Keele Badge

FIN-20005 - Module Specification
Keele Business School
Faculty of Business, Law, Humanities & Social Sciences

For academic year: 2026/27 Last Updated: 17 March 2026

FIN-20005 - Financial Analysis with Bloomberg
Coordinator: Robina Iqbal Room: N/A
Lecture Time: See Timetable...
Level: Level 5
Credits: 30
Study Hours: 300
School Office: 01782 733094

Programme/Approved Electives for 2026/27

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

Methods for Finance

Barred Combinations

None

Description for 2026/27

This module is designed to equip you with essential skills in financial data analysis and risk management. You will engage hands-on with the Bloomberg terminal and Python to analyze real-world financial data.
Key topics include AutoRegressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, empowering students to forecast market trends and assess volatility.
Prepare for a successful career in investment management and quantitative analysis by mastering critical concepts in financial econometrics and risk management.

Aims
1. Develop knowledge and understanding of financial data analysis techniques, including the use of the Bloomberg terminal.
2. Apply advanced time series models and financial econometric analysis to real-world financial data and scenarios.
3. Enhance practical skills in risk management and decision-making using statistical methods and Python.

Intended Learning Outcomes

use the Bloomberg terminal to collect and analyse financial data to support informed financial decisions.: 1,2
explain key concepts in financial data analysis, including time series behaviour and models such as ARIMA and GARCH.: 1,2
identify financial problems and choose suitable time series or econometric models to analyse them.: 1
apply time series and econometric techniques to real financial data and explain the outcomes.: 2
assess financial risk using current techniques like Value at Risk (VaR).: 1
use Python to analyse financial data, run models, perform simulations, and present results visually.: 2

Study hours

Interactive Lectures 12 hours
Practical Tutorials / Labs (Bloomberg & Python) Data Workshops 36 hours

Directed Reading and Resource Review 50 hours
Preparation for Practical Sessions 40 hours
Independent Financial Data Analysis and Model Practice 50 hours
Assessment Preparation and Completion 90 hours
Reflection, Review, and Consolidation 22 hours

School Rules

None

Description of Module Assessment

1: Portfolio weighted 60%
Applied Skills Portfolio
Students will complete and submit a portfolio consisting of three practical tasks selected by the module leader. Each task will focus on a different key aspect of the module, such as time series modelling, Python-based financial analysis, and risk measurement. The tasks are designed to be submitted at regular intervals across the semester to support cumulative skill development and reduce assessment pressure. Submissions will combine code, output, and concise written explanations (approx. 600–800 words per task).

2: Report weighted 40%
Applied Analysis Report
Students will produce an individual applied report (1,500–2,000 words) based on real-world financial data obtained via the Bloomberg terminal. The report will require students to identify a relevant financial problem or scenario, apply appropriate time series or econometric models (e.g., ARIMA, GARCH), perform analysis using Python, and evaluate financial risks using techniques such as Value at Risk. The assessment encourages synthesis of theory and practice and interpretation of analytical outputs in a decision-making context.

The University undertakes a continuous review of its teaching and research provision to ensure programmes are of a high quality and changes may be made to modules where it is necessary and reasonable to do so. Please be aware that minor changes to modules, such as a change to an individual assessment, can occur up until enrolment, although we endeavour to give you as much notice as possible of any changes. Details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/legalgovernancecompliance/governance/actcharterstatutesordinancesandregulations/studenttermsconditions/

Last Updated: 17 March 2026