MATH 321 | Course Introduction and Application Information - Department of Mathematics

Course Name

Introduction fo Mathematical Finance

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
MATH 321	Fall/Spring	3	0	3	5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	face to face
Teaching Methods and Techniques of the Course	Problem Solving Lecture / Presentation
National Occupation Classification	-
Course Coordinator	-
Course Lecturer(s)	Doç. Dr. Cemal Murat ÖZKUT
Assistant(s)	Araş. Gör. Merve KAHRAMAN ARİMAN Araş. Gör. Sıla Selenay KOÇ

Course Objectives	The aim of this course is to provide students with a strong foundation in financial mathematics, focusing on core principles such as the time value of money, portfolio optimization, and risk management. The course integrates practical applications in finance through Python-based tools, gradually introducing advanced concepts like big data analytics, machine learning, and neural networks as students build their understanding of fundamental financial models and techniques.
Learning Outcomes	The students who succeeded in this course; will be able to analyze foundational knowledge of key financial mathematics concepts, including the time value of money, portfolio optimization, and risk-return analysis. will be able to describe the structure of financial markets, financial instruments, and risk management principles. will be able to simulate financial models and solve practical problems using Python-based tools. will be able to analyze financial data, evaluate investment strategies, and discuss complex scenarios through techniques such as yield curve analysis and portfolio optimization. will be able to use their knowledge to design and implement scalable financial applications, while also exploring advanced concepts like machine learning and big data analytics in later stages, ensuring a well-rounded understanding of both theory and practice.
Course Description	This course provides a comprehensive introduction to financial mathematics, emphasizing practical applications of quantitative finance. Students will explore topics ranging from time value of money and portfolio optimization to advanced neural network models and big data analytics.
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses
	Supportive Courses	X
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Introduction to Financial Mathematics, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
2	Progressions and Statistical Measures in Finance, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
3	Time Value of Money (TVM): Simple and Compound Interest, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
4	Time Value of Money (TVM): Simple and Compound Interest, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
5	Introduction to Bonds and Yield Curves, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
6	Efficient Market Hypothesis (EMH): Strong, semi-strong, and weak forms, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
7	Capital Budgeting, Depreciation, Sinking Fund Analysis, Break-Even Analysis and Leverage, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
8	Introduction to Derivatives: Futures and Options, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
9	Midterm Exam
10	Advanced Derivatives: Futures, Forwards, Swaps and Risk Management, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
11	Risk and Return Analysis, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
12	Advanced risk measures: Value at Risk (VaR) and Conditional Value at Risk (CVaR), Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
13	Deep Learning in Financial Applications, Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
14	Deep Learning in Financial Applications (cont.), Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
15	Deep Learning in Financial Applications (cont.), Python Application	Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)
16	Final Exam

Course Notes/Textbooks

- Alhabeeb, M. J..”Mathematical finance”. (Wiley, 2011)

ISBN 978-0-470-64184-2

- Burns, B., Beda, J., & Hightower, K. “ Kubernetes: Up and Running.” 3rd edition. (O'Reilly Media, 2019).

ISBN: 9781098110208

Suggested Readings/Materials

- Research articles on machine learning applications in finance.

- Online documentation for tools: NumPy, Pandas, Scikit-Learn, Apache Spark

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	1	20
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	50
Total

Weighting of Semester Activities on the Final Grade	2	50
Weighting of End-of-Semester Activities on the Final Grade	1	50
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	3	42
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	1	5	5
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	25	25
Final Exam	1	30	30
		Total	150

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To be able to have a grasp of basic mathematics, applied mathematics or theories and applications of statistics.	-	-	-	-	-
2	To be able to use advanced theoretical and applied knowledge, interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the fields of mathematics or statistics.	-	-	-	-	-
3	To be able to apply mathematics or statistics in real life phenomena with interdisciplinary approach and discover their potentials.	-	-	-	X	-
4	To be able to evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach and develop positive attitude towards lifelong learning.	-	-	X	-	-
5	To be able to share the ideas and solution proposals to problems on issues in the field with professionals, non-professionals.	-	-	-	-	-
6	To be able to take responsibility both as a team member or individual in order to solve unexpected complex problems faced within the implementations in the field, planning and managing activities towards the development of subordinates in the framework of a project.	-	-	-	X	-
7	To be able to use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge.	-	-	-	-	-
8	To be able to act in accordance with social, scientific, cultural and ethical values on the stages of gathering, implementation and release of the results of data related to the field.	-	-	-	-	-
9	To be able to possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also environmental protection, worker's health and security.	-	-	-	-	-
10	To be able to connect concrete events and transfer solutions, collect data, analyze and interpret results using scientific methods and having a way of abstract thinking.	-	-	-	-	-
11	To be able to collect data in the areas of Mathematics or Statistics and communicate with colleagues in a foreign language.	-	-	-	-	-
12	To be able to speak a second foreign language at a medium level of fluency efficiently.	-	-	-	-	-
13	To be able to relate the knowledge accumulated throughout the human history to their field of expertise.	-	-	-	-	-