The secondary market for private company shares has grown rapidly, but pricing infrastructure has lagged behind. For most market participants, the price of a name like OpenAI or SpaceX is still whatever number comes through on a broker’s phone call, a single data point with no composite context, no observable bid-offer spread, and no audit trail.
A fireside chat at the A-Team/Eagle Alpha Alternative Data Conference in New York examined how that gap is starting to close, and why the implications extend well beyond private market participants. The session featured Nick Fusco, founder and CEO of PM Insights, in conversation with Julia Bardmesser, adjunct professor at NYU Stern School of Business and founder and CEO of Data4Real.The discussion drew a direct line between the pricing infrastructure that fixed income markets built over the past two decades and what is now emerging in private markets. The argument is that a single broker quote for a private company name is no more useful than a single dealer mark on a bond, and that aggregating bids, offers and trades from dozens of broker-dealers active in the institutional secondary market can produce a meaningfully more robust composite price.
That composite approach already draws on a substantial dealer network. It is also cross-referenced against publicly available data, including SEC import filings showing where 40 Act funds hold private assets. The discrepancies between those public marks and secondary market activity can be striking, with different funds marking identical shares at vastly different levels, or common stock being valued at multiples of preferred in ways that are difficult to reconcile.
From transparency to signal
The more interesting story for data-driven investors is what happens when private market pricing data is used not just to value private holdings, but as a signal source for public equity analysis.
The discussion highlighted the deep ownership connections between public companies and private market names. A public company whose core business model is under pressure may nonetheless hold a portfolio of early-stage investments collectively worth billions. Another whose stock price has declined sharply may hold a substantial position in a high-growth private AI company that has appreciated significantly. For a quant researcher or portfolio manager modelling public equities, real-time pricing on those private holdings is material information.
The connections run in both directions. Partnership dynamics in private markets – a wave of data vendor integrations with a major AI platform, for instance – can create pricing signals for public companies. And macro events, such as regulatory intervention, can reverse the narrative just as quickly.
Correlation breakdown
At a sector level, the session surfaced an observation that the traditional correlation between public and private market pricing is breaking down. For much of the past few years, there was a recognisable lag: downturns in public markets would feed through into private valuations with a delay. That pattern is shifting.
Private SaaS companies in particular are showing more pricing stability than their public counterparts. The so-called “SaaS apocalypse” playing out in public markets has not taken hold to the same degree among private names. This is not simply a case of outdated valuations masking a downturn; secondary market activity levels remain robust, suggesting the divergence is real. For investors looking for cross-market signals – or trying to anticipate IPO pricing dynamics – that’s a data point worth tracking.Operational impact: moving up the fair value hierarchy
Beyond the alpha applications that hedge funds are understandably reluctant to disclose, the session highlighted a quieter but potentially more consequential shift in how private market pricing data is being used operationally.
Valuation teams at institutional investors are using composite secondary market pricing to move private holdings from ASC 820 Level 3 – subjective, model-based marks relying on last-round pricing or discounted cash flows – up to Level 2, where valuations are supported by observable market data. The trade-off is greater mark-to-market volatility, since prices can no longer sit static between funding rounds. But the gain is a more defensible, auditable valuation process.
For firms preparing for an environment where private market allocations are growing and regulatory scrutiny of valuation practices is tightening, that operational upgrade may prove as valuable as any trading signal.
Looking ahead
As private companies stay private longer and secondary market volumes continue to grow, the infrastructure gap is only going to widen without the kind of composite pricing approach discussed in the session. Multi-dimensional calculation engines that triangulate pricing across multiple data inputs – essentially econometric frameworks for private market valuation – are one direction the market is moving in.
The broader trajectory is clear: private market pricing is moving from anecdote toward observable, composite, institutional-grade data. For the buy-side and sell-side firms that have spent decades building analytics around public market data, that represents both a new data source and a new set of workflow challenges.
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