Odds on Open

Ethan Kho

Conversations with leading thinkers on trading, betting, and risk. Formerly the Build to Last Podcast. Hosted by Ethan Kho. Produced by Patrick Kho.

  1. 4日前

    Ex-AIA Quant Director: Every Hedge Fund That Fails Makes THIS Mistake

    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

    44分

  2. 5日前

    Partners at Versor Reveal Their Quant Strategies Managing $1.4 Billion

    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 here Why 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

    1時間2分

  3. 6日前

    Ex- Citadel Analyst and Millennium PM: What It’s Like Inside Both Hedge Funds

    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

    53分

  4. 11月8日

    Hedge Fund Manager Alix Pasquet: Why Smart People Lose Money

    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

    1時間25分

  5. 11月6日

    GBE Founder Cory Paddock: Great Traders Know When a Regime Change Is Coming

    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

    42分

  6. 11月5日

    Christina Qi Started a Hedge Fund From Her Dorm Room. Now, Top Trading Firms Now Buy Her Data.

    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

    52分

  7. 11月4日

    159 Billion-Dollar Quant Investor: Stop Only Investing in the S&P500

    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

    53分

  8. 9月23日

    She Left Citadel and Built a BILLION-DOLLAR Hedge Fund

    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

    36分
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番組について

Conversations with leading thinkers on trading, betting, and risk. Formerly the Build to Last Podcast. Hosted by Ethan Kho. Produced by Patrick Kho.

情報

  • クリエイター
    Ethan Kho
  • 配信期間
    25年
  • エピソード
    31
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © Ethan Kho
  • 番組のWebサイト
    Odds on Open

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