Odds on Open

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 priors
Timestamps:(00:00) Intro(04:47) Tutor vs Glenview vs Point72: how edge differs(12:29) How to build “lift” for PMs: at-bats, hit-rate, sizing(18:44) Building research edge: outwork, read, fieldwork(27:16) Personal moat in 2026: analogs, history, decision trees(40:08) “Main Street becomes Wall Street”: what that actually means(44:30) Carbon Arc thesis: “decimalization” of data market structure(46:43) Why the edge migrates to data plus domain context(51:00) How to win in commoditized research: sample size beats anecdotes(01:03:26) Factorizing everything: themes, market structure, business models(01:08:37) Pruning decision trees: signals, scale points, inventory dynamics(01:14:18) Contrarian 2026 take: hedge funds launching enterprise AI labs(01:23:32) Final question: one habit to build career alpha
Follow 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 rails
Why SIG's poker training program—playing for hours daily, turning over cards after every hand, and defending each decision quantitatively—builds world-class traders
How thinking in bets becomes second nature and why Andrew now frames every decision (like private school vs public school) as an expected value calculation
The 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 optionality
Finding edge in prediction markets: liquidity incentives, identifying who you're trading against, and why some markets are wildly inefficient
Trading 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 trades
The insider trading debate in prediction markets and why Andrew thinks it's corrosive to incentives, trust, and long-term market quality
Risk transfer opportunities: using prediction markets for insurance-like hedging (Florida hurricane risk, California earthquake exposure) rather than pure speculation
Whether prediction markets are good for society: the value of probabilistic news context versus the risk of casino-ification and degenerate gambling
Career advice for aspiring traders: evaluating if you can handle constant screen time, limited networks, and high-variance outcomes
How 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 edge
Why Kalshinomics focuses on analytics and custom interfaces for serious traders rather than trying to be the "Bloomberg Terminal" of prediction markets
Lessons 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 difference
Building durable relationships across geographies you're not from
The evolution of edge: from reading 10-Qs at the library to AI
Why understanding your own behavior matters more than any tool
How to think about career decisions when everyone's chasing the same thing
Portfolio construction strategies for managing emerging market risks
Why the best English-speaking management teams often underperform
Sinan 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.