Odds on Open

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.