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Fundamental Anylysis of Selected Stocks
Dvořáček, Martin ; Henešová, Šárka (referee) ; Sojka, Zdeněk (advisor)
This thesis deals with the fundamental analysis of stocks selected Telefónica Czech Republic a.s. This paper contains a theoretical description of the use of fundamental analysis including a description of the specific models use dinfundamental analysis as a tool for determining the intrinsic value of traded shares. In the practical part I apply this analysis to the selected share with the help of publicly available information.
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PERSPECTIVES OF IPO DEVELOPMENT IN CENTRAL AND EASTERN EUROPEAN REGION
Plottová, Sylvia ; Bartoš, Vojtěch (referee) ; Kulhánek, Lumír (referee) ; Myšková, Renáta (referee) ; Meluzín, Tomáš (advisor)
The main aim of this dissertation is to identify the factors influencing the decision-making of enterprises on entering the capital market in selected CEE countries and formulate recommendations for further development of this form of financing. The key methodological tool is the collection of primary data by means of a questionnaire in which respondents (usually in the CFOs position) expressed their views on the issues related to internal and external factors affecting IPO activity. The results of the questionnaire survey show that the strongest motives for IPO implementation are the ability to raise capital to finance investment opportunities, improve publicity and image of the company, reduce debt, and be recognized by a relevant financial community as an important company. Among the barriers that most affect IPO implementation are the obligation to disclose company information that is key to a competitive advantage, asymmetry of information between external investors and the company, the interest in retaining decision-making control over the company, and the existence of alternative administratively less complex capital resources at the time of implementation of IPO. As per CFOs macroeconomic factors that have the greatest impact on IPO are favorable conditions in the stock market, favorable conditions in the sector in which the company operates, favorable GDP growth, and the use of banking loan at a relatively low interest rate. The results of the dissertation are the basis for the formulation of recommendations for potential IPO candidates.
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The Use of Artificial Intelligence on Stock Market
Skočík, Michal ; Pekárek, Jan (referee) ; Budík, Jan (advisor)
Diploma thesis is focused on problematics of artificial neural networks and their usage on capital markets. There is a software created as a part of this diploma thesis which can load input data and create neural network that serves for share price forecast. This program is created in numerical computing environment MATLAB. Created neural network is tested under simulation of business model. Results are discussed upon examination of results of simulation.
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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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Expert System for Decision-Making on Stock Markets Using Investor Sentiment
Janková, Zuzana ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Chramcov, Bronislav (referee) ; Dostál, Petr (advisor)
The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.
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Technical Analysis
Tesař, Petr ; Doubravský, Karel (referee) ; Novotná, Veronika (advisor)
This thesis focused on general characteristics of technical analysis and the use of its instruments to support making decisions when trading in stock market. Within the theoretic part are processed theoretic solutions which are primarily relevant to technical analysis. At the specific stock item is by the help of proposed application consequently implemented the analysis of individual indicators of technical analysis. At the conclusion is compared the profitability and reliability of used indicators.
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