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2017/18 Undergraduate Module Catalogue

LUBS2225 Credit and Financial Analytics

20 creditsClass Size: 92

Module manager: Prof Nick Wilson

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2017/18

This module is mutually exclusive with

LUBS2570Introduction to Econometrics
LUBS2670Statistics for Business and Economics 2

This module is not approved as a discovery module


The aims of this module are to give students: a knowledge of research methods and statistical financial analytical techniques and how they can be used to analyse complex financial data sets, credit decisions and in credit risk management (credit scoring methods); the opportunity to develop their knowledge and understanding of the practical application of credit and financial analytics in practice and in research in credit and finance, and an appreciation of some of the current research being conducted in credit risk management and finance.

Learning outcomes
On completion of the module students will be able to:
- Identify and use modern credit and financial analytical techniques as applied in research in finance and in practice in the financial services within the Basel II/III regulatory framework;
- Critically evaluate analytical tools used in credit risk modelling and develop the quantitative skills required to assess and manage default risk and the associated metrics used by banks and other lenders;
- Apply analytical techniques to model, analyse and predict financial market and individual/corporate behaviour and test theories and hypothesise in the discipline.

Skills outcomes
Transferable skills:

- Written communication skills
- Critical thinking skills
- Advanced numeracy skills

Subject specific skills:

On completion of this module students will be able to:

Demonstrate advanced problem solving, analytical and quantitative skills by applying current theory and appropriate analytical tools to complex problems in credit risk management.


Indicative content:
- Introduction to Econometrics and Applications in Finance
- Statistical Foundations, Descriptive Statistics, Distributions and Hypotheses Tests Data Types and Issues, Topics and Concepts in Finance.
- Correlation and Simple Regression. Least Squares Methods and Diagnostics. Testing. Hypothesis Testing. Issues Relating to Autocorrelation.
- Multiple Regression. Specification and Testing of Regression Models. Functional Forms and Transformations. Dummy Variables. Issues Relating to Multi-collinearity, autocorrelation and heterscedasticity. Applications and Examples.
- Analysis of Limited Dependent Variables. Probability. Linear probability and Logistic Regression. Models, Tests and Interpretation. Applications in Finance: Credit Risk and Probability of Default. Other Limited Dependent Variables.
- Elementary time series estimation.
- Introduction to credit scoring techniques in retail and corporate lending. Estimation and management of credit risk components: PD (probability of default), LGD (loss given default), and EAD (exposure at default). Assessing and validating credit risk models.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours144.00
Total Contact hours56.00
Total hours (100hr per 10 credits)200.00

Private study

Students are required to undertake specified pre-reading and question preparation in advance of the classes based on the material introduced in the lectures. This includes some online pre-reading and formative questions.

Opportunities for Formative Feedback

Students will be able to monitor their progress through fortnightly seminars. Model answers and answers and marking schemes for workshop papers against which students can assess their own performance;

Methods of assessment

Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)2 hr 50.00
Standard exam (closed essays, MCQs etc) (S1)2 hr 50.00
Total percentage (Assessment Exams)100.00

The resit for this module will be 100% by examination.

Reading list

The reading list is available from the Library website

Last updated: 25/01/2018


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