 |
| DESCRIPTION | This is the compulsory one-year development course for FTEC students. The course provides academic and professional advising to students on the development of professional ethics, social awareness, responsibilities, and communication skills. An intended learning outcome is to develop a holistic, interdisciplinary, and evidence-based understanding of the issues in FinTech in the financial markets. Grading Type: Pass/ Fail |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6409) | We 05:00PM - 05:50PM | Rm 101, E1 | BAI, Ruoqi CAI, Ning CAO, Yiyue LI, Siguang QIU, Shi SUN, Shuo WANG, Junxuan WANG, Xuechao YUAN, Zixuan ZHANG, Chao ZHANG, Guang ZHANG, Leifu ZHANG, Liang ZHANG, Yi ZHU, Zimu | 60 | 56 | 4 | 0 |
| PRE-REQUISITE | UFUG 2103 |
|---|---|
| CROSS CAMPUS COURSE EQUIVALENCE | MATH 2421 |
| DESCRIPTION | Sample spaces, conditional probability, random variables, independence, discrete and continuous distributions, expectation, correlation, moment generating function, distributions of function of random variables, law of large numbers and limit theorems. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6404) | MoWe 09:00AM - 10:20AM | Rm 222, W1 | NI, Bingye WANG, Xiaoyu | 40 | 5 | 35 | 0 | |
| T01 (6405) | Mo 06:00PM - 06:50PM | Rm 201, W2 | NI, Bingye WANG, Xiaoyu | 40 | 5 | 35 | 0 |
| PRE-REQUISITE | DSAA 2011 |
|---|---|
| DESCRIPTION | Students will learn how to develop Python code for cleaning and preparing data for analysis. This includes handling missing values, formatting, normalizing, and binning data. They will also learn to perform exploratory data analysis and apply analytical techniques to real-world datasets using libraries such as Pandas, Numpy, and Scipy. The course will also cover manipulating data using data frames, summarizing data, understanding data distribution, performing correlation analysis, and creating data pipelines. Additionally, students will learn how to build and evaluate regression models with the scikit-learn library for machine learning and how to use these for prediction and decision-making. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6408) | TBA | TBA | TBA | 40 | 0 | 40 | 0 |
| PRE-REQUISITE | DSAA 2011 |
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| DESCRIPTION | Financial Data Analysis explores state-of-the-art approaches for extracting insights and building AI-driven intelligent systems using diverse financial data sources. Students will engage in hands-on projects that involve analyzing and modeling multimodal financial data, including time-series (e.g., stock prices), textual data (e.g., news and social media), graph data (e.g., supply chain), tabular data (e.g., company fundamentals), and alternative data (satellite images). Drawing on tools from artificial intelligence and statistical modeling, students will learn to design and evaluate data-driven solutions to real-world financial tasks such as market prediction, sentiment analysis, and risk estimation. Through guided projects and open-ended problem-solving, students will gain experience in end-to-end data workflows, from acquisition and preprocessing to modeling and evaluation. The course emphasizes implementation and experimentation, preparing students for future research and industry roles in FinTech. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6406) | We 09:00AM - 11:50AM | Rm 223, W1 | SUN, Shuo ZHAO, Mingxuan | 30 | 18 | 12 | 0 | |
| T01 (6407) | Fr 06:00PM - 06:50PM | Rm 201, W2 | SUN, Shuo ZHAO, Mingxuan | 30 | 18 | 12 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course will survey modern financial technology, through the lens of statistics, which is the science of the analysis of data. Students will learn how statistical methodology, in conjunction with advances in technology, is used to efficiently acquire, utilize and interpret data, as it relates to innovations in the financial services sector. This course will develop skillsets for Big Data analytics and Predictive modelling, for better understanding of the financial markets. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6077) | We 09:00AM - 11:50AM | Rm 122, E1 | XU, Yuanjian ZHANG, Guang | 40 | 22 | 18 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | The objective of this course is to provide students with an extensive exposure to important research in financial technology and a rigorous training in related research methodologies. Main topics include cryptocurrencies, blockchain, P2P lending, crowdfunding, robo-advisors, regulatory technology (RegTech), and insurance technology (InsurTech). This course also enables students to gain an appreciation for how research in financial technologies improves traditional financial services and overcomes various difficulties inherent in the current financial system. