Course detail
Investment Decision-Making Methodology
FP-PmirKAcad. year: 2022/2023
The course concentrates particularly on the following topics:
" Ad hoc identification of meaningful compromises among the most important goals as profit, risk and liquidity
" Interpretation of formal tools (e.g. methods of operational research) studied in the course of previous study into a form suitable for solving investment-related tasks
" Interpretation of available information into a form which allows its formalisation
" Solution of Investments-related tasks
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
" Unsteady state behaviours of a complex stream of investments / money in a complex systems with many sub systems, which are mutually interconnected into an oriented graph. An oriented arc represents either continuous or discontinuous money transfers. A detailed study of a simplified problem in steady state with a mathematical model represented by a set of linear equations.
" Analysis of the very basic investment compromises i.e. a good (meaningful) compromise between risk and profit within an information poor environment where a significant shortage of data is made more complex by vagueness of available data. Such compromises are studied as a multicriterial one and addition criteria e.g. liquidity can be additionally attached.
" A specification of a decision tree based on disintegration of ad hoc decision processes into a set of decisions and set of lotteries with specific emphasis placed on long term investments under conditions of high risks.
" Investments in knowledge economy represented by hi tech companies
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
REŽŇÁKOVÁ, M.; WOUTERS, H.; DOHNAL, M. Equationless qualitative models of science parks: part I, individual scenarios as models solutions. International Journal of Technology Intelligence and Planning, 2012, roč. 8, č. 3, s. 295-306. ISSN: 1740- 2832. REŽŇÁKOVÁ, M.; WOUTERS, H.; DOHNAL, M.; BROŽ, Z. Equationless qualitative models of science parks: part II, optimisation by time sequences of scenarios. International Journal of Technology Intelligence and Planning, 2012, roč. 8, č. 3, s. 307-315. ISSN: 1740- 2832. (CS)
SOHN, S. Y. a J. W. KIM. Decision tree-based technology credit scoring for start-up firms: Korean case, Expert Systems with Applications. 2012, 39 (4), p. 4007-4012. ISSN: 0957-4174. (CS)
Recommended reading
Classification of course in study plans
- Programme MGR-KS Master's
branch MGR-PFO-KS , 1 year of study, winter semester, compulsory
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
Guided consultation in combined form of studies
Teacher / Lecturer