Programme And Module Handbook
 
Course Details in 2020/21 Session


If you find any data displayed on this website that should be amended, please contact the Curriculum Management Team.

Module Title LM Financial Modelling Techniques
SchoolBirmingham Business School
Department Birmingham Business School
Module Code 07 26687
Module Lead Jane Binner
Level Masters Level
Credits 20
Semester Semester 1
Pre-requisites
Co-requisites LM Corporate Financial Management - (07 26685)
LM Advanced Corporate Financial Management - (07 26688)
Restrictions Available to only students on the MSc Financial Management programme.
Contact Hours Guided independent study-170 hours
Practical Classes and workshops-10 hours
Lecture-20 hours
Total: 200 hours
Exclusions
Description This module teaches students a wide range of techniques for summarising data analysing data, estimating models and testing hypotheses. The module has three sections. In the first section, students will learn the techniques for calculating measures of central tendency and dispersion of a dataset, such as mode, median, mean, range, standard deviation, skewness and kurtosis. Statistical inference from the classical linear regression model will cover concepts of hypothesis testing and construction of confidence intervals for regression coefficients.

In Section two, further developments of the classical linear regression model will include the breakdown of the classical regression model assumptions e.g. multicollinearity, heteroscedasticity and autocorrelation and remedies for these problems.

In the final section, students will learn parametric techniques for analysing data, constructing financial models and testing the goodness of fit of models Topics that will be covered will include multiple regression analysis, moving average processes, autoregressive processes, ARCH and GARCH processes and an introduction to forecasting. More sophisticated models such as logit and probit models and an introduction to simultaneous equation models as well as the strengths and limitations of each technique will conclude the module.
Learning Outcomes By the end of the module students should be able to:
  • determine the appropriate technique(s) to use to summarise a set of data, apply the technique(s) and interpret results obtained correctly;
  • determine the appropriate probability model(s) to use to estimate the likelihood of occurrence of some events that a finance manager is interested in, apply the model(s) and interpret results obtained correctly;
  • determine the appropriate parametric technique(s) to use to test a hypothesis, construct suitable financial models, apply the technique(s) and interpret results obtained correctly;
  • determine the appropriate non-parametric technique(s) to use to test a hypothesis, apply the technique(s) and interpret results obtained correctly;
  • demonstrate critical awareness of the strengths and limitations of any technique that they use.
Assessment 26687-01 : Individual Assignment : Coursework (50%)
26687-02 : Exam : Exam (Centrally timetabled) - Computer based (50%)
Assessment Methods & Exceptions Assessment: 2,500 word individual assignment (50%) & 2 hour exam (50%)
Reassessment: Students only resit the failed component
Other
Reading List