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Analysis and Prediction of Foreign Exchange Markets by Chaotic Attractors and Neural Networks
Pekárek, Jan ; Dostál, Petr (referee) ; Budík, Jan (advisor)
This thesis deals with a complex analysis and prediction of foreign exchange markets. It uses advanced artificial intelligence methods, namely neural networks and chaos theory. It introduces unconventional approaches and methods of each of these areas, compares them and uses on a real problem. The core of this thesis is a comparison of several prediction models based on completely different principles and underlying theories. The outcome is then a selection of the most appropriate prediction model called NAR + H. The model is evaluated according to several criteria, the pros and cons are discussed and approximate expected profitability and risk are calculated. All analytical, prediction and partial algorithms are implemented in Matlab development environment and form a unified library of all used functions and scripts. It also may be considered as a secondary main outcome of the thesis.
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Application of the Fibonacci sequence in asset markets
Hájek, Štěpán ; Soukup, Alexandr (advisor)
This bachelor thesis deals with ,how says the title ,Application of the Fibonacci sequence in asset markets. Aim of the thesis itself is preview of the main technics of aúplication of the Fibonacci sequence on real markets.
In the teoretic part of thesis is eplained Fibonacci sequence everywhere around us, not only on markets. Explaind are also basic applications of Fibonacci sequence on various markets.
In the solution of particular problem were attached technics used by proffesional traders and verified by myself. We will be able to identify levels ,which markets tend to use as a support or resistance. We will review the reasearch´s productivness of particular Aápplication of the Fibonacci sequence.
Based on the reasearch ,we will have basic idea about tool, that is beinng used by traders all around the world. Goal of the thesis is to provide knowledge about Fibonacci sequence and also the application of it.
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Analysis and Prediction of Foreign Exchange Markets by Chaotic Attractors and Neural Networks
Pekárek, Jan ; Dostál, Petr (referee) ; Budík, Jan (advisor)
This thesis deals with a complex analysis and prediction of foreign exchange markets. It uses advanced artificial intelligence methods, namely neural networks and chaos theory. It introduces unconventional approaches and methods of each of these areas, compares them and uses on a real problem. The core of this thesis is a comparison of several prediction models based on completely different principles and underlying theories. The outcome is then a selection of the most appropriate prediction model called NAR + H. The model is evaluated according to several criteria, the pros and cons are discussed and approximate expected profitability and risk are calculated. All analytical, prediction and partial algorithms are implemented in Matlab development environment and form a unified library of all used functions and scripts. It also may be considered as a secondary main outcome of the thesis.
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