Two Sigma Machine Learning to Learn “Godfather” to Start His own Funding

One of the most prolific Wall Street investors you've never heard of is getting ready to launch a hedge fund.

He has never been written in mainstream news articles, but in a certain amount of financial circles, Hao Chang is a respected person. Until 2024, for 17 years, Quant Researcher produced some of the most valuable trading signals for $60 billion hedge fund giant 2 Sigma.

A pioneer in applying machine learning to the two sigma markets, Chan is currently planning to hang out his own piece of iron. According to five people who were briefed on the plan, he is in the early stages of talking to potential investors and hedge fund service providers.

Zhang is planning to call on hippocampal capital management for his company, sources said he was targeting the release date in early 2026, one of the people said. It's not clear how much capital he is trying to nurture. The Zhang's contract with Two Sigma banned funds from investors by July 1, people said.

Industry sources hope to attract a lot of interest from investors given Zhang's reputation and track record. His Alpha research has been responsible for billions of dollars in the trading profits of two Sigmas over the course of his career, people said.

Chan did not respond to requests for comment. Two Sigmas declined to comment.

Technique “The Godfather”

Two Sigma, with 1,700 employees today, was a relatively small company when he hired Zhang in 2007. Quant Trading's secret hedge fund niche.

The fund was a pioneer in machine learning, an early artificial intelligence method that helped computers make decisions, make predictions and recognize patterns. Zhang developed a profitable trading model early on, focusing on nonlinear technology in machine learning, with his specialization specialising in both statistics and computer science. Those familiar with Chan said. Groups that evolved around such strategies have taken on different names for many years, but staff shorthands were often simply “techniques.”

The two Sigmas pursue other machine learning initiatives, but the technique has become a closely guarded secret with Crown Jewel. Some call Zhang the unit's “godfather,” according to former employees.

People, including two former Sigma employees familiar with Zhang, said the billions of dollars attributed to his trading model make him one of the top researchers in nearly 25 years of history.

The small footprint Zhang has online comes from The University Press story has been linked to donations to California Institute of Technology and the University of California, Berkeley in recent years.

Zhang moved to Caltech from Fudan University in Shanghai in 1997, studying engineering and applied science, and later earned a Master's in Statistics and a PhD in Computer Science from UC Berkeley.

“That training and education was the cornerstone of my life,” Zhang said in a 2024 announcement of Professor Berkeley's donation.

While studying at Berkeley, Chang lives with another mathematics, and according to some people who know them, there was a statistic named Pen Zao. After completing his PhD, Chang joined two sigmas. Zhao helped build Citadel Securities and was appointed CEO in 2017.

Two years of turbulent Sigma

As is common in Quant Investment Firms, the two Sigmas have historically maintained a low profile.

However, the past two years are not characteristically highlighted by the longstanding Cold War between founders John Overdeck and David Siegel, which exploded into a public perspective in 2023. (Sigma denied any fraudulent behavior).

Roags from a series of organizations also occurred as well as senior leadership departures and layoffs.

Zhang's technique unit has not been affected by leadership innovations. Ken Baron resigned in 2024, and Kang Hwang left for the Cubist systematic strategy last fall. People said Chang went to Sabbatical last year and left in late 2024.

He has earned tens of millions of compensation over the years, according to those familiar with the issue. QUANT FUND startups are highly valued to get off the ground – they can cost millions of people the data and technology they need, and it can take years to generate revenue.

Source link