Postgraduate Courses
MFIT
Financial Technology
- MFIT 5001AI for FinTech[2-0-0:2]BackgroundLinear Algebra, Multivariable Calculus, Probability and StatisticsDescriptionThis course covers the basic theory of artificial intelligence and machine learning, and their applications to FinTech. Topics include natural language understanding and sentiment analysis using various deep learning architectures. The course also covers basic natural language processing methods for applications such as event and anomaly detection, fraud and fake news detection. The course will also relate sentiment and affect analysis to stock market trading, market monitoring, and to compliance and regulatory-related adverse events.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Describe and explain basic concepts of Machine Learning and Natural Language Processing (NLP).
- 2.Implement Machine Learning and NLP algorithm in the FinTech domain.
- 3.Analyze different financial related problems and their possible solutions.
- 4.Explain how and when an NLP solution (e.g. Sentiment Analysis) can be used in the FinTech industry.
- MFIT 5002Blockchain[2-0-0:2]Exclusion(s)MSBD 5017DescriptionThis course introduces basic concepts and technologies of blockchain from engineering perspectives, such as Bitcoin architecture, consensus protocol of Bitcoin, proof of work, Ethereum, Hyperledger and smart contracts, as well as the blockchain applications. The course also covers the limitations and possible improvements of the blockchain system.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain Bitcoin concepts.
- 2.Define consensus protocols of Bitcoins.
- 3.Compose smart contracts on Ethereum.
- 4.Develop decentralized applications (DAPP).
- MFIT 5003Data Analysis[2-0-0:2]DescriptionThis course covers the basic and advanced statistical approaches to data analysis and shows how to use these techniques to analyze a financial data with a statistical package, such as Python and R. The key topics are reading and describing data, categorical data, time series data, correlation, nonparametric comparisons, ANOVA, multiple regression, general linear models and quantile regression models.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Explain the core ideas on financial data analysis by using statistical models.
- 2.Apply rigorous, analytic, highly numerate approaches to analyze and solve problems in daily life and at work with Python and R, especially in finance.
- 3.Carry out objective analysis and prediction of quantitative information in finance with independent judgment.
- 4.Communicate effectively about statistical results obtained from R and Python to both lay and expert audiences utilizing appropriate information and suitable technology.
- MFIT 5004Financial Data Mining[2-0-0:2]Exclusion(s)CSIT 5210, MSBD 5002DescriptionIn this course, students will first learn basic concepts and techniques about data mining, including data preprocessing, data cleaning, clustering, classification and outlier detection. Then, students will learn how to apply these techniques to financial data, such as sentiment analysis and social networking mining.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Define data preprocessing.
- 2.Explain clustering, classification and frequent pattern mining.
- 3.Describe outlier and anomaly detection.
- 4.Apply sentiment analysis and community detection.
- MFIT 5005Foundations of FinTech[2-0-0:2]DescriptionThis course aims to provide a foundational introduction to financial technologies. More specifically, this course will cover various important financial technologies and innovations, including investment and financing technologies such as P2P lending and crowdfunding, payment technologies such as mobile payments, wealth management technologies such as robo-advisors, blockchain technologies such as cryptocurrencies, and other technologies such as InsurTech and RegTech.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Describe the principles of P2P lending and crowdfunding.
- 2.Explain the principles of wealth management technologies.
- 3.Define the foundational theories of blockchain technologies.
- 4.Identify the functions of InsurTech and RegTech.
- 5.Analyze advantages and limitations of financial technologies qualitatively and/or quantitatively.
- 6.Apply finance or economics to the analysis financial technologies.
- MFIT 5006Mathematical Foundation of FinTech[2-0-0:2]DescriptionThis course teaches mathematical and quantitative skills as a technical preparation for development of financial technology. The topics covered in this include multivariate calculus, linear algebra, optimization, numerical computation, elementary number theory for cryptography, probability, statistics and other topics, with applications to finance.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Recognize a wide variety of statistical models.