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6079) | We 01:30PM - 04:20PM | Rm 102, E4 | CAI, Ning XUN, Zixiong | 60 | 31 | 29 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course introduces the main issues in corporate finance, identifies principal theoretical tools and empirical approaches, and fosters thinking about current research questions. The theoretical part includes classic theories such as Modigliani‐Miller theorem, Coase theorem, and Fisher separation theorem, with a focus on financing decisions of firms, corporate governance, and their implications. The empirical part reviews econometric methods commonly used in corporate finance research and covers selected topics. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6080) | We 06:00PM - 08:50PM | Rm 201, W2 | WANG, Xiang ZHANG, Leifu | 40 | 17 | 23 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course addresses issues in both theoretical development and empirical studies of asset pricing. The theoretical part covers portfolio theory, arbitrage pricing theory with large numbers of assets, the intertemporal asset pricing model and the production-based asset pricing model. Topics related to derivative pricing are also covered. The empirical part covers asset return predictability, volatility-return relationship, asset pricing testing methodology, popular factor models used by practitioners and empirical findings in derivative markets. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6081) | Fr 01:30PM - 04:20PM | Rm 122, E1 | PENG, Dexin WANG, Junxuan | 40 | 24 | 16 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course provides an introduction to textual analysis in social science research. It covers text mining models and related statistical tools, including Dictionary Method, SVD, Word2Vec, WEAT, Probabilistic Modeling, Regression Modeling, and Large Language Models in modern social science research. Applications related to the financial market are emphasized, including macro-finance, empirical asset pricing, empirical corporate finance, and ESG. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6082) | Mo 06:00PM - 08:50PM | Rm 150, E1 | PENG, Meishu ZHANG, Yi | 30 | 22 | 8 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers basic pricing theory of financial derivatives and risk hedging of exotic options. The course starts with the fundamental theorem of asset pricing and risk neutral valuation principle. The renowned Black-Scholes pricing theory and martingale pricing theory are introduced. Advanced topics include exchange options, quanto options, implied volatility and VIX. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6083) | 23-JAN-2026 - 31-JAN-2026 Th 06:00PM - 08:50PM | Rm 205, C7 Library | HAN, Bingyan ZHANG, Yifan | 20 | 19 | 1 | 0 | |
| 01-FEB-2026 - 07-FEB-2026 Th 06:00PM - 08:50PM | Rm 101, E1 | HAN, Bingyan ZHANG, Yifan | ||||||
| 08-FEB-2026 - 11-MAY-2026 Th 06:00PM - 08:50PM | Rm 101, E4 | HAN, Bingyan ZHANG, Yifan |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course covers Monte Carol simulation methods from the perspectives of derivatives pricing, credit risk modeling and trading strategies. The first topic starts with various sampling methods for generating random variables, like the basic inverse transform method and acceptance-rejection method. Special emphasis is placed on simulation of normal distributions. Next, we consider pricing financial derivatives via simulation. The dynamic price processes include the Geometric Brownian motion and jump diffusion models. Various variance reduction techniques, like the antithetic variate, control variate, conditioning and stratified sampling are considered. The solution of the optimal stopping model of an American option via the Longstaff-Schwartz regression method is discussed. We also consider rare event simulation via various importance sampling methods, like the mean drift method and cross entropy method. Applications in risk measures calculation in credit risk models, like the Gaussian copula models, are considered. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6084) | Mo 01:30PM - 04:20PM | Rm 101, W2 | CHENG, Ziteng LI, Wei | 20 | 5 | 15 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course introduces various novel types of financial instruments enabled by blockchains, collectively known as Decentralized Finance (DeFi). Students will delve into key DeFi elements, gaining both theoretical knowledge and practical skills to effectively navigate and engage with DeFi platforms and protocols. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6085) | We 06:00PM - 08:50PM | Rm 201, E4 | SU, Hongxu WANG, Xuechao | 20 | 16 | 4 | 0 |
| VECTOR | [0-1-0:0] |
|---|---|
| DESCRIPTION | Advanced seminar series presented by guest speakers and faculty members on selected topics in Financial Technology. This course is offered every regular term. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| T01 (6308) | Fr 10:30AM - 11:50AM | Rm 102, W4 | LI, Siguang | 70 | 47 | 23 | 0 |
| VECTOR | [1-0-0:1] |
|---|---|
