Analytics of Finance

Data is displayed for the academic year: 2024./2025.

Lecturers

Prof.
Zvonko Kostanjčar
PhD
Asst. Prof.
Stjepan Begušić
PhD

Laboratory exercises

Tomislav Kovačević
PhD

Course Description

This course covers the main quantitative methods of finance. It covers three broad sets of topics: financial econometrics, statistical machine learning for financial applications and dynamic programing. This course gives a comprehensive introduction to financial economics, and particularly asset pricing. Provides the foundation for the main analytical techniques and quantitative methods necessary in the financial industry Examples of applications include portfolio optimization, risk management, and proprietary trading.

Study Programmes

University graduate
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Learning Outcomes

  1. Define main notions in the quantitative finance
  2. Explain mathematical backgrounds of main quantitative methods in finance
  3. Define and differentiate the types of financial data
  4. Differentiate between different asset pricing models
  5. Explain the basics of portfolio theory and apply portfolio optimization methods
  6. Justify the adequacy of statistical methods and machine learning algorithms for applications in quantitative finance

Forms of Teaching

Lectures

Lectures will take place for 3 hours a week.

Laboratory

Laboratory exercises are organized as projects.

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Seminar/Project 25 % 40 % 25 % 40 %
Mid Term Exam: Written 0 % 30 % 0 %
Final Exam: Written 0 % 30 %
Exam: Written 50 % 60 %
Comment:

Week by Week Schedule

  1. Introduction to finance. Course organization.
  2. Financial markets and instruments.
  3. Variables and data in finance.
  4. Asset return models.
  5. -
  6. Risk modelling and estimation.
  7. Guest lecture.
  8. Midterm exam
  9. Introduction to portfolio theory.
  10. Asset pricing.
  11. Financial reports and valuation.
  12. Approaches to investing and trading strategies.,
  13. Guest lecture.
  14. Guest lecture.
  15. Final exam

Literature

(.), Statistics and Data Analysis for Financial Engineering, David Ruppert, Springer, 2011,
(.), Advances in Financial Machine Learning, De Prado, Marcos Lopez, Wiley; 1 edition, 2018,
(.), The Econometrics of Financial Markets, Campbell, John Y., Andrew W. Lo, and A. Craig MacKinlay, Princeton, NJ: Princeton University Press, 1996.,
(.), Asset Pricing, Cochrane, John H, Revised ed. Princeton, NJ: Princeton University Press, 2005.,

General

ID 222444
  Winter semester
5 ECTS
L1 English Level
L1 e-Learning
45 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory
0 Physical education excercises

Grading System

89 Excellent
76 Very Good
63 Good
50 Sufficient

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