Quantitative Finance · Data Analytics · Python
Global Business student at Sungkyunkwan University (SKKU) · Exchange @ Indiana University Kelley School of Business
Targeting quantitative research and buy-side roles in Singapore
Three independent projects covering complementary dimensions of quantitative investment analysis:
| Project | Focus | Key Methods |
|---|---|---|
| Factor Investing Model | US equities · alpha generation | Momentum ensemble, min-variance optimisation, walk-forward backtesting, 35+ unit tests |
| Corporate Valuation & Bankruptcy Risk | Korean market · downside risk | Altman Z-Score, Random Forest (AUC 0.971), DART OpenAPI, Streamlit dashboard |
| SG-REITs Analysis | Singapore REITs · income & valuation | DCF / CAPM WACC, Monte Carlo simulation, strategy backtesting, GitHub Actions automation |
The three projects span three markets (US · KR · SG) and three strategy dimensions (alpha · risk · income), reflecting a systematic approach to investment research across geographies and asset classes.
Languages: Python · SQL
Libraries: pandas · numpy · scipy · scikit-learn · yfinance · Streamlit · Plotly · matplotlib
Finance: Factor models · DCF valuation · Monte Carlo simulation · Portfolio optimisation · Altman Z-Score · CAPM
Tools: GitHub Actions · DART OpenAPI · pytest · Jupyter
- Sungkyunkwan University (SKKU) — B.B.A. Global Business (expected 2027)
- Indiana University Kelley School of Business — Exchange student · I-Core programme
- BDA (Big Data Analytics Society) — Python & data analysis
- CFA Level I — Candidate (Nov 2026)
- Certifications in progress: ADsP · SQLD · 투운사
- WorldQuant University Applied Data Science Lab (8-project curriculum)
- MIT OCW 18.06SC Linear Algebra · 18.05 Probability & Statistics
- CS50W (Harvard/edX) — Web Programming with Python and JavaScript
Open to quantitative research, data analytics, and buy-side roles in Singapore and Hong Kong.