Quants worth following: Renee Yao
"Quants Worth Following" is our latest interview series highlighting thought leaders in the quantitative trading industry actively sharing their knowledge and resources with the community.
In this edition, we spoke with Renee Yao , founder and CIO of Neo Ivy Capital , a quantitative hedge fund focused primarily on investing in statistical arbitrage related equity strategies. Renee started her career at Citadel and Millennium before founding Neo Ivy Capital 10 years ago, in February, 2015.
Renee’s journey began in China, where a childhood visit to her uncle’s workplace ignited her passion for mathematics and computer science. Her uncle, a member of a pioneering Chinese supercomputer research team, introduced her to a massive supercomputer that filled two entire rooms.
After moving to the United States, Renee pursued a Ph.D. in applied math at Columbia University. During her third year, a serendipitous recruiter call introduced her to an opportunity at Citadel. She joined their mid-frequency statistical arbitrage team in 2009, helping to develop the strategy from the ground up.
"If I had joined a large quant shop today like Two Sigma, I might have only seen a small part of the picture. At Citadel, I had the unique chance to witness the end-to-end development of a strategy," Renee shares.
Renee’s career progressed as she joined WorldQuant, a Millennium portfolio manager, where she managed her own book and hired her first team member, a fellow Columbia Ph.D. She later transitioned to HAP Capital, overseeing equity trading. In 2015, WorldQuant’s parent company, Millennium approached her with an offer to seed her own fund, leading to the creation of Neo Ivy Capital.
Renee believes that the value of a Ph.D. is in cultivating the ability to navigate complex problems independently and develop original research ideas. For Renee, this self-sufficiency forms the backbone of innovation.
“The goal of having a Ph.D. is not necessarily that you're going to use what you wrote in your thesis in the financial markets. It's more to say, do you have the ability to conduct independent research without someone holding your hand, or pointing out the direction for you?” Renee reflects.
Renee’s academic background and orientation towards independent thinking have been instrumental in shaping Neo Ivy Capital’s research-driven approach. With a foundation in independent research, the firm was an early adopter of AI in finance, initiating AI-focused research in 2014.
"Machine learning has existed for over 70 years. What differentiates modern AI from traditional machine learning is its ability to work with open-ended, causative questions," Renee explains.
Renee explains the evolution of machine learning and its transition to modern AI by comparing two classic examples: IBM’s Deep Blue and Google’s AlphaGo . Deep Blue, a traditional machine learning algorithm developed in the 1990s, famously defeated a world chess champion by analyzing every possible move in a finite game with well-defined rules. However, such brute-force computation is impractical in real-world scenarios, like the financial markets, where outcomes are open-ended and lack predefined rules.
Modern AI systems like AlphaGo excel at handling open-ended questions due to advancements in reinforcement learning, deep neural networks, and probabilistic reasoning. Unlike traditional algorithms that rely on predefined rules and brute-force computation, modern AI learns through trial and error, adapts to novel situations, and evaluates outcomes probabilistically rather than exhaustively.
Renee emphasizes that AI is neither a silver bullet nor pure hype—it is a powerful tool grounded in mathematics and probability. Its effectiveness depends on how intelligently it is implemented.
Renee describes the evolution of the industry through three distinct generations. First-generation firms, such as the Salomon Brothers and early Renaissance, pioneered the use of quantitative methods to identify market opportunities. Second-generation firms, including Citadel and Two Sigma, expanded on this by scaling operations and assembling large teams of researchers, enabling them to tackle increasingly complex strategies.
Renee sees Neo Ivy as belonging to the third-generation of firms. Rather than emphasizing headcount, Neo Ivy uses machine learning and AI to help human researchers in their discovery of trading strategies, allowing the firm to remain agile and innovative.
Renee candidly addresses the distinct challenges of running a business versus managing a portfolio.
"Fundraising is one of the most difficult jobs in the world. Early on, I thought good performance would naturally attract investors, but I quickly learned that communicating our competitive edge in simple, compelling terms is just as important."
Hiring is another key focus for Neo Ivy Capital. The firm seeks candidates with robust technical expertise in math, computer science, or physics, coupled with soft skills like teamwork and communication.
"Smart people want to work on something meaningful. Building a culture where talented individuals feel motivated and valued is key to staying competitive," she emphasizes.
As Neo Ivy Capital continues to grow, Renee remains committed to fostering a culture of innovation and inclusivity. She is a strong advocate for increasing female representation in finance, and shares with enthusiasm that, “We need to promote more women into our space to make it a fairer game."
Renee emphasizes the importance of foundational skills that transcend specific roles. She highlights the need for independent research, critical thinking, and a deep understanding of mathematics and computer science as cornerstones for success in quantitative finance. These foundational skills position to be adaptable as the industry evolves.
For founders, Renee hones in on the importance of resilience in the face of challenges. "Starting your own firm comes with enormous risks," she acknowledges, "but if you stay consistent and persistent, the rewards will follow."
Check out the full interview on our YouTube here .