Insights

Last round vs. real price: why private data is the true alpha?

How do you price an asset in the private market?

In public markets, price discovery is instant. You have a ticker tape. You have an order book. In private markets, there is no market price. There are just “vibes” and speculation.

Without data, you are essentially gambling, like a blind monkey throwing a dart at a dartboard and hoping you hit something.

Without an anchor, massive volatility reigns in the private market. A tweet of a demo can cause a 6x markup on a company’s valuation. An asset might move 100% in 24 hours simply because there is no order book to absorb the noise.

This chaos can be an opportunity, but only if you have the transactional data to see through it.

The public data won’t cut it

Most investors rely on public databases like Pitchbook or Crunchbase. While useful for historical context, these platforms are facing a crisis because AI is so good at scraping public data. If it’s publicly available, AI will find it.

But the transactional data, i.e., the real-time record of what people are actually paying for an asset today, remains hidden.

Take the collapse of Byju’s. For over a year, public databases continued to list the EdTech giant at its peak valuation of $22 billion. To the outsider, it looked stable. But inside the private market, the bids evaporated; private transaction data showed a straight line to zero long before the headlines caught up.

The transaction data is the gold.

A good example: the majority, almost half, of the London Stock Exchange Group (LSEG) revenue comes not from trading fees, but from data and analytics. And LSE doesn’t feel threatened by advancements in AI because they are the only ones who possess the raw transaction logs.

This trend defines the modern financial infrastructure. The business models of Nasdaq, ICE, S&P Global, and other major players have shifted, with a massive chunk of their revenue now driven by proprietary analytics rather than simple transaction fees.

This is the last moat: the specialized knowledge that isn’t publicly available – the data layer. The transaction data is the gold.

Pricing by “vibes” vs. pricing by velocity

Why do we see massive dislocations in private valuations? Because without a brokerage to anchor the price, sellers rely on hope, and buyers rely on fear.

Take the recent volatility in AI valuations. You look at OpenAI trading at implied valuations of $500 billion. But barely a year ago, OpenAI did a round at $157 billion. Why is it trading at $500 billion in the secondary market? There is no sound logic in that pricing despite having public references.

This is pricing by vibes.

The only way to find the fair market value is to price by the velocity of capital.

To be clear, low velocity isn’t always bad. Long-term investors like Warren Buffett hold assets for years by choice, that is a strategy.

But in the secondary market, we look for involuntary low velocity. If there is a line of sellers trying to exit, but no trades are clearing, that standstill is a red flag. It means the buyers reject the current price. The headline valuation might say $10 billion, but the lack of movement tells us the real price is much lower.

The catch is, only a brokerage sitting in the middle of the flow can see that the market has frozen. Everyone else sees the old price tag.

The “shadow” order book

The greatest advantage of private data is the ability to see the “shadow order book” – the gap between what sellers want and what buyers will pay.

In public markets, the spread is pennies. In private markets, it can be infinite.

A seller often thinks their asset is worth the last funding round valuation, but they don’t factor in illiquidity discounts. This is where internal data becomes a negotiation super-weapon.

Knowing that 10 buyers refused to pay above $20 recently is far more valuable than knowing one investor paid $100 a year ago.

Furthermore, private data reveals the context of a trade, which public data ignores. In public markets, a share is a share. In private markets, a share sold by a distressed venture fund facing expiration is priced very differently from a share sold by a founder realizing part of their profits.

When a seller comes to us demanding a premium, we don’t just say, ‘That’s too high.’ We use data as the anchor. We tell them: ‘That’s a great price, and we would love to get that for you. However, the last 10 people that reached out to us were only willing to pay 20 cents on the dollar.’

This alpha comes from a record of rejected bids.

Public data shows you the deals that happened, but private brokerage data shows you the deals that failed. Knowing that 10 buyers refused to pay above $20 recently is far more valuable than knowing one investor paid $100 a year ago.

This is also how you spot a ‘Zombie Unicorn.’ PitchBook tracks over 1,400 unicorns with a collective valuation of $5 trillion. But our internal ‘shadow book’ might show that for 30% of them, there hasn’t been a valid bid in six months. Publicly, they are worth billions. Privately, their liquidity score is zero.

Oracles for the next derivative market

Why does this data matter for the future? Because the market is desperate to financialize private assets.

Everyone wants to trade perpetuals on private equity. They want to long SpaceX or short a pre-token crypto project. But you cannot build a derivatives market without a spot price or reliable pricing oracles that feed real-time, trade-verified pricing into the smart contracts.

And as this type of market develops, it relies entirely on the emergence of on-chain transfer agents and risk algorithms based on real secondary data.

Conclusion

In a world where AI has commoditized general knowledge, the only alpha left is proprietary data.

If you are an investor relying on the same Pitchbook dashboard as everyone else, you are throwing your darts in the dark. To win in the private market, you need to see the bids, the asks, and the rejected offers that define the true price of liquidity.