The graduation thesis "Analysis of stock-exchange data using AI methods" is focused on the use of neural networks while predicating the exchange-rate movements on Change. The theoretical part is divided into three independent units. The Change matters and the related individual terms are described in the first part. In the second part, the two basic approaches to the stock-exchange data analysis are analyzed, these two approaches being the fundamental and technical analysis. The third, and the last, theoretical part forms an individual unit describing the Artificial Intelligence theory. Particularly the issue of the neuronal networks is described in detail. The practical part seeks the use for the chosen neuronal network GAME. It analyses the chosen YMZ9 market. It focuses on the prediction of the exchange-rate movements using the "sliding window" method. The last chapter summarizes the results and it proves that under certain circumstances it is possible to properly use the neuronal networks both for the prediction of the stock-exchange movements and as one of the corner-stones of the profitable trading system.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:15866 |
| Date | January 2008 |
| Creators | Kutina, Michal |
| Contributors | Jelínek, Jiří, Dvořák, Pavel |
| Publisher | Vysoká škola ekonomická v Praze |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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