Regan C.H. Yin

About Me

I’m Regan C.H. Yin, an aspiring business analytics professional with a robust cross-functional background in data science, finance, and entrepreneurship.

Currently, I am pursuing my Master of Business Analytics (MBAN) at UBC Sauder School of Business (Class of 2026), awarded the prestigious Accelerated Career Scholarship for outstanding academic and professional achievements.

Previously, I completed a BBA in Information Systems and Finance with a specialization in Business Analytics at HKUST, where I was a recipient of the Tin Ka Ping Scholarship for Innovation and Entrepreneurship. I also attended UIUC Gies College of Business as a semester exchange student, focusing on Advanced Corporate Finance, Portfolio Management, and Derivatives.

Professional Experience

• Summer Analyst @ HKEX (Hong Kong Exchanges & Clearing Ltd.)
  • Designed real-time dashboards for senior executives using live data feeds.
  • Built the team’s first client monitoring system integrated with actionable analytics.
  • Conducted strategic analyses on Mainland A-share and GDR markets.
• Equity Intern @ CCB International Asset Management
  • Applied NLP sentiment analysis on financial news to support investment insights.
  • Evaluated the SPAC market under new regulatory environments.
  • Produced industry outlook reports for the semiconductor sector.
• Co-Founder & CFO @ DecenPay (dpAuth)
  • Spearheaded the creation of a DLT-based authentication ecosystem used in HR and logistics.
  • Secured over HKD$200K in early-stage funding from HKSTP and CityU.
  • Negotiated Federated Learning licensing with universities for privacy-preserving analytics.

Projects & Research

• Insider Behavior Detection in Earnings Calls
  Developed a multi-modal analytics framework combining FinBERT, WhisperX, and yFinance for identifying sentiment anomalies in earnings calls. Applied Granger causality and event study methodology to link executive tone with abnormal stock returns.

• Heart Disease Prediction via Machine Learning
  Built multiple ML models (Logistic Regression, KNN, Naïve Bayes) using the BRFSS 2020 dataset, incorporating cost-sensitive thresholds, achieving high recall for identifying high-risk individuals.

• US 2022 House Election Visualization
  Created a comprehensive time-series dashboard mapping electoral outcomes with polling, demographic, and turnout data. Demonstrated advanced data storytelling using Python and geopandas.

Skills

• Technical: Python, SQL, Tableau, Excel, Bloomberg, Linux, Whisper, FinBERT, HuggingFace, yFinance, GridSearchCV, Scikit-learn, PyAudioAnalysis
• Domain: Financial Markets, Risk Analytics, Predictive Modeling, Healthcare Data, Public Policy
• Soft Skills: Strategic Communication, Cross-cultural Collaboration, Presentation, Leadership, Entrepreneurial Thinking