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Program Type: 
Thesis
Non Thesis
Course Code: 
FE 514
P: 
3
Lab: 
0
Credits: 
3
ECTS: 
10
Course Language: 
English
Course Objectives: 

It is aimed for students to comprehend and use data analysis techniques, single and multivariate models, estimation, hypothesis testing, analysis of variance, regression, correlation analysis and performance of fitness tests in portfolio optimization, capital pricing models, arbitrage pricing models and factor models.

Course Content: 

Application of data analysis techniques, single and multivariate models, estimation, hypothesis testing, analysis of variance, regression, correlation analysis and fitness test performances in portfolio optimization, capital pricing models, arbitrage pricing models and factor models

Teaching Methods: 
1: Anlatım, 2: Soru-Cevap, 3: Tartışma, 4: Benzetim, 5: Vaka Çalışması
Assessment Methods: 
A: Sınav, B: Sunum C: Ödev, D: Proje, E: Laboratuar

Vertical Tabs

Course Learning Outcomes

Course Learning Outcomes Program Learning Outcomes Teaching Methods Assessment Methods
Understanding of data analysis techniques   1,2,3 1,2
Achieving proficiency in forecasting with univariate and multivariate models   1,2,3 1,2
Understanding hypothesis testing, analysis of variance and correlation   1,2,3 1,2

Course Flow

COURSE CONTENT
Week  Topics Study Materials
1 Introduction to the course  
2 Hypothesis testing  
3 Parametric and non-parametric tests  
4 Factor analysis  
5 Software applications  
6 Discriminant and group analysis  
7 Software applications  
8 Midterm  
9 Neural networks and their applications  
10 Simulation techniques  
11 Indeterminate regression analysis  
12 Stochastic Boundary Analysis  
13 Boundary analysis applications  
14 Introduction to Chaos Theory  
15 An overview  
16 Final  

Recommended Sources

RESOURCES
Course Note  
Others Resources Anderson, D.R. & Sweeney, D.J. & Williams, T.A., Essentials of Statistics for Business and Economics, 6th Ed., South-Western Cengage-Learning, 2010

Johnson, R.A., & Wichern, D.W, Applied Multivariate Statistical Analysis, 2th Ed., Prentice-Hall, 1998

Kalaycı, S., SPSS Uygulamalı Çok Değiskenli Istatistik Teknikleri, 1st Ed., Asil Yayın Dagıtım, 2005

Day, A.L., Mastering Risk Modeling, 2th Ed., Prentice-Hall, 2009

Shone, R., An Introduction to Economic Dynamics, 2th Ed.,  Cambridge University Press, 2003

Assessment

ASSESSMENT
IN-TERM STUDIES NUMBER PERCENTAGE
Mid-Term 1 100
Assignment    
Final Exam 1 100
  Total 100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL
GRADE
  50
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL
GRADE
  50
  Total 100

Course’s Contribution to Program

Contribution of Course to Program Outcomes
No Program Learning Outcomes Contribution
1 2 3 4 5  
1 To be able to develop and deepen current and advanced knowledge in the fields of Finance and Economics and its sub-branches at the level of expertise with original thought and research; has the ability to reach new definitions that will bring innovation to the field.     X      
2 Having the ability to analyze and evaluate information related to more than one field; Based on this information, they have the competence to independently plan and conduct scientific research in accordance with academic rules.     X      
3 Has the ability to transfer the knowledge she has to the relevant people with theoretical and practical principles     X      
4 His scientific articles in various fields, especially in the fields of Finance, Economics, Statistics, Computer; expands the limits of knowledge by preparing it individually or as a team and publishing it in national and international refereed journals or by producing an original work; Makes presentations at national and international meetings.       X    
5 With her English proficiency, she follows the knowledge and developments in her field at an international level and communicates with her colleagues.     X      
6 Uses computer software and information and communication technologies required by related fields at an advanced level.         X  

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION  
 
Activities Quantity Duration (Hour) Total Workload (Hour)  
Course Duration (Including the exam week: 15x Total
course hours/week)
16 3 48  
Hours for off-the-classroom study (Pre-study, practice,
review/week)
16 5 80  
Homework 10 6 60  
Mid-term 1 20 20  
Final 1 40 40  
Total Work Load     248  
Total Work Load / 25 (h)     9,92  
ECTS Credit of the Course     10