Core Courses

MFTZ01 Introduction to Python Programming for Finance (3 credits)

The course aims to help students master Python programming tools and apply them to financial data processing. The core content overs four major modules: basic Python syntax, financial data collection and cleaning, data visualization, and financial case studies. The course adopts a combination of programming syntax instruction, programming labs, and case-based exercises, with hands-on tasks accompanying each chapter to ensure that students practice while learning. Upon completion of this course, students will be able to independently collect, organize, process, and visualize financial data, thereby establishing a solid programming foundation for subsequent advanced courses such as machine learning and quantitative analysis.

MFTZ02 Machine Learning for Fintech (3 credits)

This course introduces the fundamental theories of machine learning and their practical applications in finance. It covers core supervised and unsupervised learning methods for regression, classification, and clustering, etc. It also teaches how to apply these techniques to complex financial data analysis, including investment analysis, risk management, credit scoring, fraud detection, and market forecasting. Combining theoretical analysis, hands-on programming labs, and case studies, the course guides students through the complete pipeline of machine learning projects in FinTech. Through real-world case studies and hands-on projects, students will develop a deep understanding of machine learning methods and master the key techniques and solutions for applying them to the financial industry.

MFTZ03 Blockchain for Finance (3 credits)

The course aims to explore the applications of blockchain technology in the financial sector, guiding students to systematically understand the fundamental principles of blockchain and its applications in financial systems. The course content covers the foundations of cryptography, the core principles of blockchain, smart contracts, and the operational mechanisms of decentralized finance (DeFi), and introduces practical cases in areas such as banking operations, payment and settlement, asset management, and digital currencies. Upon completion of this course, students will be able to master the underlying logic of blockchain, evaluate its application value and potential risks in scenarios such as digital assets and payment settlement, and through case analysis, develop a forward-looking vision for insight into the future trends of financial technology and for assisting business decision-making.

MFTZ04 Financial Data Analysis and Econometrics (3 credits)

This course aims to equip students with the fundamental theories, methods, and statistical analysis tools of modern financial econometrics to address data modeling and decision-making problems in financial practice. The core content of the course covers formulating empirical research questions, collecting and organizing data from professional financial databases, building econometric models, interpreting analytical results, and effectively presenting research findings. The teaching approach combines theoretical lectures with hands-on practice on empirical cases. Through practical exercises that apply statistical software to real financial data, students will strengthen their ability to translate financial theory into empirical analysis. Upon completion of this course, students will be able to independently complete the full empirical analysis process, from problem formulation and data processing to model construction and result interpretation; write properly structured empirical research reports; and support business decisions with rigorous quantitative analysis.

MFTZ05 FinTech Regulations and Compliance (3 credits)

This course is designed to help students understand the regulatory framework in the FinTech sector and identify and analyze typical compliance issues. The core content covers the relevant legal frameworks, regulatory requirements, and compliance strategies within the FinTech industry. Specific topics include an analysis of the global and regional FinTech regulatory landscape, an in-depth examination of the major regulatory challenges faced by FinTech companies, and a discussion of key issues such as data privacy and protection, anti-money laundering (AML), counter-terrorist financing (CTF), and data governance and ethical considerations in the application of artificial intelligence. The teaching approach combines lectures with case studies to actively engage students in discussions on relevant compliance issues. Upon completion of this course, students will understand how to build and maintain compliance systems in the rapidly evolving FinTech industry and will gain practical skills for navigating complex regulatory environments through case study analysis.

MFTZ06 Advanced Corporate Finance (3 credits)

This course aims to provide students with a systematic framework for understanding and analyzing key issues in corporate finance. It develops a comprehensive understanding of corporate financing, investment, valuation, and working capital management, with particular emphasis on the financial decisions that shape corporate value. The course covers five core areas: capital structure and financing decisions, corporate valuation and investment analysis, financial forecasting, short- and long-term financial planning, and corporate risk management. A combination of theoretical lectures, case studies, and practical applications is adopted to help students connect corporate finance theories with real-world corporate decision-making and develop their analytical and decision-making skills. Upon completion of the course, students will have a solid understanding of the fundamental principles, concepts, and analytical methods of modern corporate finance. They will be able to apply corporate finance theories and financial analysis tools to evaluate and address practical issues relating to corporate financing, investment, valuation, and working capital management. The course also provides a strong theoretical and practical foundation for students pursuing careers in corporate finance, investment analysis, financial management, risk management, and related areas.

