Advanced Methods of Analyses and Simulation
FP-RpmamPAcad. year: 2017/2018
The content of the subject is to make students familiar with the methods of analyses and simulation techniques (fuzzy logic, artificial neural networks, and genetic algorithms) by the way of explanation of the principles of these theories and their resulting applications in managerial practice.
Learning outcomes of the course unit
The obtained knowledge and skills of the subject will enable the graduates the top and modern access in the processes of analyses and simulation in the national economy and private sector, organizations, firms, companies, banks, etc., especially in managerial, but also in economical and financial sphere.
The knowledge in the area of math (linear algebra, arrays, analyses of functions, operations with matrixes) statistics (analysis of time series, regression analyses, the use of statistical methods in economy), operational analysis (linear programming), financial analyses and planning (the analyses of profits and costs, cash flow, value and bankruptcy model).
Recommended optional programme components
Recommended or required reading
DOSTÁL, P. Pokročilé metody analýz a modelování v podnikatelství a veřejné správě. CERM. CERM. Brno: CERM Akademické nakladatelství, 2008. 340 p. ISBN: 978-80-7204-605-8. (CS)
DOSTÁL, P.: Advanced Decision making in Business and Public Services, Akademické nakladatelství CERM, 2011 Brno,ISBN 978-80-7204-747-5. (EN)
DOSTÁL, P, RAIS, K., SOJKA, Z.: Pokročilé metody manažerského rozhodování, Praha Grada, 2005., ISBN 80-247-1338-1. (CS)
THE MATHWORKS. MATLAB – User’s Guide, The MathWorks, Inc., 2011. (EN)
FANTA, J.: Technologie umělé inteligence na kapitálových trzích, UK Praha, 1999, 92 s., ISBN 80-7184-8661. (CS)
RAIS, K., SMEJKAL,V.: Řízení rizik, Grada, 2004, 274 s., ISBN 80-247-0198-7. (CS)
HERBST,F.: Analyzing and Forecasting Futures Prices, John Wiley & Sons Inc., 1992, 238 s., ISBN 0-471-53312-2. (EN)
ALTROCK,C.: Fuzzy Logic &Neurofuzzy – Applications in Business & Finance, Book & Cd Edition, 1996, 375 s., ISBN 0-13-591512-0. (EN)
GATELY, E.: Neural Network for Financial Forecasting, John Wiley & Sons Inc., 1996, 169 s., ISBN 0-471-11212-7. (EN)
DAVIS,L.: Handbook of Genetic Algorithms, Int. Thomson Com. Press, 1991, 385 s., ISBN 1-850-32825-0. (EN)
PETERS, E.: Fractal Market Analysis – Applying Chaos Theory, John Wiley & Sons Inc., 1994, 315 s., ISBN 0-471-58524-6. (EN)
REBEIRO,R.R., ZIMMERMANN,H.J.: Soft Computing in Financial Engineering, Spring Verlag Company, 1999, 509 s., ISBN 3-7908-1173-4. (EN)
Planned learning activities and teaching methods
The course contains lectures that explain basic principles, problems and methodology of the discipline, and exercises that promote the practical knowledge of the subject presented in the lectures.
Assesment methods and criteria linked to learning outcomes
To obtain a classified credit it will be required:
1) Active participation in the exercises, i.e. processing of at least 4 of the 5 thematic tasks in the individual exercises (1. FL Excel, 2. FL MATLAB, 3. NN, 4. GA, 5. theory of chaos).
2) At least 5 points from the written semester project (max. 10 points). The scope of the project will be about 8 - 12 pages with an individual focus of the student on practical problems leading to the solution using fuzzy logic theory, artificial neural networks or genetic algorithms. Details of the project will be presented at the first exercise and the work must be submitted by the end of the 10th semester week.
3) Passing the final test to a minimum of 10 points, this test is classified by ECTS. Its implementation is a written form with closed questions and a score of 0-20 points. A: 20-19; B: 18-17; C: 16-15; D: 14-13; E: 12-10; F: 9-0.
Language of instruction
1. Fuzzy logic (FL): To be familiar with the basic notions and fuzzy logic rules, creation of models. The presentation of cases of application of fuzzy logic in decision making processes e.g. managerial and investment decision making, prediction, etc.
2. Artificial neural networks (ANN): To be familiar with the basic notions in the area of artificial neural networks, presentation of the notation perceptron, multilayer neural network and their parameters. The applications cover investment decision making, estimations of the price of products, real properties, evaluation of value of client, etc.
3. Genetic algorithms (GA): To be familiar with the principles of genetics, the analogy between nature and math description that enables the solution of decision making of problems. The use in the area of optimization of wide spectrum of problems is mentioned - the optimization of investment strategy, production control, cutting plans, curve fitting, the solution of traveling salesman, cluster analyses, etc.
4. Chaos theory: The theory deals with the possibilities of better description of economic phenomena than the classical methods do. The notion chaos, order and fractal are clarified, the use of this theory to determinate the level of chaos of measured and watched system is mentioned
5. Data mining: The notion data mining, the definition of aims, the selection of methods of simulation, sources and preparation of data, creation of models, their verification, evaluation, implementation and maintenance are mentioned there. The presentation of the cases of the use for strategy of cooperation with customer, direct mailing, etc
6. Simulation: The presentation of the notion system and its identification and simulation. The description of the use of FL, ANN and GA during the process of simulation of decision making processes in enterprise sphere.
The aim of the course is to get acquainted with some advanced and non-standard methods of analysis and simulation techniques in economy and finance by the method of explanation of these theories, to become familiar with these theories and their use.
Specification of controlled education, way of implementation and compensation for absences
Attendance at lectures is not checked. Participation in the exercises is mandatory and is systematically checked. The student is obliged to excuse their absence. An absence must be compensated by processing the missed assignment and its presentation to the instructor in the next exercise. For the entire semester, the student has to write at least 4 of 5 assignments, either on a seminar, or individually with a subsequent personal presentation to the instructor.