Module Information
Course Delivery
Assessment
Due to Covid-19 students should refer to the module Blackboard pages for assessment details
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Exam | 3 Hours Written Exam | 70% |
| Semester Assessment | Written Report (3,000 words) | 30% |
| Supplementary Exam | 3 Hours Written Exam | 70% |
| Supplementary Assessment | Written Report (3,000 words) | 30% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Use concepts from probability theory to describe the distributional properties of key financial variable.
2. Apply appropriate statistical methods to financial data to answer questions of interest to finance practitioners and researchers.
3. Identify the direction and strength of a linear relationship between two numerical variables using correlation or regression analysis.
4. Estimate a multiple regression model and interpret the results.
5. Apply estimation techniques such as logit or probit, or panel regression estimation, to financial data sets where appropriate, and interpret the results.
Brief description
This module provides an introduction to statistical and econometric methods that are used to model financial data. Although the main emphasis is on practice rather than theory, there is sufficient theoretical grounding to provide a critical awareness of the strengths and weaknesses of the modelling techniques that are employed. There is emphasis on development of the independent capability to design and estimate appropriate statistical econometric models using the Stata software package.
Content
• Measures of central tendency and dispersion
• Probability density function and distribution function
• The binomial, Poisson and normal distributions
• Jointly distributed random variables, covariance, correlation
• Sampling and estimation, sampling distributions
• Hypothesis tests
• Contingency tables, ANOVA
• Sample correlation: estimation and statistical inference
• The two-variable linear regression model
• The multiple regression model
• Discrete choice regression models: logit and probit regression
• Models for panel data: fixed and random effects
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | • Develop an easy familiarity with numerical data sources and numerical data • Apply numerical data to problem solving with care and accuracy • Assess the reasonableness of and interpret numerical solutions • Support assertions/arguments with appropriately developed and presented numerical data Apply complex mathematical formulae. |
| Communication | • Develop confidence in and clarity of oral communication via example class/tutorial participation • Develop clarity and focus of written communication via development of answers to self-study questions Develop and use appropriate subject-specific vocabulary in oral and written communication |
| Improving own Learning and Performance | • Identify and distil the key issues covered by lectures, tutorials and self-study • Identify and use a range of learning resources • Investigate benefits of small group working on self-study Structure study to accommodate intensive learning |
| Information Technology | • Use a variety of electronic web- and library-based resources to review available information and retrieve pertinent information Use spreadsheet software to complete elements of the self-study (e.g., for ease of tabulated numerical calculations, production of summary statistics, production of graphs, etc.) |
| Personal Development and Career planning | • Preparation for seminar tasks will encourage initiative, independence and self-awareness • Identify a variety of potential career opportunities within the financial and professional services sector. |
| Problem solving | • Identify the precise problem to be solved • Assess which data are pertinent to the problem • Recognize that alternative solution methods might be available • Select and apply appropriate methods for solving the problem Assess the reasonableness of problem solutions and interpret those solutions |
| Research skills | • Identify which information sources are available to: o facilitate module study (understanding, wider reading) o provide data which allow application of module learning in a real world context Properly reference/attribute information sources |
| Subject Specific Skills | • Develop competence in understanding and appropriately applying statistical data analysis/ theory to practice. • Develop competence in understanding the appropriate methods to estimate and make inferences about a population characteristics and their limitations. |
| Team work | Develop experience of team work and develop team working skills via small group working on self-study. |
Notes
This module is at CQFW Level 7