IAM756 TEXT ANALYSIS APPLIED TO FINANCE
| Course Code: |
9700756 |
| METU Credit (Theoretical-Laboratory hours/week): |
3(3-0) |
| ECTS Credit: |
8.0 |
| Department: |
Institute Of Applied Mathematics |
| Language of Instruction: |
English |
| Level of Study: |
Graduate |
| Course Coordinator: |
|
| Offered Semester: |
Spring Semesters. |
Course Content
This course introduces basic text analytics models used in the finance industry to link unstructured text data to financial market data. The covered models and algorithms include the bag-of-words, term frequency-inverse document frequency, naive Bayes, BERT, k-means clustering, neural networks, and recurrent neural networks. These models are implemented on two sets of data: Financial Times and Xueqiu news articles.Applications will be done in Python. The first three weeks of the course consistof tutorials aimed at developing programming skills in Python - no prior experience is assumed.
