 |
| 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 (6118) | We 01:30PM - 04:20PM | Rm 202, W4 | CHEN, Weizheng YU, Holam ZHANG, Guang | 40 | 27 | 13 | 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 (6119) | Fr 03:00PM - 05:50PM | Rm 147, E1 | CAI, Ning LIN, Hao | 50 | 35 | 15 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | The objective of this course is to provide students with fundamentals of stochastic processes. Main topics cover Poisson processes, renewal theory, discrete-time Markov chains, continuous-time Markov chains, and martingales. The emphasis will be on methodologies, fundamental concepts, and mathematical insights. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6113) | Tu 06:00PM - 08:50PM | Rm 101, W2 | SHI, Jiahui ZUO, Ruiting | 20 | 5 | 15 | 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 (6122) | Tu 01:30PM - 04:20PM | Rm 202, W4 | WANG, Xiang ZHANG, Leifu | 40 | 20 | 20 | 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 (6124) | Th 09:00AM - 11:50AM | Rm 202, W4 | KWOK, YUE KUEN WANG, Jiaqi | 40 | 20 | 20 | 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 (6115) | Mo 06:00PM - 08:50PM | Rm 201, W2 | SHI, Yu ZHANG, Yi | 20 | 20 | 0 | 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 (6116) | Mo 01:30PM - 04:20PM | Rm 201, W1 | HAN, Bingyan JIA, Yusen | 20 | 6 | 14 | 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 (6117) | We 09:00AM - 11:50AM | Rm 202, W2 | WANG, Xuechao YU, Qianyu | 20 | 12 | 8 | 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 (6368) | Fr 09:00AM - 10:20AM | Rm 101, W1 | ZHANG, Chao | 70 | 50 | 20 | 0 |
| VECTOR | [1-3 credit(s)] |
|---|---|
| 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 (6525) | TBA | TBA | TBA | 20 | 0 | 20 | 0 |
| VECTOR | [1 credit] |
|---|---|
| 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 (6544) | TBA | TBA | TBA | 10 | 1 | 9 | 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 (6316) | 06-FEB-2025 - 28-FEB-2025 We 06:00PM - 08:50PM | Rm 233, W1 | LI, Siguang ZHENG, Mingzhe | 20 | 9 | 11 | 0 | |
| 03-MAR-2025 - 12-MAY-2025 We 06:00PM - 08:50PM | Rm 202, W1 | LI, Siguang ZHENG, Mingzhe |
| 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 (6317) | Tu 09:00AM - 11:50AM | Rm 101, W2 | XIAN, Chulin ZHANG, Chao | 20 | 10 | 10 | 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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6126) | TBA | No room required | TBA | 40 | 20 | 20 | 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. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6128) | TBA | No room required | TBA | 40 | 4 | 36 | 0 |