SDSC6016 - Predictive Analytics and Financial Applications

Offering Academic Unit
School of Data Science
Credit Units
Course Duration
One Semester
Course Offering Term*:
Not offering in current academic year

* The offering term is subject to change without prior notice
Course Aims

This course focuses on the application of predictive analytics to financial data.  We will review some central tools used in economic and financial forecasting, including predictive regressions, time series models, variable and model selection, forecast combinations, shrinkage methods, and vector autoregression.  We will explore how these tools can be applied to predict the returns to financial assets, including global equities and commodities, paying special attention to the different types of forecasts – point forecast, interval forecast, or density forecast.  We will also introduce modern predictive methods, such as penalized regressions and neural networks, and their applications to making predictions in financial markets.  Students are expected to gain working knowledge of how to apply predictive analytics to economics and financial problems.

Assessment (Indicative only, please check the detailed course information)

Continuous Assessment: 70%
Examination: 30%
Examination Duration: 2 hours
Detailed Course Information


Useful Links

School of Data Science