MinKyeom Kim

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.

"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.

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.

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.

I simply prepared thoroughly and never lost confidence in my research and alpha portfolio. Having put in genuine effort over six months, my mindset going in was: show them who you've become. That conviction meant I wasn't nervous at all — if anything, I enjoyed it. I practiced alone in an empty classroom, over and over, imagining the Singapore stage right in front of me. By the day of the finals, the nerves never really came.

I take genuine pride in being the first Korean to win. Honestly, I didn't feel the pressure too heavily in the moment — the whole experience felt new and exciting throughout. The win and the internship opportunity feel like the beginning of my career in the quant industry. As many people know, breaking into this field is extremely difficult. I look forward to learning a great deal and continuing to grow from here.

For me, it's the learning. Philosophically speaking, I think a single individual life, in the grand scheme of things, carries no inherent cosmic weight. Camus asked in The Stranger, "Why not suicide?" — and paradoxically, that question leads me to find meaning in precisely the opposite: the daily act of learning and growing. That might sound rather philosophical — perhaps a bit embarrassing to say out loud — but it's genuinely what drives me.

To be more precise, I'll actually be interning at WorldQuant's Yeouido office in Seoul! The biggest difference: in a competition, you're competing against other beginners; in professional practice, you need to create something that adds genuine value to the firm. In IQC, you only need to clear self-correlation thresholds. In industry, you also have to pass production-correlation checks against the entire pool of other researchers' alphas. You're expected to produce research that brings new, meaningful value to the organization.

I'd borrow Nike's slogan: Just Do It. And alongside that — learn to use AI well, but don't lean on it too heavily. AI won't build a finished product for you from scratch. Where it truly excels is as a learning tool: ask it what you don't understand, use it to organize your thoughts, use it to study. Start moving first. When you hit a wall, use AI to help you get over it. Repeat that process, and you'll look back one day at how far you've come.