Feb 17, 2020 · 1h 4m · capital-allocators
Dan Rasmussen – Private Equity Risk and Public Equity Opportunity at Verdad Advisers (First Meeting, EP.15)
gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this episode of Capital Allocators, host Ted Seides interviews Dan Rasmussen, founder of Verdad Advisers, who deconstructs the historical drivers and systemic vulnerabilities of private equity and private credit. Rasmussen explains how Verdad utilizes machine learning, rigorous credit underwriting, and strict capacity discipline to replicate leveraged buyout returns in deeply discounted public micro-cap equities and execute systematic crisis playbooks.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Ted holds 17.8% of the talking time here. How this is scored →
speaking balance: gold is Ted, purple is the guest (3 minute bins)
Dan forcefully rejects private market valuations, arguing that buying proforma-adjusted micro-caps at 16x EBITDA while the S&P trades at 12-13x defies logic and is destined to fail.
Hardest push from Ted ▶ 36:02 Ted Challenges Study for Conflating Share Price and Business FundamentalsTed firmly interrupts Dan's CEO pedigree conclusion by pointing out that measuring share price performance fails to test whether superior management improves actual business operations.
Biggest teaching moment ▶ 14:35 Dan Dismantles Growth-Oriented Leveraged BuyoutsDan walks through the corporate finance reality that heavily levered companies cannot fund the capex and SG&A required for growth because cash flow is drained by debt service.
Ted holds their own ▶ 36:02 Ted Exposes Methodological Blindspot in CEO Track Record StudyTed demonstrates his deep fundamental background by catching that Dan's quant study analyzed equity volatility rather than operating metrics, forcing Dan to concede the point.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Ted as informed peer | Guest teaching | Guest disagreement | Ted pushing back | Why |
|---|---|---|---|---|---|---|
| Socratic Upbringing and the Juridical Mindset | 3 | 1 | 2 | 2 | Ted opens by asking if Dan's analytical skepticism stems from natural cynicism as described by Malcolm Gladwell. Dan gently reframes cynicism into a Socratic, legalistic mindset developed around the family dinner table. | |
| Transition from Humanities to Bridgewater and Bain Capital | 2 | 2 | 1 | 0 | Ted prompts Dan to explain how he entered investing from a humanities background. Dan describes his time at Bridgewater and Bain Capital, noting the contrast between academic finance literature and fundamental modeling practices. | |
| Deconstructing Historical Private Equity Returns at Bain | 3 | 6 | 4 | 1 | Dan delivers a thorough empirical deconstruction of private equity alpha, explaining that historic gains came from purchasing sub-7x EBITDA assets rather than managerial genius. He explains why high leverage and high multiples mathematically impair operational reinvestment. | |
| Operational Realities and Limits of Cost Cutting in LBOs | 5 | 5 | 4 | 3 | Ted probes operational value creation and debt refinancing optionality in private markets. Dan points out that sponsor-to-sponsor buyouts leave little room for real cost cutting, while private credit lenders obscure defaults through covenant-lite renegotiations. | |
| Recession Scenarios and the Zombie Buyout Phenomenon | 5 | 5 | 4 | 2 | Ted asks how an economic downturn plays out when immense dry powder collides with weakening fundamentals. Dan cites energy PE fund marks to illustrate extend-and-pretend zombie dynamics before explaining why he turned to public markets to find cheap levered companies. | |
| Algorithmic Screens and Machine Learning Error Detection | 3 | 4 | 1 | 0 | Ted asks how Verdad filters its universe, prompting Dan to explain their quantitative value screens and two-tier machine learning architecture that predicts debt paydown and identifies model errors. | |
| Regional Market Dynamics, Bankruptcy Risk, and Qualitative Judgment | 4 | 5 | 2 | 1 | Dan elaborates on regional screening differences, noting that corporate bankruptcy risk is effectively zero in Japan while US and European models require qualitative human checks to weed out fraudulent value traps. | |
| Empirical Testing of Executive Pedigree and Management Track Records | 6 | 4 | 3 | 6 | Dan reviews a study demonstrating that CEO pedigree and past track records have no predictive relationship with future stock price returns. Ted intervenes with sharp pushback, pointing out that Dan tested share price movement rather than underlying business operational performance. | |
| Portfolio Construction, Equal Weighting, and Capacity Discipline | 4 | 3 | 1 | 1 | Dan outlines his portfolio construction principles, stressing industry diversification, quarterly rebalancing, and capping assets under management to preserve the ability to trade illiquid micro-caps. | |
| Trading Illiquidity, Position Sizing, and Volatility Management | 5 | 3 | 1 | 2 | Ted asks about position weighting and execution friction in micro-cap trading. Dan explains the danger of buying sudden screen jumpers immediately and describes patient execution across weeks as a compensated service. | |
| Developing Credit Strategies and Analyzing Cross-Asset Signals | 4 | 6 | 2 | 1 | Dan details the creation of Verdad's credit strategy with Greg Obenshain, debunking yield chasing in favor of upgrade-prone Goldilocks debt and illustrating lead-lag momentum signals between debt and equity markets. | |
| Research Culture, Transparency, and Navigating Small-Cap Value Cycles | 4 | 4 | 3 | 1 | Ted asks how published weekly research feeds into Verdad's investment process. Dan explains that research transparency serves to align investors psychologically for the 50% of years when small-cap value underperforms. | |
| Crisis Playbook: Capitalizing on Macroeconomic Recessions | 5 | 6 | 3 | 2 | Dan presents findings from Verdad's study across eight historical recessions, demonstrating that quantitative model predictive power surges during downturns and laying out a rules-based strategy to heavily allocate when spreads exceed 600 basis points. |