Feiyang He

Feiyang has acquired strong analytical skills using machine learning models, time series models, and portfolio theory to solve financial problems. Feiyang also has exhibited excellent oral and written communication skills when working independently and collaboratively in teams.

Experience

Project: MFI Insurance Summer Project, UofT, Jul. 2023 - Sep. 2023

• Implemented cvxpy package in Python to find the minimum variance portfolio within the targeted range of return.
• Performed value at risk analysis to justify the minimum variance portfolio in comparison to other portfolios within the targeted range of return.
• Presented the optimal portfolio’s characteristics & the value at risk analysis to program instructors.

Project: Machine Learning Project
University of California, Santa Barbara, Jan. 2023 - Apr. 2023

• Collected monthly data of 8 macroeconomic variables, such as GDP & CPI, to predict US government 10-year bond monthly yield.
• Implemented Linear Regression, K-Nearest Neighbors, Random Forest, & Elastic Net Regression making prediction in R.
• Applied exploratory data analysis, data splitting, stratified sampling, cross validation, & model tuning to improve model performance.

Mingyi Fund, China (Remote)
Quantitative Researcher, Sep. 2022 - Dec. 2022

• Collected monthly data of 14 macroeconomic variables to predict Chinese government 10-year bond monthly yield trend using Logistic Regression.
• Implemented VAR model, ARIMA model, Random Walk, & Nelson-Siegel Model to predict Chinese government 10- year bond daily yield in R.

Skills

• Technical: Python
• R
• SQL
• AXIS
• Microsoft 365

Professional Certificates & Awards

• Dean’s Honors 2020-2021