Investment Decision-Making Methodology
FP-PmirKAcad. year: 2019/2020
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
Learning outcomes of the course unit
Student must be able to clearly specify basic steps and have, at least, elementary understanding of the very nature of required data and of the consequences of his/her decisions. Important aspect is the inevitable complexity of a decision. Predictions as the very basic investment-related task. Description of the basic types of investments. Description of goal functions used to evaluate investments, risk, profit, and liquidity. Brief description of characteristics of different types of investments and their evaluations in relation with trade offs
The following skills and knowledge are the very basics - high school level (geometry), university level (ability to for a set of linear equations and solve them using a computer, linear programming, elementary knowledge of statistics). Elementary knowledge of fuzzy logic is desirable (definition of a fuzzy set, intersection and union of fuzzy sets, a conditional statement). Time series, elementary understanding of numerical filtration
Recommended optional programme components
Recommended or required reading
Dohnal, M., Kučerová, V. Metody investičního rozhodování, CERM, Brno, 2005, ISBN 80-214-3133-4 (CS)
Raclife, R. C. Investment: Concepts, Analysis, Strategy, Harper Collins College Publishers, 1994 (EN)
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)
Planned learning activities and teaching methods
Lectures consist of an explanation of basic principles, methodology of the discipline, problems and their solutions.
Assesment methods and criteria linked to learning outcomes
The first exam is based on a written test only. A student fails is he/she does not reach the limit 50 points out of 100. The second exam is written and / or oral.
Language of instruction
The course is divided into the following parts according to their topics:
" 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
The main goal of the course is to integrate the knowledge gained in the course of whole university study, especially in mathematics, statistics, operational research, finance, micro and macroeconomics, for solution of investment-related tasks. The emphasis is placed on 1) formalisation of specific tasks and their consecutive computer-aided solution 2) Interpretations of available, mostly very vague, pieces of information.
Specification of controlled education, way of implementation and compensation for absences
No apriori conditions