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DRPS : Course Catalogue : Business School : Business Studies

Undergraduate Course: Research Methods in Finance (BUST10132)

Course Outline
SchoolBusiness School CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThe aim of this course is to equip students with statistical tools and techniques to undertake quantitative research projects in Finance. It is suitable for students (majoring in Finance) who have performed well in Business Research Methods I and who wish to conduct a quantitative dissertation. Students are expected to choose between this course or Business Research Methods II: Applications and Analysis (which includes qualitative research methods).
Course description The syllabus includes:
1. Ordinary Least Squares (OLS) assumptions, estimation and inference
2. Practical Session: Simple/multiple linear regression,
3. Estimation techniques involving non-linear terms, dummy variables and interactions
4. Practical Session: Non-linear terms, dummy variables and interactions.
5. Time series analysis
6. Violations of OLS assumptions (e.g. heteroskedasticity and autocorrelation): consequences, detection and correction
7. Practical Session: Time series analysis, heteroskedasticity and autocorrelation
8. Carrying out an empirical project: posing a question, literature review, data collection and econometric analysis
9. Practical Session: Obtaining financial data
10. Practical Session: Issues with regards to data (coding, outliers, missing data, etc.

The main tool for the course is OLS regression analysis; however, students may be made aware of other quantitative techniques available in the Accounting and Finance literature in order to analyse the existing literature in an informed manner.

The modes of delivery are lectures and computer lab sessions. The computer lab sessions are used to support practical learning of the concepts and tools delivered in the lectures. Students are expected to engage in online participations both in class and independently in their own time. Participations will be recorded and assessed.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Introduction to Financial Markets (BUST08029) AND Introduction to Corporate Finance (BUST08030) AND Business Research Methods I: Introduction to Data Analysis (BUST08033)
Prohibited Combinations Other requirements This course is only available to students on the MA Accounting and Finance or MA Business and Finance degree programmes. Students must have obtained at least 60% in Business Research Methods I: Quantitative Techniques (CMSE08002)
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Summative Assessment Hours 2, Revision Session Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 172 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) Examination (50%)
Group project (40%)
Participation (10%)
Feedback Formative feedback:
- Multiple choice questions during the lectures
- Weekly exercises
- Online quizzes
- Online discussion board
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Students will demonstrate a knowledge and understanding of key econometric techniques for the empirical analysis of economic phenomena, along with application of these techniques in the contexts of Accounting and Finance research.
  2. Students will be able to apply specialist research and investigative skills such as problem framing and solving and be able to assemble and evaluate complex evidence and arguments.
  3. Students will be able to undertake practical/technical competencies such as, modelling skills (abstraction, logic, succinctness), qualitative and quantitative analysis and interpretation of data, programming of statistical packages and demonstrate general IT literacy.
  4. Students will be able to present outputs from a statistical software in the ways that are formal, visually appealing and widely accepted in the accounting and finance disciplines. Student will also be able to communicate in writing the meaning and significance of their statistical findings.
  5. Students will demonstrate personal effectiveness through task-management, time-management, teamwork and group interaction through the ability to successfully complete a group work.
Reading List
Jeffrey M. Wooldridge. Introductory Econometrics: A Modern Approach, 4e, Cengage Learning, 2009.
- Weeks 1-2 (OLS assumptions, estimation and inference; simple/multiple linear regression): Chapters 2-5
- Weeks 3-4 (Non-linear terms, dummy variables and interactions): Chapters 6-7, 9
- Week 5 (Time series analysis): Chapter 10
- Weeks 8-9 (Violations of OLS assumptions): Chapters 8, 11, 12
- Week 10 (Carrying out an empirical project): Chapter 19
- Weeks 11-12 (Issues with regards to data): Chapter 9

Additional Information
Graduate Attributes and Skills Research and Enquiry: Students are able to critically evaluate research questions, transform research questions into testable hypotheses and choose appropriate empirical approaches to answer the research questions.

Personal and Intellectual Autonomy: Students are able to understand complex research methodologies and theories in the areas of Accounting and Finance.

Personal Effectiveness Students are able to manage a small project, allocate tasks among team members. Students are able to manage time effectively and meet deadlines.

Communication skills: Students are able to effectively communicate complex empirical results in an appropriate technical manner.

Enquiry and lifelong learning: Students are aware of tools and techniques used to carry out quantitative research and are able to understand the interpretation, implications and limitations of research results. These skills allow students to understand and question statistical results often presented in business reports and the media.

Engagement with Practice: Students are expected to develop statistical mind and data management skills which are transferrable skills relevant to modern skilled employment.
KeywordsNot entered
Course organiserDr Angelica Gonzalez
Tel: (0131 6)51 3027
Course secretaryMs Claire McCullough
Tel: (0131 6)51 3798
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