Korea’s Donghwa Seo talks the 2023 IQC Global Finals and the WorldQuant BRAIN consultancy program.
Where did you grow up?
I grew up in South Korea and completed my education in Seoul and Daejeon.
What did you study at university?
I studied Industrial Engineering both as an undergraduate and graduate student, and I am currently pursuing a PhD in the Risk Management Lab at KAIST Seoul.
How did you get interested in quantitative finance?
My field of research closely relates to quantitative finance, and I frequently encountered it in academic papers, which naturally piqued my interest.
Which BRAIN competitions have you participated in?
I participated in the International Quant Championship 2023, KAIST Seoul – Alphathon 2023, and the Korea Hackathon 2024, and I just participated in the SuperAlpha Competition 2024 Month 3 for Consultants.
How did it feel to be one of the winners?
Along with my team members, we represented Korea at the Global Finals of the International Quant Championship, 2023 in The Bahamas. And I also achieved strong positions in other BRAIN competitions – like the Alphathon. This was a real confidence boost for me. The experience enhanced my ability to develop Alphas and allowed me to grow my experience as I put my ideas to the test against others.
What’s it like to be part of the BRAIN Consultancy Program?
Developing Alphas constantly reminds me of the areas I need to focus on and continuously improve upon my research frameworks. In those moments, having a community where I can ask questions, interact, and grow is incredibly valuable.
What do you like most about the BRAIN platform?
The platform automates many tasks, provides extensive data, and offers ample simulation opportunities at the initial stages, allowing us to focus solely on the essence of the problems. It’s an ideal starting point for aspiring quants interested in Alpha development.
How do you come up with ideas for new alphas?
I often refer to academic papers and continually explore ways to automate aspects of the Alpha development pipeline.
What is the most challenging part about doing quant finance research?
There is an abundance of information and knowledge available. The real challenge lies in applying this knowledge and linking it to actual sounds research approaches. The hardest part is filtering out ‘good-looking’ Alphas, as the attractive results from in-sample data can be tempting but might lead to overfitting.
How has your experience participating in the WorldQuant BRAIN consultancy program helped shape your career goals?
Working as a BRAIN Consultant has brought me closer to realizing my goals as a quant and was the entry point into the field.
What advice would you give others considering getting involved with the WorldQuant BRAIN consultancy program?
Despite numerous experiments, I’ve found that conventional wisdom often prevails. Applying various methods introduced by BRAIN to find unique ideas and avoid overfitting might feel like running with weights initially. However, paradoxically, this approach can be the fastest way to develop quality Alphas.
*WorldQuant defines Alphas as mathematical models that seek to predict the future price movements of various financial instruments.