An introduction to methods and tools useful in decision-making in the financial industry, including: macroeconomic event studies, analysis of term structures, Morningstar equity data, style analysis, credit card receivables, trading analytics, execution algorithms, etc. This course blends easy-to-use statistical tools with complex machine learning tools and algorithms to equip the participants with the requisite skill set in analyzing data.
Financial Analytics (BAN 807)
2021 Spring
Sabancı Business School
Business Analytics(BAN)
3
10
Erdinç Akyıldırım erdinc.akyildirim@sabanciuniv.edu,
Click here to view.
Doctoral, Master
--
Click
here
to view.
| Programs\Type | Required | Core Elective | Area Elective |
| Masters in Finance - Non Thesis | * |
CONTENT
PROGRAMME OUTCOMES
1. Develop the ability to use critical, analytical, and reflective thinking and reasoning
2. Reflect on social and ethical responsibilities in his/her professional life.
3. Gain experience and confidence in the dissemination of project/research outputs
4. Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments.
5. Communicate effectively by oral, written, graphical and technological means and have competency in English.
6. Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating.
1. Develop, interpret and use statistical analyses in decision making.
1. Demonstrate knowledge of financial theory as well as practice and to be able to display critical evaluation of the knowledge.
2. Comprehend the international financial markets, practices, and accounting standards.