Module manager: Nabi Omidvar
Email: M.N.Omidvar@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
| LUBS2227 | Financial Econometrics |
This module is not approved as a discovery module
This module introduces the essentials of programming and software development with a focus on finance. It aims to equip students with the skills required to develop computer applications capable of managing and analysing large-scale financial datasets, or "big data", encompassing data processing, manipulation, and visualisation. Students will gain the experience needed to handle financial datasets effectively, enabling them to generate visual representations and perform advanced big-data manipulations with efficiency. The module is structured to prepare students for further exploration into advanced analytics for more sophisticated financial modelling. There is a growing demand by employers for finance experts to perform financial analytics with Python as they transition to Python for predictive analytics. This shift has often led employers to hire data scientists without a background in finance to fill this need. The goal of this module is to bridge the gap by equipping students with both financial expertise and Python programming skills, ensuring they are well-prepared to meet industry demands.
This module aims to equip students with practical Python skills for finance-related data processing, analysis, and introductory predictive analytics while laying the foundation for advanced topics such as AI, machine learning, and deep learning. The module is designed for students with no prior programming experience and provides a structured progression from basic Python concepts to the manipulation of large-scale financial datasets and real-world problem-solving, addressing industry demand for expertise in both finance and programming.
Learning activities are hands-on and practical, with lectures offering step-by-step demonstrations of key concepts and lab-based practical sessions guiding students through problem-solving exercises using real-world financial datasets. These sessions, supported by direct tutor feedback, bridge the gap between theory and practical application, ensuring students gain experience relevant to industry needs.
The assessment consists of one project submitted in two stages: a formative submission midway through the module provides students with feedback on their progress, while the final summative submission evaluates their ability to solve a real-world financial problem, such as credit risk analysis or financial time series forecasting. The final report will also include an executive summary and a self-reflective hindsight analysis. Additional resources, including written notes and extra exercises, support independent study, helping students consolidate their skills.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Design, implement, and test Python programmes to address financial problems.
2. Develop systems that connect with external data sources, enabling big data manipulation and transformation for financial analysis.
3. Create visualisations that effectively communicate insights from financial datasets.
4. Apply data analytics using Python for basic predictive modelling in finance.
5. Critically evaluate and reflect on the choice of modelling techniques and their suitability for specific financial analyses.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Effectively communicate technical financial analysis and findings to non-technical audiences.
2. Cultivate the ability to creatively apply programming techniques to solve financial problems.
3. Efficiently navigate and utilise Python resources for independent financial problem-solving.
4. Gain familiarity with several AI-based models to complement traditional financial analysis.
Indicative content includes:
1. Python Programming Foundations
2. Data Handling and Manipulation
3. Accessing Financial Data with Python
4. Analysis and Visualisation of Financial Data
5. Applied Text Mining for Finance
6. Introduction to Predictive Analytics in Finance
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 10 | 1.5 | 15 |
| Practical | 5 | 1 | 5 |
| Independent online learning hours | 20 | ||
| Private study hours | 60 | ||
| Total Contact hours | 20 | ||
| Total hours (100hr per 10 credits) | 100 | ||
The module offers ample opportunities for formative feedback. In bi-weekly practical lab sessions, students will tackle guided exercises, allowing them to directly apply concepts learned in lectures and receive immediate feedback. Additionally, weekly short multiple-choice quizzes focusing on core concepts from the lectures will reinforce learning and provide insights into students' comprehension. In the second half of the module, students will be given a small modelling question as part of their bi-weekly practical session (most likely in week 7), and they will be asked to write a brief interpretation and analysis of the modelling exercise for the following practical session (week 9) to receive feedback on their analysis. This serves as an opportunity to provide formative feedback that also helps students prepare for the final coursework.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | 2,000 word report | 100 |
| Total percentage (Assessment Coursework) | 100 | |
The resit for this module will be 100% examined by a 2,000 word report.
There is no reading list for this module
Last updated: 30/04/2025
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