| DESCRIPTION | Independent study in a designated subject under direct guidance of a faculty member to provide students the advanced knowledge and research skill sets on a topic of Financial Technology. Required readings, tutorial discussions, and submission of report(s) will be used for assessment. The course may be repeated for credit if different topics are studied. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6769) | TBA | TBA | TBA | 20 | 0 | 20 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course offers an in-depth analysis of game theory and its advanced applications in fin-tech related fields. First, it covers theoretical foundations, including extensive form game, strategic form game, Nash Equilibrium and its existence, backward induction, games of incomplete information, sub-game perfection, Bayesian Nash equilibrium, sequential equilibrium. Second, it covers mechanism design, including non-linear pricing, dominant strategy mechanism, Bayesian mechanism design, robust mechanism design, and dynamic mechanism design. The course fosters a deep understanding of game theory, equipping students with advanced tools to engage in independent research, and contribute to the development of novel financial technologies. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6745) | Th 09:00AM - 11:50AM | Rm 201, E4 | HU, Jiaqi LI, Siguang | 20 | 7 | 13 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course offers an in-depth introduction to the fundamental concepts of artificial intelligence (AI) and its advanced applications in finance. It covers key AI techniques, including linear models, tree-based methods, clustering algorithms, neural networks, and reinforcement learning, with an emphasis on both theoretical and practical implementation. Applications in finance, including risk management, asset pricing, fraud detection, and high-frequency trading, will be critically analyzed through real-world datasets. The course fosters a deep understanding of AI methods, enabling students to engage in independent research, and contribute to the development of novel financial technologies. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6746) | Tu 09:00AM - 11:50AM | Rm 202, E4 | CHEN, Zhanghao ZHANG, Chao | 20 | 17 | 3 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course provides an overview of methods used in the rapidly evolving field of robo-advising, focusing on customized portfolio optimization. It aims to introduce the theory of portfolio management and popular techniques in automated trading. The course also explores the nascent field of understanding client risk preference in robo-advising. These techniques combine to offer the basis for developing a robo-advisory system. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6086) | Fr 01:30PM - 04:20PM | Rm 201, E4 | CHENG, Ziteng YAN, He | 20 | 8 | 12 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Graph data are pervasive in finance and business management. The course will be organized in three parts. Part I introduces graph data types and representations, including typical simple/weighted and directed/undirected graphs; bipartite, heterogeneous and knowledge graphs; temporal/dynamic graphs; hypergraphs together with node/edge attributes and common storage/processing patterns. Part II introduces graph data modeling methods from shallow embeddings to modern graph neural networks (message passing mechanism, heterogeneous/temporal architectures, contrastive pretraining) and LLM-based approaches (graph-aware prompting, graph-based RAG, graph foundation models). Part III examines graph data based applications in finance and business management, including anomaly and fraud detection, stock market prediction, and recommendation systems, with emphasis on reproducible, practice-oriented implementation. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6087) | Tu 06:00PM - 08:50PM | Rm 201, E4 | HUA, Fengrui ZHANG, Liang | 20 | 10 | 10 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | The overarching goal of this course is to equip students with a working knowledge of important econometric techniques necessary to understand and interpret financial data in a broad sense. We will investigate topics such as market efficiency, event studies, factor models, return anomalies, bond markets, among others. Apart from model-based approaches, we will also partially cover state-of-the-art design-based approaches such as synthetic control, regression discontinuity, matrix completion, double machine learning and so on. This course will involve both lecture and paper discussion. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6089) | Tu 01:30PM - 04:20PM | Rm 103, E1 | LYU, Aoqing QIU, Shi | 20 | 8 | 12 | 0 |
| DESCRIPTION | Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6018) | TBA | No room required | TBA | 40 | 18 | 22 | 0 |
| DESCRIPTION | Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
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| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6021) | TBA | No room required | TBA | 40 | 9 | 31 | 0 |