MinKyeom Kim
🇰🇷South Korea· Ulsan
The first Korean to win the IQC — a 25-year-old industrial-engineering undergrad who beat 80,000 contestants from his dorm room in Ulsan.
Out of 80,000 contestants from 11,000 universities, MinKyeom Kim was the one who walked out of the Marina Bay finals with first place. Korea had never won the WorldQuant International Quant Championship before. His prize: $23,000 and a 2026 internship at WorldQuant's headquarters in Old Greenwich, Connecticut. He is double-majoring in Industrial Engineering and Business Administration at UNIST in Ulsan.
He is not a finance or mathematics major. He approached the IQC the way an engineer approaches any problem: define the objective, design a solution, implement it. His workflow had three stages: forming a hypothesis, building a raw signal, then refining it mathematically. What set him apart was the emphasis on hypothesis formation as a safeguard against overfitting — while many competitors chased in-sample Sharpe ratios, Kim built alphas that could hedge one another and hold up out-of-sample.
At the Singapore finals, he defended his research in front of WorldQuant's senior leadership without nerves. He had practiced alone in an empty classroom, imagining the stage right in front of him. By the day of the finals, the nerves never came. His next chapter is the 2026 WorldQuant internship in the U.S.
Interview
You won IQC 2025, beating 80,000 participants from around the world — from a dormitory room in Ulsan. What was that moment like when you first found out?
Ulsan is a quieter city, a bit removed from the bustle of Korea's major urban centers. My journey started from a simple curiosity about finance and quantitative research — winning was never the primary goal. I just focused on studying and building alphas diligently, day by day. When I heard the announcement in Singapore after the final presentation, I was happy, of course — but more than the result itself, what felt meaningful was the growth and learning I experienced throughout the process.
For readers without a quant background: what exactly is an "alpha," and how does one go about building one from scratch?
"Alpha" refers to excess returns — returns above a benchmark, whether that's the broader market or specific factors. The precise meaning varies by context and industry. In the IQC, alpha referred to the quantitative logic itself underlying a long-short equity strategy. Building alpha requires a technical foundation in coding and mathematics, but financial intuition and interpretive skill are just as important. I think the core competency of a quant researcher lies in bridging those two domains and moving fluidly between them.
Can you walk us through your research workflow during the competition — how you generated, tested, and refined signals on the BRAIN platform?
My workflow had three broad stages: forming a hypothesis, building a raw signal, and then developing it through various mathematical techniques. What set my approach apart was the emphasis I placed on the hypothesis-formation stage as a safeguard against overfitting. Many highly skilled CS-oriented participants treated alpha purely as a numerical optimization problem — many saw their in-sample Sharpe ratios collapse by 90% or more out-of-sample. To guard against that, I paid close attention to the financial validity of each hypothesis, considered the macroeconomic interest rate environment, and built alphas that could hedge one another.
You studied industrial engineering, not finance or mathematics. Did that ever feel like a disadvantage — or did it actually help you think differently?
Personally, I felt that industrial engineering was a remarkably well-suited background for quant research. Industrial engineering is fundamentally about defining problems, designing solutions, and implementing them — and quant research is exactly that, just applied to finance. The problem-solving mindset translated naturally: clarifying the objective and constraints, then designing optimized approaches to meet them.
