Weidi MIN

Weidi MIN

Intern

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location of Weidi MINHong Kong, Hong Kong SAR

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  • Timeline

  • About me

    Student in the Chinese University of Hong Kong

  • Education

    • The George Washington University School of Business

      2023 - 2023
      Exchange Finance 4.0 / 4.0
    • The Chinese University of Hong Kong

      2020 - 2024
      Bachelor of Science Statisitcs 3.5

      Dean's list 2022-2023, Science Faculty

  • Experience

    • Census and Statistics Department, HKSAR

      Jun 2023 - Aug 2023
      Intern

      1. Used Python and PostgreSQL for backend development, storing U.S. SEC's 13F filing data.2. Employed statistical methods like heatmap plotting, regression, and correlation analysis to assess large U.S. funds' strategies, evaluating performance and risk over quarters.3. Conducted fundamental analysis on company financial statements and macroeconomic data to determine their value and potential investment opportunities.

    • 香港中文大学

      Jun 2023 - Aug 2023
      Research Assistant

      1. Employed Python and PostgreSQL for backend development, including the storage of filing data from the U.S. SEC's 13F reports in a database.2. Utilized statistical methods such as heatmap plotting, regression analysis, correlation analysis and machine learning skills to qualitatively and quantitatively analyze the strategies of large U.S. funds, evaluating their performance and risk across different quarters.3. Conducted fundamental analysis on company financial statements and macroeconomic data to determine their value and potential investment opportunities. Show less

    • 汇丰

      Jan 2024 - Jun 2024
      Credit Risk Analyst

      1. Employed statistical methods like KS test, ECDF, and PSI to compare parameter distributions of IRB models, including EAD, LGD and PD during market downturns and normal periods, analyzing eight parameters during economic recessions.2. Performed data cleaning, feature selection, and missing data processing on the dataset. Implemented machine learning models such as logistic regression to predict credit default rates.3. Refactored Python code to enhance program speed using mapping techniques and matrix calculation functions. Also, leveraged Plotly and Dash packages to create a dynamic dashboard for visualizing data insights and facilitating user interaction. Show less

  • Licenses & Certifications