"How do you turn volatility into opportunity? My answer: innovating factors, harnessing AI, and applying market intuition."
I graduated from the University of Chicago with Cum Laude, B.S. in Computational and Applied Mathematics and B.A. in Biology. I am enthusiastic about joining a hedge fund or quantitative trading firm where I can combine my experiences in ML, mathematical modeling, and fundamental research to generate alpha and improve decision-making at scale.
At Shanghai Jinde Asset Management, I built a stacked XGBoost-Logistic Regression model on 900+ companies' liquidity and credit data, increasing the predictive power of downgrade risk by 30% (PR-AUC to 95%). I also engineered a Monte Carlo framework using bond redeeming probabilities to identify arbitrage signals and undervaluation in the convertible bonds market. This equips me with solid project experiences in machine learning and derivatives pricing.
What differentiates me is my ability to bridge rigorous quantitative research with deep market understanding. During my internship at Morgan Stanley, I directly supported Volkswagen for its $700MM cross-border investment in XPeng. Besides building DCF and public comps to value the companies' intricate synergies, I deeply researched XPeng's past price drivers and established an event-driven framework to predict elevated price dislocations based on EV sector news. Combining quant and fundamental skills, I am thrilled to help you uncover innovative alpha and trading strategies.
I am more than glad to meet you to learn about how you successfully navigated through the quant finance landscape, and I am open to any relevant opportunities!
