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Performance Evaluation of Real Estate Investment and Mutual Funds
Janková, Zuzana ; Novotná, Veronika (referee) ; Rejnuš, Oldřich (advisor)
Diploma thesis deals with the evaluation and the comparison of the performance of mutual funds and investment funds with a focus on the real estate sector. The essence and principles of mutual funds, ETF and REIT are presented, and the resulting weaknesses and advantages. According to the selected indicators, the profitability, riskiness and expense of the investment opportunities are examined and investment recommendations for management of an investment company and potential retail investors are established.
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The Application of Fuzzy Logic for Rating of Suppliers
Rusňáková, Alexandra ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The master's thesis deals with the evaluation of suppliers for the needs of the company MOTOSTYLE PLANET s.r.o using the knowledge of advanced decision-making methods. The fuzzy logic method used is solved using MS Excel and MATLAB. The model is built on the basis of criteria formed for the needs of the company and in the conclusion it pronounces a recommendation in the selection of the supplier.
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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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Evaluating an appropriate investment strategy using fuzzy logic
Macharová, Aneta ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
This diploma thesis deals with the use of fuzzy logic in evaluating a suitable investment strategy for those interested in investing. Models created in MS Excel and MathWorks MATLAB will be used for this evaluation. The first part of the thesis presents the theory that is needed to understand the addressed problematics. The second part presents a selected company for which the work is processed, and the final part contains models, results and proposals found through evaluation via fuzzy logic.
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Risk of Choosing a Supplier Using Fuzzy Logic
Vyskočilová, Monika ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The thesis deals with the design of a model used for evaluation and selection of fire-retardant footwear suppliers for the company Požární bezpečnost s. r. o. The thesis includes a summary of the theoretical foundation for processing the work, a presentation of the selected company and a draft of an evaluation model that assesses the contractor based on the chosen criteria and makes it easier for the company to make their decision. The model is created by using fuzzy logic in Microsoft Excel and MATLAB programs.
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The Application of Fuzzy Logic for Rating of Suppliers
Boros, Adrián ; Podešva, Lukáš (referee) ; Janková, Zuzana (advisor)
The master’s thesis deals with the design and implementation of decision models for the evaluation and subsequent selection of suppliers of powder paints for the company Kenzel s.r.o. Decision models are created in MS Excel and MathWorks MATLAB and use the principles of fuzzy logic. The thesis describes the theoretical basis of the work, the current state of the company and the implementation of both proposed models. Part of the work is also the selection of evaluation criteria on the basis of which the evaluation of selected suppliers takes place.
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The Application of Fuzzy Logic for Rating of Suppliers for the Firm
Froehling, Kryštof ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The diploma thesis deals with the application of the theory of fuzzy logic in the evaluation of client translation commissions for a foreign language text. This fuzzy model is used for better selection of orders and faster allocation of human resources for specific orders. The fuzzy model is composed of multi-valued decision-making criteria that are essential for the company. The model is processed in MS Excel using VBA and MathWorks MATLAB.
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Risk of Choosing a Supplier Using Fuzzy Logic
Sekáč, Jan ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The diploma thesis is focused on analysis and evaluation of supplier risk using the theory of fuzzy logic. The analysis and evaluation of suppliers' risks was compiled for the company PROBYT REAL s.r.o., which is looking for a supplier of construction work for the reconstruction of its building. Two decision models based on the principles of fuzzy logic were created in this work. The models were processed in MS Excel and MATLAB software. The selection of the least risky supplier is based on the results of the models.
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Application of Fuzzy Logic for Evaluating Investments in Stock Markets
Šmerda, Patrik ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The diploma thesis focuses on the application of fuzzy logic in constructing a decisionmaking model for evaluating investment opportunities in stock markets, with a specific emphasis on the selection of exchange-traded funds. The model is based on essential criteria that play a crucial role in the efficient selection of funds. These criteria are implemented in two decision-making models that have been developed using Microsoft Excel and MATLAB tools. The results obtained from these models provide a solid foundation for further steps in the investment decision-making process.
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