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Prediction of Exchange Rate Movements on Forex
Balog, Miroslav ; Menšík, Jakub (referee) ; Budík, Jan (advisor)
The thesis deals with the possibility of prediction of the exchange rate on forex. The combination of Elliott wave principle and Fibonacci numbers examines to what extent and in what time periods it is possible to predict exchange rate. The thesis use fundamental analysis and MACD oscillator to confirm the accuracy of this prediction.
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Decision-making to Change the Legal Form of a Company and its IPO
Jankechová, Simona ; Horňáková, Dominika (referee) ; Doubravský, Karel (advisor)
The bachelor thesis deals with the initial public offering (IPO), as an external source of funding of business activities. There is the basic definition of the theoretical concepts related to the IPO for example financial market, joint-stock company and IPO. Further the work deals with the analysis of the chosen company by means of analysis of chosen financial indicators and macro economical analysis. Aim of this work is proposal of decision model for the support of decision-making to change the legal form of the company and its IPO. It is basically support of the decision-making, where financial health of the company, macro economical aspects and current situation in the sector are taken into account. Furthermore, there is determined decision model which is used for formulation of main recommendations.
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A Web Interface for the Management of Virtual Portfolio
Bali, Filip ; Hruška, Martin (referee) ; Lengál, Ondřej (advisor)
This thesis designs and implements a web application for managing virtual portfolios. Main goal of the application is visualise and analyze data from stock exchange services API. User can be notified on price change. The application also uses existing methods to predict stock prices and supports the visualization of the user's stock exchange decisions and provides him/her a general overview of them.
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Fundamental Analysis of Numerical Data for Automatic Trading
Huf, Petr ; Szőke, Igor (referee) ; Černocký, Jan (advisor)
This thesis is aimed to exploitation of fundamental analysis in automatic trading. Technical analysis uses historical prices and indicators derived from price for price prediction. On the opposite, fundamental analysis uses various information resources for price prediction. In this thesis, only quantitative data are used. These data sources are namely weather, Forex, Google Trends, WikiTrends, historical prices of futures and some fundamental data (birth rate, migration, \dots). These data are processed with LSTM neural network, which predicts stocks prices of selected companies. This prediction is basis for created trading system. Experiments show major improvement in results of the trading system; 8\% increase in success prediction accuracy thanks to involvement of fundamental analysis.
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Fundamental Analysis for Automatic Trading Systems
Miček, Marek ; Kanich, Ondřej (referee) ; Rozman, Jaroslav (advisor)
This thesis deals with the creation of automatic trading systems which are able to predict market trends for stocks selected in advance. Proper trading strategy of this system is mainly created from the elements of fundamental analysis, such as annual returns of company, it's gains, level of shareholder's equity or total debt. All the stocks are classified by these fundaments, where result of this classification determines whether to buy or sell the stock. For the purpose of this thesis, 5 autamatic trading systems were created in order to compare different approaches to the stock evaluation, managment or diversification of business portfolio. Created systems were properly tested on historical data and, in order to determine their level of complexity, tests were executed in both periods of economic recession and expansion too. All the created systems reported great returns and most of them have potential to generate long-term gains. On the basis of received results, it is possible to make conclusion that fundamental analysis has a high value in the field of automatic trading systems, and it increases the chances of generating a profit.
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Algorithmic Trading Using Artificial Neural Networks
Šeda, Jan ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
The capability to be able to determine the future progression on the worlds stock exchange is an important issue, which has become discernible in the last decades. An important role of this progression lies within the fast advancements in computerized technology.Aforementioned document describes a mechanism used for prediction of the future price of a certain stock. The strategy of trading is build upon this mechanism, and the core of this prediction system is an artificial neural network. Inputs used in this network are indicators derived from technical analysis. This trading system was implemented into historical trades and successfully tested.
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Prediction of Prices in Stock Exchange Trading
Mikulenčák, Roman ; Szőke, Igor (referee) ; Černocký, Jan (advisor)
The work deals with an automatic trading system and adaptive training. Is used both technical and automatic fundamental analyses, therefore as inputs to the neural network is used historical data exchanges and text data from reports. It explains the basics of trading, technical analysis and technical terms. The work deals with technical and fundamental analysis. It contains a description of algorithmic nature, program implementation and experiment with developed trading system. The selected strategy is compared to other approaches.
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