COURSE UNIT TITLE

: FINANCIAL DATA ANALYSIS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IBS 4351 FINANCIAL DATA ANALYSIS ELECTIVE 3 0 0 4

Offered By

International Business and Trade

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ERDOST TORUN

Offered to

BUSINESS ADMINISTRATION
International Business and Trade
International Trade and Business (English)

Course Objective

This course aims at analyzing financial data sets and increasing the ability of interpreting daily financial developments by using statistical methods.

Learning Outcomes of the Course Unit

1   Use quantitative methods for analyzing high frequency financial data.
2   Understand basic properties of fundamental statistical and econometric models
3   Use basic financial data analysis software to estimate models and interpret the estimation results.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

ECN 1904 - PRINCIPLES OF MACROECONOMICS

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Fundamental Statistics 1
2 Fundamental Statistics 2
3 Linear Regression Model
4 Multiple Regression Model
5 Panel Data Analysis 1
6 Panel Data Analysis 2
7 Overall Review
8 Unit Root Tests
9 Time Series Analysis 1
10 Time Series Analysis 2
11 Overall Review
12 Modelling Short Run Relationships in Finance
13 Modelling Long Run Relationships in Finance
14 General Overview

Recomended or Required Reading

1. Analysis of Financial Data, Gary Koop, Wiley Publishing.

Planned Learning Activities and Teaching Methods

1. Lectures
2. Problem session
3. Computer program application sessions.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 TP TermProject
2 ASS Assignment
3 FCG FINAL COURSE GRADE TP * 0.58 + ASS * 0.42


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

THERE WILL BE 1 ASSIGNMENT AND 1 TERM PROJECT BASED ON DATA ANALYSIS METHODS IN THIS COURSE. Reading materials before attending the course is expected. The purpose of all assignments is to apply the theories and concepts discussed in class to the real world. Assignments will be prepared by groups (Up to 4 students).
The following scale will be used in the evaluation of all criteria. The criteria are presented with point values under the scale.
This criterion is not available or significantly below expectations and/or entirely irrelevant given the context. - Insufficient (0%)
This criterion is somewhat lacking and does not fully align with the context. - Mediocre (40%)
This criterion is satisfactorily developed and aligns with an acceptable standard in the context. - Good (60%)
This criterion is well-developed and professionally executed, fitting the context. - Very good (80%)
The development of this criterion is outstanding and fits the context perfectly. - Excellent (100%)
ASSIGNMENT
CRITERIA USED FOR THE ASSESSMENT:
Research Question: (20 points)
Theory and Literature Review: (70 points)
Data Collection: (10 points)
CRITERIA USED FOR THE ASSESSMENT OF TERM PROJECT
Abstract and Introduction: (20 points)
Methodology: (10 points)
Explanation of Empirical (Estimation) Results: (20 points)
Discussion of Results based on Theory and Articles used in Literature Review Section: (20 points)
Policy Implications: (10 points)
Conclusions: (10 points)

Language of Instruction

English

Course Policies and Rules

1. Plagiarism of any type will result in disciplinary action.
2. Attending at least 70 percent of lectures is mandatory.

Contact Details for the Lecturer(s)

erdost.torun@deu.edu.tr

Office Hours

Wednesday, 12:00 - 13:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Tutorials 0 0 0
Preparation for midterm exam 1 5 5
Preparations before/after weekly lectures 12 3 36
Preparation for final exam 1 10 10
Final 1 1,5 2
Midterm 1 1,5 2
Quiz etc. 0 0 0
TOTAL WORKLOAD (hours) 97

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.1444334434
LO.2442244444
LO.3422333