| Unit name | Advanced Financial Technology |
|---|---|
| Unit code | COMSM0090 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Ge |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Introduction to Financial Technology and a competency in Python, equivalent to EMATM0048 (SDPA) |
| Units you must take alongside this one (co-requisite units) |
N/A |
| Units you may not take alongside this one | |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
This unit aims to provide students with the practical skills to perform advanced data driven analysis of financial markets typically used by investment banks and hedge funds for trading and risk management.
Financial markets generate masses of time series data. Therefore, to analyse financial markets, it is necessary to be competent in time series analysis. This unit commences with approaches to solve common data engineering and analysis challenges in time series, including wrangling time series data, undertaking exploratory time series data analysis, storing temporal data, simulating time series data, generating and selecting features for a time series, forecasting and classifying time series with statistical machine learning and deep learning, and evaluating accuracy and performance. The unit then considers methods for applying financial time series analysis and machine learning for managing financial investments. Students will be introduced to active portfolio management and gain a practical understanding of concepts such as expected returns, signal weighting, risk management, and portfolio construction.
At the end of the course a successful student will be able to:
1. Apply established time series analysis methods on large-scale financial data sources.
2. Implement machine learning models for financial data and explain their operation.
3. Demonstrate and explain the concepts and assumptions underpinning active portfolio management.
Unit delivery will be blended. Unit content will be provided as a series of short pre-recorded online video lectures, organised into topics, for students to watch asynchronously. Each topic will have associated links for additional reading and formative online exercises. Each topic will also have associated synchronous flipped lecture sessions (either online or physical) and technical lab sessions with live streaming for remote participation. Flipped lectures will involve student participation in individual and group activities.
Continuous assessment (30%): Students take a series of short online tests at regular intervals throughout the unit. The mark for this component is formed from the best two tests out of a total of three
Coursework (70%): Analyse a real-world financial dataset using time series analysis and machine learning and use findings to suggest investment strategies and portfolio construction. Submit an 8-page report presenting a real-world contextualisation of the work, data analysis, results, and conclusions (50%). (ILO 1, 2, 3). Give a 10-minute presentation of the report in an industry style, for example as though presenting to a fund manager or board of directors (20%). (ILO 3).
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMSM0090).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the Faculty workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
