, and market expansion. In markets like India, venture capital is helping industrial groups future-proof their businesses while tapping into innovation. He also highlights how India’s maturing public markets and mutual fund sector are making early-stage investing and startup exits far more viable than in places like Southeast Asia, where liquidity is still limited.He breaks down:– Why VC returns vs public markets often look misleading—unless you know how to analyze them– How family offices in India are using venture funding for strategic acquisitions– Why M&A is finally taking off in Asia—and what that means for founders– The key differences between venture capital in India, Southeast Asia, and the U.S.– Why India’s public markets are becoming a critical exit path– How startup exits work in markets without strong IPO pipelines– Why Southeast Asia’s VC boom from 2014–2018 underperformed– What Gen Z needs to understand about building in a noisy, AI-native world– How venture capital vs private equity differs in terms of outcomes, strategy, and timelinesHe also shares an important reminder for our age of endless short-form content: “The most expensive thing you can give today isn’t your time—it’s your attention.”00:00 Intro00:33 Why invest in venture capital?01:01 How venture returns work03:01 Venture as an R&D and acquisition pipeline04:11 The outlier nature of VC returns05:11 Why family offices invest in venture05:52 Examples of conglomerate acquisitions in India09:43 Differences in VC ecosystems: US, Singapore, and India16:29 Do family-backed VCs perform better in India?16:50 Riverwalk Holdings’ experience in India18:22 Maturity of India’s financial ecosystem for startups23:59 Where will venture gains come from?27:51 Indian conglomerates embracing startups28:52 Challenges of building companies in Asia30:22 Advice for young people in an uncertain world32:44 Tech’s share of the US market cap38:04 Staying focused amidst noise40:57 Advice for recent graduates42:24 Outro

Is venture capital dead? Not for Guy Horowitz, who boasts 20+ years in the VC ecosystem, holding the title of partner at firms like DTCP, 33N, and ESH.vc. In this episode of Build to Last, we unpack the changing face of early-stage investing and startup funding—from the 2000s hardware boom to the rise of software unicorns, and now the new frontier: AI-first companies. Guy shares battle-tested insights on identifying founder-market fit, navigating VC cycles, and why understanding capital formation is just as important as finding the next big tech innovation.We also dive into the future of work, education, and the middle class. What happens when generative AI and automation replace white-collar jobs faster than traditional schools can adapt? What happens when AI agents arrive at desks, offices, and boardrooms? With three kids of his own, Guy reflects on raising future-proof talent and what young people today really need to succeed in a world defined by machine learning, venture-backed disruption, and rapid technological change. Spoiler: it's not just about coding bootcamps—it's about curiosity, adaptability, and a willingness to learn from people who aren’t like you.Some key takeaways:– How venture capital has changed in the past two decades– Why "great ideas" don’t matter without the right founders– What makes a great startup exit– The question top VCs like Guy Horowitz ask before writing a check– What NOT to say in a pitch meeting– How DTCP became a breakout fund backed by Deutsche Telekom– Why today’s job market is rigged (and how you can stand out anyway)– Whether AI will make human investors obsolete– Why most white-collar jobs are more automatable than we think– Is now a good time to start a fund? Guy’s honest take– Advice for young people unsure about their futureAlso in this episode, we discuss how to identify REAL startup talent (even if they’re mean) and what makes a great VC (beyond capital). Subscribe for more conversations with founders, builders, and leaders.#venturecapital #investing #ai 00:00 Podcast Intro 00:43 How venture capital has changed 01:55 Guy’s early career at Gemini 05:46 Lessons from being cocky too early 09:15 What makes a great VC investor 12:49 How Guy evaluates founders 18:54 What not to say in a pitch 21:38 The story behind DTCP 27:08 Fundraising success and growth 30:11 Is venture capital dead? 36:41 Raising kids in the AI age 43:44 What happens to the middle class?51:12 Advice to young people 52:42 Outro

How will AI affect education? Minh Tran has a lot to say about the future of learning in the age of large language models (LLMs) and generative AI. As the COO of GoodNotes—one of the world’s leading AI-powered note-taking apps—he’s at the forefront of how AI is changing the education system. For Minh, the future of learning is already here: “We need to rethink WHAT we teach, not just HOW we teach,” he says.Before working in edtech, Minh was the Executive Director at Education First, the global learning company. He also founded Bloom Academy in Hong Kong, literally building a K–12 school FROM SCRATCH during COVID. With experience in both traditional and startup education organizations, Minh shares why AI-first schools are better positioned to thrive, while schools that don’t adapt to AI will fall behind.Some key takeaways:– How Minh started a school from scratch during the pandemic– What today’s students *actually* need to learn in an AI-first world– Why most schools are failing to adapt to ChatGPT and generative AI tools– How Goodnotes became a tech unicorn through remote-first culture– How Goodnotes is winning the tech talent war through flexible work arrangements– The importance of mentorship– How to find a mentor– Why AI experimentation, curiosity, and play are key to raising future-ready kids– How to pivot from education to tech (and how others can break into tech from different industries)– What Gen Z can offer senior leaders at the workplaceWe also dive into Minh’s advice for young people chasing unconventional careers and the secret to building a career with impact. He emphasizes this: put in the hours, and if you’re privileged enough to follow your passion, just do it.Subscribe for more conversations with founders, builders, and leaders.

