SDSC3022 - Financial Data Analytics for Investments | ||||||||||
| ||||||||||
| * The offering term is subject to change without prior notice | ||||||||||
Course Aims | ||||||||||
The goal of this course is to build a foundation for analyzing financial investments.? We will cover cross-sectional and time-series facts central to modern financial economics such as the size effect, value effect, momentum, and return predictability.? We will introduce these facts through the lens of both traditional tools available in financial economics such as predictive regressions and panel regressions, as well as more modern predictive analytics such as dimensionality reduction and other machine learning techniques.? We will also equip students with an introductory understanding of the investment management industry, hedge funds, and high-frequency trading.? At the end of this course, students should have a solid background in the issues in modern finance as well as the tools in data science used to address them.?? | ||||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||||
Continuous Assessment: 60% | ||||||||||
Examination: 40% | ||||||||||
Examination Duration: 2 hours | ||||||||||
Note: To pass the course, apart from obtaining a minimum of 40% in the overall mark, a student must also obtain a minimum mark of 30% in both continuous assessment and examination components. | ||||||||||
Detailed Course Information | ||||||||||
| SDSC3022.pdf | ||||||||||