- 2.Identify a wide variety of optimization algorithms.
- 3.Develop skills on evaluation of models and optimization algorithms.
- 4.Differentiate strength and weakness for difference models and algorithms.
- 5.Implement at least one programming language, e.g., Python, R or MATLAB.
- MFIT 5007Technology and Analytics of Alternative Finance[3-0-0:3]DescriptionThis course aims to provide an introduction to technology and analytics related to alternative finance. More specifically, this course primarily covers various alternative finance models, including P2P consumer and business lending, donation-based, equity-based and reward-based crowdfunding, invoice trading, and pension-led funding, and alternative finance instruments, including debt-based securities, SME mini-bonds, social impact bonds, community shares, and shadow banking system.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Describe the principles and functions of P2P lending and crowdfunding.
- 2.Analyze the models of P2P lending and crowdfunding.
- 3.Identify the functions of invoice trading, pension-led funding and community shares.
- 4.Analyze and manage risks of debt-based securities, SME mini-bonds and social impact bonds.
- 5.Define the structures and roles of shadow banking system.
- 6.Conduct qualitative or quantitative analysis about the shadow banking system.
- MFIT 5008Decision Analytics for FinTech[3-0-0:3]DescriptionThis course aims to introduce decision analytics instruments and their applications in FinTech. Main topics covered in this course include basic probability and statistics, predictive analytics, prescriptive analytics such as linear programming integer programming, dynamic programming and sequential decision making, stochastic models, quality control, Monte Carlo simulation, game theory, and their applications in various areas of FinTech.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Define basic theories of probability and statistics.
- 2.Apply prescriptive analytics to modeling and analysis.
- 3.Apply predictive analytics to modeling and analysis.
- 4.Analyze real problems via quality control and game theory.
- 5.Conduct Monte Carlo simulation to solve real problems.
- 6.Explain the applications of decision analytics to various FinTech-related problems.
- MFIT 5009Optimization in FinTech[3-0-0:3]DescriptionThis course introduces the basic theory of convex optimization and illustrates its practical employment in a wide range of FinTech applications. Techniques and applications of nonconvex optimization are also considered. Examples of the problems considered include Markowitz portfolio optimization and its many variations (e.g., maximum Sharpe ratio portfolio, risk-parity portfolio, robust portfolio, sparse portfolio), data fusion, machine learning for classification/estimation, imputation of missing data, big data analysis, outlier detection, data clustering, and deep learning.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Design a portfolio based on optimization.
- 2.Clean data (outlier detection and imputation of missing data).
- 3.Perform data fusion from different sources via optimization.
- 4.Analyze high-dimensional data via clustering and low-rank fitting methods.
- 5.Employ deep learning methods in a financial context.
- MFIT 5010Statistical Machine Learning[3-0-0:3]Exclusion(s)MATH 5470, MSDM 5054DescriptionThis course provides students with an extensive exposure to the elements of statistical machine learning in supervised and unsupervised learning with real world datasets. Topics include basic models in regression and classification, resampling methods, model selection/assessment, and some standard techniques in unsupervised learning such as clustering and dimensionally reduction.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Recognize a wide variety of learning algorithms.
- 2.Execute the application of algorithms to data.
- 3.Develop the skill to perform evaluation of learning algorithms.
- 4.Differentiate strength and weakness for different algorithms.
- 5.Implement well at least one programming language.
- MFIT 5011Statistical Methods in Finance[3-0-0:3]DescriptionThis course addresses fundamental topics in statistics and their applications to financial models. The statistical methods include descriptive and exploratory statistical analysis, statistical inference, linear and non-linear regression, principal components and factor models. Financial applications include statistical analysis of portfolio theory, CAPM and multifactor pricing models and financial time series analysis.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.List and apply basic statistical tools.
- 2.Implement the statistical analysis procedures through R.
- 3.Differentiate basic financial models.
- 4.Execute statistical analysis of financial data.
