Assessment of Master Thesis – Academic Consultant
Study programme:Quantitative Methods in Economics Field of study:Quantitative Economic Analysis
Academic year:2020/2021
Master Thesis Topic:A Comparative Study of Financial Time Series Forecasting Using Machine Learning and Traditional Statistical Methods – An Application To Stock Market Data
Author’s name:Mesut Yasar Ozturk
Ac. Consultant’s Name:doc. Ing. Tomáš Formánek, Ph.D.
Opponent:Ing. Petra Tomanová, MSc
Criterion Mark
(1–4)
1. Clarity and comprehensibility of the thesis topic and aims 1
2. The extent and relevance of the description of the current state of knowledge 2
3. The complexity of the thesis topic 1
4. Method adequeteness for solving the given issue, correctness of the choice and use 1
5. The extent, quality and precision of the result description 2
6. Relevance and correctness of the result discussion 1
7. Factual contribution of the thesis result 1
8. Information source relevance and citation correctness 1
9. Logical structure and cohesion among individual parts 1
10. Grammar, linguistic style, terminology and overall arrangement 1
11. Student’s initiative and cooperation with the supervisor 2
12. The use of analytical and data processing methods 1
13. Meeting the principles of ethics and sustainability 1
14. Critical and creative thinking 1
Comments and Questions:
This MT has been in production since September 2018. This makes for my longest MT supervision I have executed so far. Nevertheless, the final version of the MT submitted is a sound text with good theoretical and empirical grasp of financial time series analysis. The author has a sound professional background in financial market operations, which is noticeable and clearly helps the quality of the text.
The theoretical part of this MT covers multiple approaches to forecasting financial time series – both
“classical” statistical methods and various machine learning approaches are covered. The theoretical discussion is extensive and well structured, yet some topics are discussed relatively briefly. The empirical part is well structured and follows logically from the theoretical introduction. I also highly appreciate the fact that all the Python code used in this MT is shared publicly (zenodo, GitHub).
I recommend this thesis for defense, with a suggested grade: Excellent
Conclusion: The Master Thesis is recommended for the defence.
Suggested Grade: 1
Date: 08/05/2021 doc. Ing. Tomáš Formánek, Ph.D.
Academic Consultant