Is a finance degree still worth it if you want to become a quant? In this week’s Build to Last episode, quant researcher and YouTuber Dmitri Bianco explains why he calls his own finance undergrad “a big mistake” — and why most quant roles today demand far more math, statistics, and programming than students realize. (The short answer to that first question is: no.)Dmitri shares how he went from a misguided finance major to Head of Quantitative Research at Agora Data, a fintech company — after being rejected from top financial engineering programs and racking up $160K in student debt. He now runs a YouTube channel with nearly 60,000 subscribers, where he shares real insights on quant careers, financial engineering, and navigating the industry.breaks down how the world of finance is evolving, and how new grads can break in. A few key takeaways:— Why most finance degrees don’t prepare you for quant roles— How buy-side firms use prestige to exploit junior talent— Why the traditional finance hiring model is broken— How passion and problem-solving beat credentials in quant interviews— Practical advice for students entering quant and finance roles today— What makes a company a great place to work in quant finance— Why career satisfaction matters more than titles or payHe also shares thoughts on AI’s impact on quant careers, what it’s like being a YouTuber with a dedicated hate page (yes, really), and how to build a career you can be proud of (even in a tight job market).#QuantFinance #FinancialEngineering #financejobs Check out Dimitri's channel: https://www.youtube.com/c/dimitribianco

A second passport isn’t just for shady billionaires or globalist elites — it’s actually a legal, fast-growing strategy used by thousands of wealthy families to unlock global freedom. It’s also the core of a booming global industry that Krista Victorio, Partner at Orience, knows inside and out.On this episode of Build to Last, Krista explains how citizenship-by-investment works — and why families from China, Russia, the Philippines, and (most surprisingly!) the United States are buying their way into second passports more than ever before. She also shares how Orience, an investment migration firm, is capitalizing on the rising demand for global mobility. For Krista, investment migration is one of the few ways you can “diversify the accident of birth.” In the episode, she breaks down:- How different programs work — from Caribbean citizenship schemes and one-time-payment Maltese passports to Golden Visas in Europe and the U.S. EB-5 visa- Why investment migration is exploding post-COVID- What wealthy people *really* want from a second passport (Hint: it’s not just taxes)- The top programs in Portugal, Spain, Greece, Malta, and more- Why the traditional career ladder is dead- Why more young people should rethink in an increasingly challenging job market

At no other point in history the #corporate and #academic worlds been so deeply at odds. The former sees the latter as useless bureaucrat over-thinkers, while the latter sees the former as money-driven opportunists placing profit over principle. But Dr. Davide Sola is rallying against this. His experience spans both the #boardroom and the #classroom. Early into his #career, he worked at #McKinsey while simultaneously enrolled as a #Enterprise #Economics PhD student at the University of Torino. "There’s this idea that academic means rigorous but irrelevant. I don’t believe that. You can be both," he says. The mission to bring #academia and #corporate closer together is still something Davide continues today, now almost two decades since his time at McKinsey and Torino. He's currently the #CEO of Strategy in Action ("a strategy solution built to give C-Suite [executives] what they need") and a #business professor at ESCP Europe.Speaking with Ethan on the Build to Last podcast, he discusses the future of #consulting, how to #scale, and the #strategy behind a generations-lasting business.

The startup world is dog eat dog. “Move fast and break things,” so goes the Silicon Valley adage. For Illai Gescheit, this does not have to be the case: “You can still move fast and break things and be a kind person as well,” he says. The unconventional mantra is plastered on his LinkedIn headline. “Kindness is my strategy,” it reads. Indeed, it’s a practice that Illai must commit to in his day-to-day work as a startup advisor. He’s one of the founders of Gescheit & Partners — a global multi-stage venture advisory firm working with bold, visionary and resilient founders. Through Gescheit & Partners, Illai actively nurtures startup founders through the firm’s venture equity firm. He also serves on the boards of Atlas Capital and Barka Fund. Illai shares with Ethan lessons from years as a serial entrepreneur, and in the past working as a Venture Partner at Siemens Energy Ventures and Entrepreneur-in-Residence at BP. On this episode of Build to Last, Illai shares his first journey into the tech industry, the difference between corporate hires and startup hires, and (of course) why kindness is a great business strategy.

We live in a world of noise. Signals are in abundance. There is too much data, too much fuzz and fluff that makes decision-making a real challenge. It’s issues like these that founder Ryan Manuel is trying to solve. As the founder and CEO of Bilby, his main goal is to use AI and machine learning to automate regulatory and government analysis, turning fuzzy policy signals into real, actionable insight for traders and investors. Ryan’s regulatory analysis software covers the regions where government policy is the most nebulous, like China and India. Prior to his foray into the startup world, Ryan was also an Associate Professor at the University of Hong Kong and a Rhodes Scholar at Oxford University.On the Build to Last podcast, Ryan discusses with Ethan some key trends in AI: how AI is redefining the startup playbook, his expert opinion on Chinese vs. American AI, and “it’s never been a better time to be nimble…” to be the “little guy.” He also shares his views on what young people should do to achieve success.