Liquidnet Targets Long-Term Investors with New Business Unit

Liquidnet is building a new business line that will run alongside its equity execution and fixed-income businesses. Set to be officially rolled out next year, the new unit, dubbed Investment Analytics (IA), will bring the company’s last three acquisitions—Prattle, RSRCHXchange, and OTAS—under one roof.

The firm has begun piloting some of its new technologies with a handful of large asset managers, combining Prattle’s experience in the field of natural language processing (NLP), OTAS’ use of machine learning (ML) in structured market data, and RSRCHXchange’s investment research platform. The target audience comprises long-term investors and analysts, says Adam Sussman, head of market structure and liquidity partnerships at Liquidnet.

IA is still in its early stages, so the firm is experimenting with how best to fit the product within investors’ workflows, and validate the concepts and models on which they are building. But some potential use cases have already been defined.

Prattle has a core equities product that analyzes companies’ earnings calls. For each person who speaks during a call—both management and sell-side analysts during the Q&A session—a sentiment score is generated, and Liquidnet can overlay that using OTAS’ trade information about a particular executive at a firm and how they trade based on that sentiment.

“We can see Executive A tends to be really good at buying the stock—always buying in on the lows. … Executive A always tends to have negative sentiment, then that leads to the stock going down, then they buy the stock,” Sussman says.

Then because each sell-side analyst is also rated on their sentiment during an earnings call, that score can be presented and viewed alongside all their published research and content via RSRCHXchange.

“For me, it’s really about we’ve got these puzzle pieces, and we’re putting them together,” Sussman says.

It was around the 2017 OTAS acquisition that Sussman and others at Liquidnet struck up in-depth talks with portfolio managers and analysts about how to make the trading desk of, say, a mutual fund company, less of a cost center and more of a value-add. A key group they’re working with are the active managers who are seeking to maintain a competitive edge as assets move more toward passive.

“In talking to them, we realized that there was this opportunity to help leverage those same technologies to help them come to conviction faster [and] investigate investment hypotheses,” Sussman says. “The challenge we saw in the marketplace is that a lot of the firms that are looking at leveraging the same set of technologies are catering to a different type of investment strategy. Some of them are focused on quant funds or very short-term discretionary hedge funds, but no one’s built a product around servicing the needs of long-term investments.”

Machines, Sussman adds, are not known for their ability to look into the future. It’s hard to generate perfect insights or apply end-to-end automation for a time horizon that could span three or five years. But this kind of automation can help portfolio managers and analysts focus on companies and datasets where something significant has happened, and find the alpha-needle in the haystack of filings and reports.

Sussman points to a proof-of-concept tool Prattle developed prior to the acquisition around facial recognition. It analyzed micro-expressions, such as a smirk given by someone on TV. “One of the exciting things about being in this space is the almost endless possibilities: using these types of technologies to uncover hidden meanings—within video, words, whatever it is,” Sussman says.