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Meet Kaiju: Sylvia Li, Quantitative Researcher Q&A

The company culture at Kaiju is very much about moving things forward and progressing, and I’m excited to have a role in making that happen.

If you don’t know what a “stochastic process” is, you’re not alone. Luckily, Sylvia Li, a quantitative researcher at Kaiju is happy to explain it to you.

To simplify, a stochastic process is a random one, and if you’ve ever been surprised by torrential rain on a day you’d planned a picnic, then you already know what it means to be at the mercy of this chance-based process. It also applies to trading: buying certain stocks can seem like a no-brainer... until life and all its variables intervene.

Sylvia analyzes all of these “ifs” and “buts” for Kaiju Capital Management, which makes her essential to the team. And while no one can predict the future with 100% accuracy, Sylvia’s work drives home the fact that trading doesn’t happen in a vacuum, that there’s always a bit of risk to go with the reward.

As part of our ongoing “Meet Kaiju” series, we caught up with Sylvia to ask her some questions about her life, her interests, and what led her to an area of quantitative analysis that borders on the philosophical... Her answers appear below.

Q. To start out, can you give a little background on where you grew up and what your education was like?

A. I grew up in Zhengzhou City, an urban area in the central part of China. I spent over two decades happily in my hometown. I attended Zhengzhou University, where I studied both pure and applied mathematics, such as quantitative finance. After getting my bachelor's degree, I wanted to experience life outside of China. I saw an opportunity in the United Kingdom, which is known for its graduate-level programs in financial mathematics, so I earned my Master of Science degree in Mathematics and Finance at Imperial College London.

Q. You work as a quantitative researcher for Kaiju. Can you explain what that means and what your job entails?

A. Quantitative research combines mathematics, computing, and finance, and uses them to analyse the market and help traders make decisions based on real-world variables. My job centers on reinforcement learning, teaching a machine to behave in certain contexts related to trading. The learning model can be applied to many scenarios and helps traders determine the optimal time to buy or sell an option.

Q. How did you decide this was something you wanted to do? Did you pick it up in college or were there things about it that interested you when you were younger?

A. I worked on a quantitative research project in college, and I immediately found it really interesting, so I chose a related major for my Master of Science studies. I was fortunate enough to get an internship at Kaiju last year. It allowed me to focus on gaining a deeper understanding of machine learning models, which I enjoy very much. Doing it as my career felt like a very natural next step.

Q. How did you end up working at Kaiju full-time?

A. I discovered Kaiju on the careers platform at Imperial College. Some of the projects being proposed were very creative and advanced -- exactly what I was looking for. During the internship, my supervisors, Aitor (Muguruza, Director of Quantitative Modeling & Data Analytics) and Nicholas (Subryan, Director of AI & Quantitative Research), gave me timely and helpful feedback, and I benefited a lot from their support, which was ongoing.

I appreciate the company culture at Kaiju, and the way Aitor and Nicholas interacted with me is a perfect example of it. Everyone at Kaiju not only excels at his or her own job, but they also pay a lot of attention to everyone’s well-being. This made it easy for me to make the decision to join Kaiju full-time.

Q. Tell us about stochastic processes. What is that and how does it relate to financial markets?

A. Stochastic processes are random. In financial markets, there is no way to use a deterministic model that yields the same result every time you use it. Things are always changing in financial markets. You have to account for real-world factors and a variety of possible outcomes.

We use stochastic processes to account for that unpredictability. Really, life itself is stochastic. You can do the same thing on two different days and get two different results, depending on external factors, which can include anything from the weather to illness. In finance, you can’t know tomorrow’s stock price today.

Q. How do you use your research skills at Kaiju?

A. Before starting a project, we examine the theoretical basics of the problem. This is to ensure that stochastic models are even feasible in the situation at hand. If so, we build a simple model and use simulated data to test its accuracy. After that, we usually adopt more complicated techniques to mature the model, and we run multiple tests to make sure that the model is sound.

Q. What do you think the future holds for Kaiju and your role here?

A. I think that Kaiju will continue to grow into a stronger company with a more varied and innovative team. As part of the research team, I’ll keep up to date on the latest technological findings from academia and industry and explore ways of incorporating them into our existing projects. The company culture at Kaiju is very much about moving things forward and progressing, and I’m excited to have a role in making that happen.

Photo by Ricky Flores

Daniel Bukszpan's reporting and commentary on finance, technology, and politics has been published in Fortune, The Daily Beast, CNBC.com, and other outlets. He lives in Brooklyn, New York.

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