The Evolution of Private Market Liquidity: From OTC Trades to Algorithmic Pools

It is no news that Private funds have long been burdened by illiquidity. Complex investment structures, opaque valuations, and lack of a connected trading network all contribute to persistent illiquidity. These issues reinforce each other and create a fragmented ecosystem. Investors who need to exit their positions have been forced to find a specific counterparty, which can take months and often results in steep discounts. In this article, we look at how removing 1:1 counterparty matching and reducing valuation time in Private Asset Secondary trading can reduce network connectivity frictions

Financial markets have historically faced similar liquidity issues. Initially, transactions were predominantly bilateral, requiring direct matching between individual buyers and sellers. This method was inherently slow, costly, and inefficient, limiting the market's scale and liquidity.

In the early public stock market of the late 19th and early 20th centuries, trading occurred predominantly over-the-counter (OTC), meaning every transaction was directly negotiated between two counterparties. This limited transparency, increased search costs and time, and resulted in wide price spreads. The introduction of centralized stock exchanges like the New York Stock Exchange (NYSE) revolutionized this system. By aggregating buyers and sellers into a single order book, exchanges enabled any buy order to be matched with any available sell order, significantly speeding up transactions, narrowing bid-ask spreads, and greatly enhancing price discovery and transparency.

This trend has continued in recent history. Commodity and derivative markets transitioned from bilateral trading to clearinghouses—such as CME and LCH—that centralized counterparty risk management, netted exposures, and substantially boosted liquidity. After the 2008 financial crisis, the swaps market similarly shifted from opaque bilateral arrangements toward centralized clearing mechanisms, resulting in greater transparency and reduced systemic risk.

More recently, the decentralized Finance (DeFi) ecosystem has come up with creative solutions to provide liquidity for illiquid assets within the ecosystem. One such innovative solution has been the Automated Market Makers (AMMs), which replace traditional order books with liquidity pools, removing the need for 1:1 counterparty matching. In AMM-based exchanges like Uniswap, users deposit assets (typically cryptocurrency tokens) into a pool, and trades are priced algorithmically, so anyone can buy or sell from the pool at the current price. This allows for 24/7 trading with dynamic pricing based on the relative supply of each token in the pool. DeFi’s continuous liquidity and automated pricing mechanism highlight a promising path for enhancing liquidity in traditionally illiquid markets. While AMMs have helped improve liquidity in DeFi, especially for long-tail assets, and also reduce barriers to entry, they still pose challenges for real-world illiquid private assets. Nonetheless, we can draw inspiration from them and create a more applicable solution to private assets.

Academic research has also long pointed out the inefficiencies in traditional private fund secondary markets. On average, private fund secondaries have traded at roughly 85–95% of NAV in normal market conditions. Multiple industry reports show that H1 2024 secondary pricing averaged ~86% of NAV across asset classes. The Tuck School of Business has further shown that liquidity discounts spike during market stress, with the widest discounts of the decade coming in 2022, when secondary interests sold at 81% of NAV on average.

The need for 1:1 counterparty matching is a significant contributor to these inefficiencies primarily because sellers must devote substantial time and resources to finding suitable buyers, conducting valuation analyses, navigating lengthy negotiations with multiple parties, and keeping initial engagements warm while they chase new ones, often resulting in steep discounts, particularly during market volatility.

All these historical and modern examples, from the transformation of OTC stock trading to centralized exchanges, grain futures and stock clearinghouses, to derivatives CCPs and decentralized AMMs, demonstrate a clear principle: removing the requirement for direct 1:1 matching unlocks liquidity by pooling resources and risk.

Introducing the Automated Liquidity Pool (ALP)

Inspired by historical innovations and cutting-edge DeFi solutions, Pontoro’s Automated Liquidity Pool (ALP) is a transformative new model designed to bridge the gap between illiquid private markets and liquid investment structures. By combining dynamic liquidity management, algorithmic pricing, and tokenization, the ALP offers investors, on one hand, flexible access to private market yields while maintaining liquidity optionality, and on the other, it offers Limited Partners (LPs) invested in private funds a more efficient and better-priced exit.

The ALP pools liquid capital from participants and deploys it into liquid money market assets and illiquid private fund interests. It utilizes algorithmic liquidity management to essentially impart the liquidity of money market assets onto the private market assets and, on the flip side, impart the enhanced yield from private assets and their illiquidity premium to the liquid money market assets. Since it's a pooled vehicle, the ALP eliminates the need for 1:1 counterparty matching for illiquid interest sellers as they simply sell it to a pool.

Furthermore, this solution is available for any fund manager to set up their own ALP with configurable parameters and provide liquidity to their LPs.

Now let's take a look at how the ALP handles pricing which is what will incentivize pooling in the first place.

Two-Layer Pricing: Decoupling Credit and Liquidity Risk

A core innovation of the ALP is a unique two-layer pricing model that separates credit risk from liquidity risk—and it is particularly powerful for private credit assets.

Layer 1 – Credit Risk Discount: This layer reflects the inherent credit risk of the underlying private fund interest. For private credit assets, the bulk of the valuation work is completed during the initial credit assessment. Subsequent valuation updates rely on recurring principal and interest (P&I) payments and minimal ongoing credit analysis. Once the initial valuation is set, and outside of impairments, the credit risk discount remains relatively stable.

Layer 2 – Liquidity Discount: Unlike credit risk, liquidity fluctuates dynamically. The ALP applies an algorithmically managed liquidity discount that adjusts in real time based on the pool’s current liquidity levels. When liquidity is scarce, the algorithm increases the discount, which discourages more private asset sales into the ALP. Furthermore, the decrease in liquidity and increase in discounts increase the yield to ALP investors to attract more deposits. When liquidity is abundant, the discount is reduced, which attracts more asset sales and reduces yield to ALP investors to dissuade more investors. These opposing forces maintain the ALP in a dynamic equilibrium. This separation ensures that the pricing remains responsive to market conditions without requiring constant revaluation of the underlying asset’s credit risk.

Qualified Private Asset Investors can sell their interests directly into the ALP at any time, bypassing the delays of finding a direct counterparty by leveraging the pooled liquidity provided by the ALP. This efficiency compounds for subsequent fund transactions. Once the ALP manager does the initial credit analysis on a fund (layer 1), any subsequent asset purchases from that fund will require minimal credit analysis work. Since the liquidity discount is algorithmic, subsequent asset sales into the ALP by a limited partner looking for liquidity will be significantly faster than traditional routes. Another key advantage is that the pricing signal continues to propagate beyond the initial transaction; we will discuss more on that in a separate article.

The evolution from OTC trading to centralized exchanges, and from traditional order books to AMMs, has consistently demonstrated that pooling liquidity drives market efficiency. Academic insights have confirmed the shortcomings of the 1:1 counterparty matching model, particularly in the private fund space, where liquidity discounts can be substantial.

At Pontoro, we take these lessons to heart. By eliminating the need for direct matching and introducing a sophisticated two-layer pricing mechanism—decoupling stable credit risk (Layer 1) from dynamic liquidity risk (Layer 2)—the ALP offers a solution tailored for modern private funds. It provides continuous liquidity, optimized pricing, and enhanced transparency, paving the way for a more efficient and inclusive financial ecosystem where assets are managed, achieve liquidity, and are recorded in a truly transformative manner.