Privacy-Preserving Pricing Layer for Private Markets
Javier Avalos, co-founder and CEO of Caplight, on building synthetic derivatives of private stock
The real bottleneck in private market trading is not finding buyers and sellers, it is getting enough shared pricing context that both sides can move quickly. In practice, investors still patch together broker quotes, tender data, cap table signals, and independent research just to decide where a trade should clear. A consolidated data layer matters because it turns pricing from a weeklong scavenger hunt into a repeatable workflow, which is what makes markets feel liquid.
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Caplight’s own trading workflow shows why this matters. Early derivative orders were priced by manually running auctions with roughly 25 counterparties over days, then using those responses to infer where a contract should trade. Better shared data compresses that discovery step.
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Other players have attacked one slice of the problem. Zanbato pools transaction data to show pricing and demand curves for restricted stock. Carta uses cap table data to run tenders more smoothly. Each dataset helps, but none alone gives the market a full picture.
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That is why data products tend to come before true marketplaces in private stock. Participants need enough trusted reference points to believe a quoted price is real. Without that trust, every transaction stays bespoke, bilateral, and slow.
The next phase of private market infrastructure will look more like a shared operating system for pricing than a simple listings venue. As more brokers, platforms, and research providers feed standardized signals into the market, derivative trading, tender programs, and LP stake transfers all become faster, more comparable, and easier to scale.