[REPLAY] Deep Basin – Earning Alpha in Energy - [Invest Like the Best, EP.81]

Episode Summary

Executive Summary: Patrick O'Shaughnessy re-releases his conversation with Deep Basin Capital's Matt Smith and Ian Singer, unpacking how they exploit energy-sector complexity with a data-heavy, market-neutral long/short process. The episode explains why energy is fertile ground for alpha, how they model assets from the well level up, and how culture and process support repeatable returns.

Main Topics: Why energy is a rich hunting ground (Priority: 5/5): Energy’s complexity and volatility create mispricings that skilled investors can isolate. Taxonomy of the energy ecosystem (Priority: 4/5): They map upstream, midstream, downstream, and services to frame the industry. Modeling assets from the well level (Priority: 5/5): Deep Basin rebuilds companies from ground-level data to forecast cash flow and productivity. Neutralizing macro and commodity risk (Priority: 5/5): The portfolio is designed to remove oil, gas, rates, and style factors. Value, longevity, and embedded expectations (Priority: 4/5): They focus on future cash-flow durability, not just cheap current multiples. Portfolio construction and leverage (Priority: 4/5): Leverage depends on idea breadth, conviction, and regime stability. Culture, process, and firm-building (Priority: 3/5): Intellectual honesty and obsessive process adherence are core to the firm.

Key Arguments: Energy offers repeatable alpha because assets, data, and narratives change constantly. Well-level data lets them infer the second derivative of cash flows and returns. Their goal is a portfolio roughly free of oil, gas, interest-rate, and style-factor exposure. Cheap stocks can be traps if declining assets force destruction of future value. A company can look expensive today yet become cheap as cash flows compound. Portfolio leverage should rise with idea breadth and fall in unstable macro regimes. Passive flows into energy ETFs can create distortions they try to exploit.

Data Points: Episode number: 81 - Patrick says this is a re-release of episode 81 with Deep Basin Capital. Years since first meeting Matt Smith: almost 10 years ago - Patrick describes his long relationship with Matt Smith. Years in energy: eight or nine years - Matt says he has spent roughly this long focused on energy. US upstream count: 70 businesses - Matt cites the number of upstream US businesses making the same product. Canada upstream count: another 15 - Matt notes additional comparable companies in Canada. EOG free cash flow example: a couple billion dollars - Matt uses EOG at $60 crude as a valuation/modeling example. Model horizon: the next 50 years by quarter - They describe building quarterly models far into the future. Firm age: a year or two years old - Patrick asks about culture at the then-young Deep Basin firm. Career start in buy-side investing: 2005 - Matt references starting professionally on the buy side in 2005. Time at prior non-compete: a year - Matt says he sat out a year after leaving his prior firm in early 2016. Team-building timeline: almost a year - They spent nearly a year building technology for the firm. Relative stock-price period: last four or five years - Matt says commodity falls hurt intrinsic value over this period. Market-cap example: $200 million - Ian contrasts a small company example with ExxonMobil. Locations example: 100 locations - Ian cites the scale of one small company's asset base. Low multiple example: four times - They discuss a company trading at four times with limited drilling runway.

Pivotal Quotes: "we purposely try to make sure it's roughly zero" — Matt Smith: On keeping commodity exposure near zero in the portfolio. "we're not factor investors, but we're very aware of factors" — Ian Singer: On separating genuine alpha from style exposure. "it needed to be capitalized like a technology business" — Matt Smith: On why Deep Basin required significant long-term technology investment.

Implications: The unresolved edge is still execution: listeners should watch whether Deep Basin can keep converting rich data and process discipline into durable alpha across cycles.