MFTZ07 Investment Analysis (3 credits)

This course introduces how fintech is transforming portfolio management, helping students master technology-empowered approaches to investment decision-making and wealth management. The core content of the course comprises four major modules: the mutual fund industry and Robo-advisors; the insurance industry and digital technology platforms; the investment banking industry and fintech giants; and the hedge fund industry and quantitative investment strategies. In addition, the course also covers the role of central banks in investment analysis, encompassing both conventional and unconventional monetary policy instruments. The teaching approach combines theoretical lectures with case studies from fintech companies. Each industry module is accompanied by both theoretical instruction and practical case applications to ensure that students acquire the relevant knowledge and methodologies. Upon completion of the course, students will acquire skills in asset allocation, risk management, and performance evaluation, and ultimately gain practical competencies for effectively managing investment portfolios in a rapidly evolving market environment.

MFTZ08 Cybersecurity and Privacy for Fintech (3 credits)

This course introduces cybersecurity and privacy in fintech, with a focus on the protection of financial systems, data, and digital services. Core topics include security architecture, data protection, access control, and common cyber threats. Additionally, the course also covers incident response, privacy protection, regulatory compliance, and risk management. The course explores real-world fintech security problems through lectures, case studies, and practical projects. By the end of the course, students will be able to identify and assess security and privacy risks in fintech settings. They will also be able to design appropriate safeguards and develop incident response plans.

Elective Courses

MFTE01 Quantitative Trading (3 credits)

This course aims to help zero-background students establish a systematic entry path into quantitative trading. The core content covers backtesting based on historical data, risk management, market microstructure, and trading platform operations. The course combines laboratory simulation, guided case studies, and step-by-step exercises, emphasizing a practice-oriented approach and teamwork. By the end of this course, students will be able to construct their own trading strategies, organize a quantitative trading team, develop a clear understanding of quantitative methods through collaboration and competition, and ensure compliance with relevant laws and professional ethics in practice.

MFTE02 Financial Technology Entrepreneurship (3 credits)

This course focuses on FinTech entrepreneurship and innovation management, systematically cultivating students’ core competencies in identifying market opportunities, designing innovative business models, and managing the growth of new ventures. The curriculum is structured around five major thematic modules: entrepreneurial opportunity identification, business model design, venture financing strategies, corporate governance mechanisms, and new venture growth management. The pedagogy integrates lectures, case studies, and project-based practice. Students will work in teams to complete a business plan for market entry strategies, simulating the core entrepreneurial process from opportunity identification to solution design. Upon completion of this course, students will acquire the fundamental frameworks and methodologies of FinTech entrepreneurship, laying a cognitive foundation for subsequent entrepreneurial practice or corporate intrapreneurship.

MFTE03 Special Topic I (Finance) (3 credits)

This course aims to help students understand how fintech is reshaping the development models and operational logic of the financial industry, driven by digital transformation and technological innovation. The core content of the course covers the technologies and applications of electronic payment systems, the application and regulatory challenges of digital currencies, and the use of artificial intelligence in financial decision-making and risk management. The teaching approach combines case analysis, academic literature review, and group discussions to facilitate the integration of theory and practice. Upon completion of this course, students will be able to apply the knowledge acquired to analyze the development trends and application value of specific fintech business sectors.

MFTE04 Financial Derivatives (3 credits)

This course aims to provide students with a solid understanding of the fundamental concepts, mechanisms, and practical applications of financial derivatives. The course covers major derivative instruments, including futures, options, forwards, and swaps, with particular emphasis on their use in risk management, investment, speculation, and arbitrage, as well as the management of price, interest rate, and foreign exchange risks. Teaching combines case analysis, class discussion, and practical exercises, using real-world market examples to develop students’ understanding of derivative pricing, trading mechanisms, and risk–return characteristics. Upon completion of the course, students will be able to analyse the characteristics and appropriate uses of common derivative instruments, select suitable instruments and strategies under different market conditions and practical circumstances, and apply relevant knowledge to basic investment and risk management problems. The course also provides a foundation for further study in financial risk management, investment analysis, quantitative trading, and related areas.

MFTE05 Database Management Systems (3 credits)

This course focuses on the fundamental theories and practical applications of database management. Topics include the database development process, an introduction to database structures, data modeling, the design and implementation of relational and non-relational databases, and the use of the Structured Query Language (SQL). The course also explores cutting-edge topics such as database security, database design for big data applications, and AI-assisted database design. The course employs a combination of lectures, in-class programming exercises, and hands-on projects to help students master practical skills in database design and management, enabling them to independently design data storage solutions for financial business scenarios.

