Master thesis mainly focuses on the use of technical analysis for testing stocks of chosen companies. The theoretical part describes background papers necessary for using technical analysis and for creating diversified portfolio. The analytical part describes the selection of several automobile manufacturers from NASDAQ stock market, which will be examined in the following part by several methods of technical analysis. The last part of this thesis is also dedicated to application created for necessary calculations in Matlab and to portfolio composed of investment funds by Erste Bank.

The thesis deals with the optimization of the selected investment portfolio. Solver suggests automated investment model that will use advanced algorithms based on artificial intelligence and principles of technical analysis. Optimization of parameters and verifying the performance of the investment model is realized on historical market data. The result of this thesis is optimized investment model with an emphasis on maximizing profits and stability. The thesis is realized in an environment Python programming language and freely available analytical libraries.

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.

This master's thesis is focused on algorithmic trading on the forex market using artificial neural networks. In the introduction, there are generally described terms concerning the trading. Subsequently, in the following chapters, the thesis describes the theory of neural networks and their possible use. The practical part contains designed business strategies with neural networks. Inputs used in the network are indicators of technical analysis or directly price level. Business strategies have been implemented and tested. In the conclusion, there are summarized findings of individual business models.

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.

The aim of the diploma thesis is portfolio optimalization of middle-sized investor. His assets are placed into repo and shares. Shares are selected from the range of blue chips in czech and german stock market, that means from securities contained in PX index and DAX index. This selection is based on fundamental and technical analysis. The portfolio in foreign currency is hedge against downtrend of exchange rate by knock-out product. The final part of the thesis belongs to appreciation of assembled portfolio.

This work deals with stock market prediction via technical and psychological analysis. We introduce theoretical resources of technical and psychological analysis. We also introduce some methods of artificial intelligence, specially neural networks and genetic algorithms. We design a system for stock market prediction. We implement and test a part of system. In conclusion we discuss results.

This diploma thesis is focused on the general characteristics of the technical analysis, definitions of the information sources and technical analyses. Main attention is given to the particular indicators of the technical analysis. Practical part of this thesis includes applying of the defined indicators in the evaluation of the money market situation.

This master‘s thesis is focused on creating an application, which would suggest an optimal portfolio of shares from SPAD Stock Market Praha. The application is based on the CAPM model, which is also described in this paper. There is a calculation of securities characteristics and specific portfolio diversification is suggested. The application also allows a user to simulate investments based on his requirements.

This master thesis deals with issue of trade on commodity market, especially the gold. It uses the artificial intelligence resources, more accurate non-linear auregressive neural network. The purpose is the prediction of the gold prices by indicators which has impact on the gold.