Information Package / Course Catalogue
| Course Code | : İKT386 |
| Course Type | : Area Elective |
| Couse Group | : First Cycle (Bachelor's Degree) |
| Education Language | : Turkish |
| Work Placement | : N/A |
| Theory | : 3 |
| Prt. | : 0 |
| Credit | : 3 |
| Lab | : 0 |
| ECTS | : 5 |
The aim of this course is to enable students to relate artificial intelligence, machine learning, and data analytics methods to financial investment decisions. Within the scope of the course, students are expected to identify financial data sources, analyze market data, evaluate algorithmic decision support systems, understand AI-supported investment strategies, and interpret investment decisions from a risk-return perspective. The course also focuses on helping students evaluate artificial intelligence applications in areas such as equities, mutual funds, foreign exchange, commodities, crypto assets, and portfolio management. By the end of the course, students are expected to critically use AI-supported financial analysis tools, understand the limitations of model outputs, and conduct investment analysis by considering ethics, security, and regulatory dimensions.
This course covers the use of artificial intelligence in financial markets, financial data literacy, introduction to machine learning, investment forecasting models, natural language processing, news and social media sentiment analysis, algorithmic trading strategies, robo-advisory, portfolio optimization, risk management, model performance measurement, explainable artificial intelligence, ethics, data security, and financial regulation. Throughout the course, students learn how to analyze financial data using AI-supported tools, evaluate data-driven decision-making processes in investment strategies, and interpret the opportunities and limitations of AI models in investment decisions.
| 1. | Relate artificial intelligence, machine learning, and data analytics concepts to financial investment decisions. |
| 2. | Identify data types used in financial markets and evaluate the impact of data quality on investment models. |
| 3. | Explain AI-supported financial forecasting, sentiment analysis, and algorithmic trading approaches. |
| 4. | Evaluate investment instruments such as equities, funds, foreign exchange, commodities, and crypto assets from an AI-supported analysis perspective. |
| 5. | Interpret AI-supported portfolio construction, risk management, and performance evaluation processes. |
| 6. | Prepare a professional investment report by analyzing the ethical, security, regulatory, explainability, and model risk dimensions of AI-supported investment models. |
| 1. | Karan, M. B. Yatırım Analizi ve Portföy Yönetimi. Ankara: Gazi Kitabevi. |
| 2. | López de Prado, M. Advances in Financial Machine Learning. Wiley. |
| 3. | Bodie, Z., Kane, A., & Marcus, A. J. Investments. McGraw-Hill Education. |
| 4. | Chan, E. P. Machine Trading: Deploying Computer Algorithms to Conquer the Markets. Wiley. |
| Type of Assessment | Count | Percent |
|---|---|---|
| Practice | 15 | %15 |
| Presentation | 15 | %5 |
| Assignment | 1 | %10 |
| Quiz | 1 | %10 |
| Midterm Examination | 1 | %20 |
| Final Examination | 1 | %40 |
| Activities | Count | Preparation | Time | Total Work Load (hours) |
|---|---|---|---|---|
| Lecture - Theory | 1 | 0 | 35 | 35 |
| Lecture - Practice | 1 | 0 | 20 | 20 |
| Assignment | 1 | 2 | 4 | 6 |
| Presentation | 1 | 0 | 20 | 20 |
| Practice Examination | 1 | 0 | 10 | 10 |
| Quiz | 1 | 4 | 4 | 8 |
| Midterm Examination | 1 | 1 | 9 | 10 |
| Final Examination | 1 | 0 | 20 | 20 |
| TOTAL WORKLOAD (hours) | 129 | |||
PÇ-1 | PÇ-2 | PÇ-3 | PÇ-4 | PÇ-5 | PÇ-6 | PÇ-7 | |
OÇ-1 | 4 | 4 | 5 | 4 | 4 | 4 | 4 |
OÇ-2 | 5 | 3 | 5 | 5 | 5 | 5 | 5 |
OÇ-3 | 5 | 4 | 5 | 5 | 5 | 5 | 5 |
OÇ-4 | 3 | 5 | 5 | 3 | 3 | 3 | 3 |
OÇ-5 | 4 | 4 | 4 | 4 | 4 | 4 | 4 |
OÇ-6 | 5 | 4 | 4 | 5 | 4 | 5 | 5 |