| Unit name | Advanced Financial Technology |
|---|---|
| Unit code | SEMTM0026 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Yang |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
SEMTM0031 Introduction to Financial Technology SEMTM0028 Software Development: Programming and Algorithms for Financial Technology or equivalent experience with Python programming |
| Units you must take alongside this one (co-requisite units) |
N/A |
| Units you may not take alongside this one |
N/A |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
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.
How does this unit fit into your programme of study?
This unit deepens the understanding of statistical and machine learning principles vital for data analysis in finance. It aligns with the program's aim to develop skills for assessing and engineering financial technologies, ensuring students can implement solutions that leverage data-driven insights effectively in the FinTech sector.
An overview of content
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.
How will students, personally, be different as a result of the unit
Students will emerge with enhanced expertise in financial technologies, mastering statistical and machine learning techniques for data-driven finance. They will develop critical thinking for complex data analysis and practical skills in computational tools, preparing them to innovate and solve real-world problems in the FinTech industry.
Learning Outcomes
On successful completion of this unit, students 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.
Tasks which help you learn and prepare you for summative tasks (formative)
Students will complete two “continuous assessment” (CA) activities during the course of the semester, each of which will have a specified deadline. Feedback on CA activities will only be given to students who submit their work by the deadline. Students will then have an opportunity to revise their CA work before it is submitted for summative assessment (see below).
The CA will develop students' skills in wrangling and exploring time series data as well as including material on applying established analytical models on large-scale financial datasets. This progression will equip students with the necessary expertise to effectively analyse and interpret complex financial data, supporting them in learning how to apply time series analysis methods in real-world financial contexts.
Students will also receive formative feedback on their work through flipped classroom activities where students participate in individual and group activities and receive feedback from teaching staff.
Tasks which count towards your unit mark (summative)
Continuous assessment (30%): As described above, students will complete two “continuous assessment” (CA) activities during the course of the semester and will receive feedback on their work if their work is submitted by the specified deadline. Students will then select one of these two CA activities to revise and submit as a summative assessment. This submission should be an extended/reworked version of their previous formative submission, with an additional (one page max) reflection summary describing changes made based on feedback. (ILO 1)
Coursework (70%): Students will analyse a real-world financial dataset using time series analysis and machine learning and use their findings to suggest investment strategies and portfolio construction. They will submit an 8-page report presenting a real-world contextualisation of the work, data analysis, results, and conclusions (50% of overall unit mark). (ILO 1, 2, 3). As part of this assessment, students will also 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% of overall unit mark) (ILO 3).
When assessment does not go to plan
Re-assessment takes the same form as the original summative assessment.
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. SEMTM0026).
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 University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
