Uncertainty and Information in Finance

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	20
Assignment	6	20
Final Exam	1	60
	Total	100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		60
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		40
	Total	100

Course’s Contribution to Program

Contribution of Course to Program Outcomes
No	Program Learning Outcomes	Contribution
No	Program Learning Outcomes	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

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