From $500M in Secondary Deals to Building an Investment Model · John Zic, Equiam

What we talked about.

In this episode of the Sunny Ray Show, host Sunny Ray talks with John Zic of Equiam, who brokered $500 million in secondary transactions at Forge before moving to the investing side. Zic explains why many private company sellers need liquidity for life reasons rather than economic ones, and how that led to Equiam's systematic, data-driven approach to investing through the secondary market. He describes a screening-only model built on proprietary data, including data rooms, hiring signals, and sentiment information, that narrows roughly 3,000 growth-stage companies to a handful each month. The conversation covers messy cap tables, the pricing gap between common and preferred shares, probability-weighted case modeling for uncertainty, and why following brand-name venture firms can mislead. Zic also shares a candid lesson from a costly early fund investment and explains why a headline valuation in a funding round can differ sharply from an investor's real economic exposure. The episode closes with where listeners can learn more about Zic and Equiam.

A former secondary-market broker explains how Equiam screens 3,000 private companies down to a handful, and why headline valuations mislead.

The questions, and the answers.

What did brokering $500 million in secondary transactions at Forge teach you about how private company shares trade?

It opened my eyes to why people need liquidity. Many sellers are not economic sellers. They need money for a house, health care bills, or college. At Forge we connected them with large institutions and family offices. We facilitated $500 million, but there was probably five billion dollars of liquidity need, and sellers had very few avenues before.

What does your model decide, and what still requires human judgment?

The model is a screen only. Private market data quality isn't good enough to trust blindly. It ingests data rooms, hiring data, and sentiment and review information to build a surface map of spiky anomalies. It narrows about 3,000 growth-stage private companies to maybe five or six a month. Then we do deep underwriting and apply human judgment.

How do you approach a messy cap table?

First I need to know which shares I'm actually buying, from common stock to the latest preferred round, because each class has a different price. Debt and convertibles add dilution. We act like scientists on cap tables. We'll happily buy common, but at a 30 or 40% discount to the latest preferred round, because that reflects the cash flow in an acquisition.

What is a signal that looks important but often misleads people?

Following other investors. Many people copy the tier one VCs, but not all of them are created equal. Some had amazing historical runs and have been weaker in recent years. You have to partition out who the truly intelligent, leading capital is versus firms with a great brand and less impressive recent performance. Blindly following brand doesn't have much predictive value.

How do you account for uncertainty rather than hiding it in a single number?

We rely on audited financials for actuals, but projections have a huge range of outcomes, and management cases are usually the most optimistic. So we model a hyperbull case, then neutral cases down to a hyper pessimistic one, apply probabilities, and get a probability-weighted fair value. If the entry price is at or below that, it usually gets a go vote.

What would make you pass on a company that otherwise looks attractive?

Sometimes the valuation simply can't pencil out, even with big odds on the hyperbull case. We don't shy away from binary outcomes, since playing it safe in venture is a mistake. But I'll pass when the TAM is tiny or growth is driven by something exogenous, like an interest rate regime, that is unlikely to persist.

What have you learned from an investment that didn't work out as expected?

In our first fund we invested in a company with amazing topline growth but extraordinarily bad bottom line performance, betting the IPO window would open. The IPO was pulled and value fell 90%. One warning signal was capital raised versus value created. It taught us to be much more careful about capital efficiency and the actual business model.

What do most people misunderstand about investing in private company shares?

The messy cap table. Buying common is not the same as buying the latest preferred round. When Sequoia leads a round at a $20 billion valuation, they don't believe it's worth that today. They sit at the top of the preference stack, so the headline number is a call option. Separate the published mark from your actual economic exposure.