COURSE DESCRIPTION AND APPLICATION INFORMATION

Course Name Code Semester T+A+L (hour/week) Type (C / O) Local Credit ECTS
Special Topics in Industrial Engineering INE 488 Spring 03+00+00 Elective 3 6
Academic Unit: Industrial Engineering
Mode of Delivery: Face to face
Prerequisites: No
Language of Instruction: English
Level of Course Unit: Undergraduate
Course Coordinator: - -
Course Objectives: This course informs undergraduate students about the mathematical foundations of finance theory. The course requires students to have an introductory level of knowledge of finance and optimization concepts. The course aims to solve numerical and computational finance problems using financial engineering tools.
Course Contents: Financial Modeling, Portfolio Optimization, Time Series Modeling, Portfolio Backtesting, Computational Finance, Financial Econometrics
Learning Outcomes of the Course Unit (LO):
  • 1- To be able to apply mathematical modeling and optimization to financial problems
  • 2- Ability to understand financial system mechanism
  • 3- Ability to apply computational finance tools that are required to solve a financial engineering problem
  • 4- Ability to apply programming and AI tools to empirical finance area
Planned Learning Activities and Teaching Methods: Lecture Slides, Face-to-Face and Lab Activities


WEEKLY SUBJECTS AND RELATED PREPARATIONS

WeekSubjectsRelated Preperation
1 Introduction
2 Financial Data and Financial Statistics
3 Financial Securities
4 Financial Time Series Modeling I
5 Financial Time Series Modeling II
6 Portfolio Basics
7 Portfolio Theory
8 Portfolio Optimization
9 Portfolio Performance
10 Asset Pricing
11 Application of Modern Finance with R and AI
12 Application of Modern Finance with R and AI
13 Special Topics in Financial Engineering
14 Special Topics in Financial Engineering


REQUIRED AND RECOMMENDED READING

Introduction to Computational Finance and Financial Econometrics (Eric Zivot, 2021)
Statistics and Data Analysis for Financial Engineering (David Ruppert & David S. Matteson, Springer 2015)
Financial Analytics with R (Mark J. Bennett & Dirk L. Hugen, Cambridge 2016)


OTHER COURSE RESOURCES

Portfolio Optimization: Theory and Application (Daniel P. Palomar, Cambridge 2025)


ASSESSMENT METHODS AND CRITERIA

Semester RequirementsNumberPercentage of Grade (%)
Attendance / Participation 10 10
Project 1 20
Homework Assignments 4 30
Final Exam 1 40
Total: 16 100


WORKLOAD

EventsCountDuration (Hours)Total Workload (hour)
Course Hours14228
Laboratory14114
Project12020
Homework Assigments41560
Final Exam13030
Total Workload (hour):152


THE RELATIONSHIP BETWEEN COURSE LEARNING OUTCOMES (LO) AND PROGRAM QUALIFICATIONS (PQ)

# PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9
LO1                  
LO2                  
LO3                  
LO4