What durable customer relationship is the market is missing?
Many hot investment themes arise from a newly-legible durable customer relationship. You often find that the theme creates its own metrics to look through GAAP accounting and see that durable FCF. John Malone & the cable cowboys promoted homes passed & EBITDA to roll up cable distribution monopolies and exercise economies of scale. Subscription revenue models (both consumer & SaaS) began to command high multiples when investors internalized metrics like CAC, churn, and net dollar retention. Some products look like they could fit the bill if they could be modeled as more durable than they currently appear (e.g. perhaps asset management incentive fees, bundled biotech R&D pipelines). What do you think is the next “durable customer relationship” category that the market is missing?
What is the right taxonomy of GAAP relationships?
Accounting represents relationships between economic actors, but our current reporting framework makes it hard to conceptualize these relationships. We should be able to visualize the network graph of businesses (nodes) and their relationships with one another (edges). Right now, we can only see a single company report data in a siloed format. Network-based accounting would help us aggregate the micro into the macro, and then run accurate agent-based models of the economy. This essay contains my first shot at thinking this through -- thoughts on a better taxonomy here?
What is the best signal that your investment ideas have gone stale?
The half-life of facts dominates investment performance. My biggest mistakes have come when good ideas have gone stale. Likewise, my biggest mistakes of omission stemmed from bad ideas that weren’t discarded quickly enough.
Some solutions include committing to re-underwrite your ideas regularly, red-teaming your thesis (finding a short thesis for your long, vice-versa), selling a stock for a few weeks to remove your commitment bias (where possible from a tax perspective), and tracking P&L against your expectations (though not all near-term price action = fundamental information).
What is the best signal you’ve seen to determine that your investment ideas and/or frameworks have gone stale?
How can we build investment funds with less key-man risk and more continuity?
Most companies rely on the top 3-5 people but can survive leadership change. Most funds are so dependent on the top 3-5 people that the fund itself collapses if any of those top people leave. Renaissance & Citadel are probably the best two counterexamples that have properly operationalized their businesses (though we shouldn't expect Ken Griffin to retire anytime soon).
What key piece have most investment funds missed that would enable a more durable company? Is it about culture, risk management, some ineffable investment prowess, or simply institutionalizing more than the typical fund does?
Where is the ECRI / Ed Leamer / Cornerstone Macro business cycle framework wrong?
A popular macro approach is to track the consumer credit cycle. Here's a simplified version. During boom times, housing & durable goods manufacturers over-extrapolate cyclical demand and buy too much inventory. When consumer credit tightens, those businesses are forced to fire-sale their inventory and fire their employees. That's why housing & durable goods are considered the leading sectors. Eventually the credit environment normalizes, and the cycle can begin anew.
Predecessors of this theory have existed for quite a long time, but I think they were first popularized by ECRI, were best codified by Ed Leamer, and are now best practiced by respected shops like Cornerstone Macro (@ Piper Sandler).
What are the major blind spots in this framework?
Is growth software now cyclical?
In Technological Revolutions and Financial Capital, Carlota Perez argues that major technologies undergo two phases: an internally-focused boom & bust and an externally-focused distribution of the tech across the broader economy. If you think of the dotcom era as the boom & bust, we are now >20 years into the distribution phase for software. This implies that software businesses continue to get more and more exposed to the macro credit cycle.
As software permeates cylical end markets, how much growth software is actually cyclical?
How much does shipping cadence predict future breakthrough products?
The best thing a growth company can do is create a hot new product. It solves for revenue growth, fundraising, recruiting, almost everything (obviously). Shipping cadence is typically lauded as the best metric for investors to evaluate a product+engineering team from the outside. But is that really the best way to determine who will release and/or distribute the next breakthrough product in their area? Could it lead you to discard the efforts that require expensive milestones to show progress? What's a better way to figure this out?
Would investors focus more on fundamentals if public equities trading was restricted to a few one-off auctions/day?
Painting with a broad brush, US capital markets trade in two auction formats. Private equities are sold in one-off auctions and then trade as secondaries in private transactions. Public equities IPO and then trade in with continuous bids and asks. If public trading was restricted to a few daily auctions that were resistant to latency arbitrage, would we actually see more focus on company fundamentals? Or would we just see a different version of today's market microstructure & investor set?
Why do we use static discount rates in DCF models instead of letting the rate at each period be stochastic or a geometric random walk with drift?
Interest rate expectations shift constantly. Just read a few issues of the Philadelphia Fed's Survey of Professional Forecasts to see how difficult this forecast can be. Equities (especially for the high-quality companies I focus on) are valued as extremely long-duration securities — think decades, not months. If we’re going to use DCF models, shouldn’t we adjust the discount rate to compensate for interest rate uncertainty?
How can we estimate single-stock price elasticity to $1 of inflows? And where does inflow modeling break?
Gabaix and Koijen codified an Inelastic Markets Hypothesis to estimate that every $1 of cash inflows increases stock prices by ~$5. How can we bring these elasticity estimates to the single-stock level?
And where does this type of modeling break? For instance, if a company reports bad news and buyers & sellers adjust their bids & asks accordingly, haven't we seen price impact without any $ flows? And how can we include ETF creation/redemption influence here?
Did low-float index investing exacerbate, or even lead to, the late 90’s tech bubble?
(related to the Inelastic Markets Hypothesis)
How much did market cap weighted indexing contribute to the 90's tech bubble? These concerns were raised by participants at the time (eg read Hugo Dixon’s “On the wrong track” in the FT on 2/23/1999), and Mike Green of Simplify suggested this thesis more recently. David Blitzer, former chair of S&P’s index committee, argues that the S&P 500 index had always excluded stocks with float <50% (though that doesn't account for other indexes).
Why don’t companies directly issue shares during index/ETF inclusion events?
IPOs are frequently derided when they pop on the first day (perhaps unfairly). Shouldn’t we treat index/ETF inclusion events the same way? Should TSLA have raised billions directly upon the 12/2020 S&P 500 inclusion?
What is the best way to CTRL+F for web pages you’ve visited but not bookmarked or noted (i.e. grep for your web history)?
For that fact you remember reading, but you can't quite remember where. Storage is cheap enough that we could just store the last month of plain text, right?
When will Larry Harris publish an updated version of Trading & Exchanges?
A personal request, though Columbia's new Special Study of the Securities Markets looks promising.