Chenxu Zhang

Notebook-based assignments and applied labs connect return and risk measurement, portfolio construction, CAPM, and factor models to real market data. Students interpret their results through executive-summary reports.

Python- and R-integrated learning activities develop statistical reasoning, data analysis, and reproducible workflows. Canvas-ready modules, assignments, and quizzes connect quantitative evidence to decisions.

Updated modules and project-based exercises explore market and economic data, uncertainty, and risk assessment. Programming-supported workflows help connect model outputs to their assumptions and limitations.

Financial reporting, the relationships between financial statements, transaction flows, and internal controls. Case-based exercises use common-size and ratio analysis to evaluate institutional performance.

Course materials and computational exercises cover valuation, hedging, portfolio strategy, financial time series, efficient-frontier analysis, and stress testing with Python and Jupyter.

Instructional materials and applied activities connect capital allocation, risk–return tradeoffs, and performance evaluation with analytical reasoning and clear financial communication.

A staged project structure connects problem framing and literature review to data collection, model design, analysis, and presentation. Analytical reports and reproducible repositories support the communication of findings.