MFTE06 Special Topic II (Technology) (3 credits)

This course aims to equip students with foundational knowledge and practical skills for applying Natural Language Processing (NLP) techniques to financial text analysis. Its core content covers fundamental NLP concepts and techniques, along with their applications to financial texts such as corporate disclosures, annual reports, government policy documents, and social media content. Key topics include text preprocessing, sentiment analysis, information extraction, text summarization, risk identification, and financial forecasting. The course also introduces recent developments in large language models, as well as their potential applications, limitations, and associated ethical issues in the financial domain. The course combines lectures, practical exercises, and case studies to guide students in applying NLP techniques to financial text analysis tasks and in interpreting and evaluating the resulting outputs. Upon completion of this course, students will be able to use NLP techniques to process and analyze various types of financial texts, undertake practical tasks such as financial sentiment analysis, risk identification, and forecasting, and critically evaluate both the insights generated through financial text analysis and the reliability, limitations, and ethical implications of using large language models in financial applications.

MFTE07 Financial Risk Management (3 credits)

This course aims to help students develop a systematic framework of knowledge in financial risk management. Throughout the course, students can understand the mechanisms underlying the formation of various risks and master the basic methods of risk identification and assessment, as well as relevant regulatory requirements. The core content of the course includes the identification and assessment of market, credit, liquidity, and operational risks; the application of risk quantification and analytical tools; and regulatory frameworks such as the Basel Accords. The teaching approach combines theoretical lectures, case studies, data applications, and thematic seminars. The teaching methodology guides students in analyzing risk events and management strategies in real‑world financial scenarios, thereby fostering their risk analysis and decision‑making capabilities. Upon completion of this course, students will be able to identify various types of financial risks, apply quantitative tools for analysis and assessment, and design appropriate risk management solutions. This will lay a solid foundation for their future careers in risk management within banking, securities, insurance, asset management, fintech, and related fields.

MFTE08 Financial Accounting and Reports (3 credits)

This course aims to equip students with the core logic of financial accounting and financial analytical tools, with a focus on cultivating capabilities in financial interpretation and decision support within fintech scenarios. Core course content covers fundamental concepts and frameworks of financial accounting, structure and interpretation of financial reports, calculation and evaluation of key financial ratios, correlation analysis between accounting information and corporate financial decision-making as well as risk assessment, together with case studies and practical applications tailored to fintech business models. The pedagogy combines theoretical lectures with scenario-based case discussions. Each chapter is supplemented with financial data analysis exercises and real-world cases, enabling them to translate theoretical accounting knowledge into practical analytical competencies. Upon completion of this course, students will be able to interpret and evaluate corporate financial conditions, and deploy financial information to support investment analysis and credit risk assessment.

MFTE09 Financial Markets and Institutions (3 credits)

This course aims to help students build a practical analytical framework for understanding the logic of financial markets, and to develop the core ability to translate macroeconomic financial concepts into effective market analysis. The course covers the functions and operations of financial institutions, the structural framework of financial markets, and the design logic of various financial products, with particular emphasis on cultivating students' ability to interpret the flow of funds in the market. The teaching approach combines theoretical lectures, real-time case analysis, and macroeconomic data interpretation exercises. By providing an in-depth examination of the logic behind movements in interest rates and exchange rates, the course guides students in understanding the intent behind financial regulatory policies and progressively enables them to build the capacity to anticipate and evaluate market trends. Upon completion of this course, students will be able to independently analyze the decision-making behavior of financial market participants, clearly assess the direction of capital flows, and lay a solid analytical foundation for their future careers in banking, securities, asset management, or financial regulation.

MFTE10 Behavioral Finance (3 credits)

This course aims to help students understand how psychological and cognitive biases influence the financial decisions of investors and financial institutions, and how these biases affect financial markets and asset prices. Key topics include the basic concepts of behavioral finance, the decision-making behavior and psychological biases of investors and financial institutions, market anomalies and asset pricing, as well as applications in investment management and corporate finance. Through lectures, classroom experiments, questionnaire-based activities, and financial market case studies, students will connect behavioral finance theories with real-world financial phenomena. Upon completion of the course, students will be able to identify common behavioral biases in financial decision-making and apply the basic theories and methods of behavioral finance to investment analysis, risk management, and financial decision-making.