IB93F-15 Research Methodology
Introductory description
The module aims to equip students with cutting edge research methods alongside other programming skills and access to relevant data sources.
Module aims
The structure of this module will provide training in programming and data handling from the very beginning of the course, to ensure that students have the required skills when they are needed for workshops and assignments in the core modules.
20 hours of programming skill sessions will be delivered primarily in Term 2, but a small fraction of these sessions will be delivered in Term 1. In addition, 10 hours of lectures will be devoted to sub-field specific research methods especially for Term 3, which are commonly used in students' dissertation subject areas.
The module will assess programming skills through one individual assessment and an empirical project in the chosen subject area.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Computer programming skills
-
Logic of programming -
Use of matrices, arrays, data types, structures and manipulation. -
Good practices with computer programming. -
Algorithmic principles in applied research. -
Data storage and retrieval.
Sub-field specific research methodology - students will be required to select 2 out of several sub-field specific research methodology lecture sets, which will cover for example:
-
Asset pricing: Fama-Macbeth regressions, Constructing and optimizing portfolios. -
Corporate finance: Instrumental variables, Quasi-experimental methods (Matching using propensity scores) and event studies.
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate an understanding of research methods used in the financial context
- Fully appreciate and thoroughly explain the limitations of research projects in a particular research subject; be realistic in what conclusions can safely be drawn as a result of executed tests.
- Use appropriate models for testing.
- Develop, apply and interpret appropriate tests of typical hypotheses in a particular subject area.
- Understand the logic of programming, use of matrices, arrays, and data manipulation, including good practices with computer programming.
- Apply appropriate analytical methods to different research questions.
Indicative reading list
Hunt, Andrew, and David Thomas. The Pragmatic Programmer: From Journeyman to Master. 2nd ed. Addison-Wesley Professional, 2019. ISBN: 0135957052.
Campbell, John Y., Andrew W. Lo, and Archie C. MacKinlay. The econometrics of financial markets. Princeton University press, 1997.
Subject-specific research articles.
Subject specific skills
Understand the logic of programming, use of matrices, arrays, and data manipulation, including good practices with computer programming.
Apply appropriate analytical methods to different research questions.
Transferable skills
Use university/library resources to generate information/datasets for use in the project.
Study time
| Type | Required |
|---|---|
| Lectures | 5 sessions of 2 hours (13%) |
| Practical classes | 10 sessions of 2 hours (26%) |
| Private study | 48 hours (62%) |
| Total | 78 hours |
Private study description
Private study to include preparation for lectures and practical classes/workshops
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
|||
| Empirical Research Project | 70% | 50 hours | Yes (extension) |
Reassessment component is the same |
|||
Assessment component |
|||
| Programming skills assignment | 30% | 22 hours | Yes (extension) |
Reassessment component is the same |
|||
Feedback on assessment
Feedback via my.wbs
There is currently no information about the courses for which this module is core or optional.