Undergraduate Program
Key Learning Approaches and Areas
| Financial Theory Learning | Lecture-based teaching is adopted, focusing on fundamental theories and financial models. Through assignments and case studies, students develop the ability to understand and apply theoretical frameworks in financial analysis. |
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| Applied Finance Courses | Courses utilize statistical methods, econometrics, and programming languages to analyze financial and economic data. Students engage in database access, data extraction, and empirical analysis, enabling them to connect financial theory with real-world market applications and decision-making. |
| Data and Text Analytics | Students apply big data analytics, text mining, statistical tools, and AI-related technologies to financial and textual data. This includes data extraction, analysis, modeling, interpretation, and forecasting, transforming raw data into actionable insights that enhance financial decision quality and process optimization. |
| FinTech Intelligent Tool Development | This course emphasizes hands-on programming and system development. Students cultivate logical thinking, analytical skills, programming ability, communication, and teamwork. With financial expertise and emerging intelligent tools, students collaborate to develop applications such as algorithmic trading systems, demonstrating integrated learning outcomes from their university studies. |
Featured Courses
This course introduces robo-advisory applications, which provide online financial advisory services. After investors complete a risk questionnaire via computer or mobile device, the robo-advisor constructs an optimal portfolio allocation based on key factors such as investment objectives, risk tolerance, and age. The portfolio is continuously rebalanced according to changes in market conditions.
Digital Finance WorkshopThis workshop series covers two important FinTech application domains: arbitrage trading and financial innovation. Through a one-credit micro-course, students learn how FinTech is applied across financial services and investment environments, fostering innovative thinking and entrepreneurial service models.
Financial Data Mining and Machine LearningFinancial data is highly complex and voluminous. This course introduces statistical and artificial intelligence methods to transform data into actionable decision-making information. Students learn data scraping, preprocessing, testing, and analysis, which can be applied to financial marketing, trading, risk management, and fraud prevention.
FinTech App Development ProjectThis course is based on team collaboration to develop financial service applications. Students gain hands-on experience in API integration, automated trading systems, and backend risk monitoring. The course integrates knowledge acquired throughout their studies, strengthens teamwork experience, and fosters innovation and entrepreneurial thinking.