Finance · Quantitative Analytics · Python · Risk Management
I have a background in finance with experience in quantitative research, financial analysis, and data analytics. My previous work includes volatility modeling, multi-factor investment strategies, investor sentiment analysis, and financial research using Python and Stata. I am particularly interested in applying quantitative and analytical methods to investment, risk management, and financial decision-making.
My career goal is to develop expertise at the intersection of finance, quantitative analysis, and risk management. I hope to apply data-driven methods to investment analysis, financial risk assessment, and strategic decision-making.
My previous experience includes quantitative research and investment analysis, where I worked with Python and Stata on volatility modeling, market sentiment analysis, multi-factor strategies, and financial risk assessment.
Modeled CSI 300 volatility using GARCH and EGARCH in Python, incorporating Monte Carlo simulations, AIC/BIC evaluation, backtesting, and rolling volatility forecasts.
Developed a quantitative stock-selection strategy combining growth and momentum signals in Python, with monthly rebalancing and backtesting to evaluate investment performance.
Conducted empirical research on the relationship between top-management-team education and stock liquidity using fixed-effects regressions, robustness tests, and panel data analysis.