EXC 3672 Analyses of Financial Data

EXC 3672 Analyses of Financial Data

Course code: 
EXC 3672
Department: 
Finance
Credits: 
7.5
Course coordinator: 
Kjell Jørgensen
Course name in Norwegian: 
Analyses of Financial Data
Product category: 
Bachelor
Portfolio: 
BBA - Specialisation in Finance
Semester: 
2024 Autumn
Active status: 
Active
Level of study: 
Bachelor
Teaching language: 
English
Course type: 
One semester
Introduction

This course covers important econometric techniques that are used in empirical finance. The main focus will be on time series econometrics as time series is the most frequent data format in finance. The course starts by examining basic regression analysis. Then it continues on to forecasting and the modelling of long-run relationships in finance, before it finally focuses on the modelling of volatility.

Learning outcomes - Knowledge

The aims of this course are to:

  1. Introduce students to important empirical quantitative techniques used in finance and more generally in business and economics.
  2. Make students able to apply them appropriately.
  3. Prepare students for subsequent course work in finance, business, and economics.

More specifically, on completion of the course the students acquired knowledge and skills should be as follows:

  • Understand in depth what is meant by correlation and regression analysis - and the difference between them
  • Understand in depth what is meant by Ordinary Least Squares (OLS) - the estimation technique used in order to estimate our econometric model
  • Understand in depth the limits and assumptions of regression analysis and the consequences that follows from violations of the different assumptions
  • Understand the rationale for dummy variables; what they are and how they can be used
  • Understand the rationale for univariate time series and how these can be for forecasting economic variables
  • Understand how long-run relationships in finance can be modelled. More specifically; understand the concepts of Stationarity, Cointegration and Error Correction Models
  • Understand how the volatility of economic variables can be modelled. More specifically; understand how ARCH-type of models work
  • Understand how the programming language R works and be familiar with basic R syntax
Learning outcomes - Skills

On completion of the course students should be able to use software like R in order to:

  • Perform basic data handling
  • Estimate financial models formulated as linear regression models
  • Test the statistical assumptions underlying OLS
  • Estimate regression models with dummy variables
  • Estimate appropriate univariate time series models and use them to forecast economic variables, and then evaluate the quality of the forecast
  • Test for Stationarity and Cointegration
  • Estimate Error Correction Models
  • Estimate appropriate volatility models (ARCH-type models)
General Competence

Students will acquire a conscious and critical attitude towards different data types and their handling, towards econometric analysis, and towards the assessment and interpretation of results from empirical research.

Students will also by completing the course develop both their analytical and programming skills. 

Course content

This course introduces students to empirical techniques that are relevant for finance and business and economics in general. More specifically, the outline of the course is as follows:

Foundations for empirical methods in finance.

  • What is econometrics?
  • Regression analysis with Ordinary Least Squares (OLS)
  • Introduction to R
  • Regression with Dummy variables
  • Univariate time series models and forecasting
  • Modelling long-run relationships in finance
  • Modelling volatility
Teaching and learning activities

Each topic will be accompanied by a hands-on practical application of an empirical finance topic. 

The software package R will be an integral part of the coursework. R is a software program that has become a standard for data analysis inside academia and corporations, especially in the finance industry. It is an open source software available free of charge on internet. The use of R will introduce students to some of the basics of programming. Programming is a skill typically required in the financial industry.

If a student misses a class, it is her/his responsibility to obtain any information provided in class that is not included on the course homepage/itslearning or in the textbook.

Computer-based tools: Google, Yahoo finance, Quandl, and itslearning. 

Software tools
R/R-Studio
Qualifications

Higher Education Entrance Qualification

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this

Required prerequisite knowledge

EXC 2910 Mathematics, EXC 2904 Statistics and EXC 3506 Research Methods and Econometrics. Or equivalent.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Submission PDF
Exam/hand-in semester: 
First Semester
Weight: 
30
Grouping: 
Group/Individual (1 - 4)
Duration: 
2 Week(s)
Comment: 
Written home assignment.
All exams must be passed to obtain a final grade in the course.
Exam code: 
EXC 36722
Grading scale: 
ECTS
Resit: 
Examination every semester
Exam category: 
School Exam
Form of assessment: 
Written School Exam - pen and paper
Exam/hand-in semester: 
First Semester
Weight: 
70
Grouping: 
Individual
Support materials: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration: 
3 Hour(s)
Comment: 
All exams must be passed to obtain a final grade in the course.
Exam code: 
EXC 36723
Grading scale: 
ECTS
Resit: 
Examination every semester
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
39 Hour(s)
Seminar groups
3 Hour(s)
Computer sessions.
Student's own work with learning resources
146 Hour(s)
Review of the slides every evening after the lecture.
Group work / Assignments
12 Hour(s)
Sum workload: 
200

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.