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The Use of Artificial Intelligence to Reduce Risk in the Company
Smija, Jakub ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The diploma thesis deals with the analysis and subsequent evaluation of selected commercial vehicle models from individual manufacturers, using artificial intelligence methods. The introductory chapters focus on the basics of risk science, fuzzy logic and software tools, their purpose is to acquaint readers with the issues addressed. The analytical part of the work contains a description of the selected company and also an introduction of specific suppliers. The main part contains decision criteria for the selection and also a description of the actual solution of the assignment. More specifically, two decision models based on fuzzy logic, which were created using MS Excel and MATLAB. In the end, the results from both models are compared, including the interpretation of the obtained results.
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Evaluation of Investment Risks Using Fuzzy Logic
Žáček, Jakub ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The diploma thesis deals with the evaluation of an investment using fuzzy logic for a specific company. With the help of the created decision-making models, the company will be able to efficiently and quickly evaluate which investment brings the highest benefit. These models follow the criteria that are most important to the company when deciding on an investment. The work also contains theoretical background, which serves as a basis for creating and evaluating models.
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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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Application of Fuzzy Logic for Evaluating Investments in Stock Markets
Kúdela, Lukáš ; Janková, Zuzana (referee) ; Dostál, Petr (advisor)
The diploma thesis deals with the principles of fuzzy logic and its practical application in the evaluation of individual passively managed ETFs as an investment option in the stock market. The model is composed of multivalued decision criteria that can be used to classify a given investment instrument. Based on the evaluation of the considered funds, these outputs will serve as a supporting tool in the further decision-making process. The processing of the model is realized in MS Excel program using VBA and in the MATLAB program.
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The Application of Fuzzy Logic for Rating of Suppliers
Šťáva, Adam ; Hutyra, Pavel (referee) ; Janková, Zuzana (advisor)
This thesis deals with the aplication of fuzzy logic in the evaluation and optimal supplier of transport services using decision models that were created in Microsoft Excel and MathWorks MATLAB. The use of these decision models is to evaluate the services offered by individual suppliers on the basis of established criteria. The company will choose its future optimal supplier according to the final evaluation.
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The Application of Fuzzy Logic for Rating of Employees
Ganzwohl, Jakub ; Coufal, Petr (referee) ; Janková, Zuzana (advisor)
In this diploma thesis, the means of fuzzy logic are used to evaluate the usefulness of employees. For this purpose, a model has been created to help decide whether an employee is worth for the company. The actual solution design is created in MS Excel and MathWorks MATLAB. The subsequent evaluation of the employee is based on the results achieved from these solutions.
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Application of Fuzzy Logic for Evaluating Investments in Stock Markets
Žigo, Tomáš ; Podešva, Lukáš (referee) ; Janková, Zuzana (advisor)
This diploma thesis deals with the evaluation of investments in stock markets through decision support models. Within the work, two decision support systems in the MS Excel and MATLAB programming environments are designed and created, which are implemented using fuzzy logic. The work describes the theoretical basis of the work, work in software environments, and the selection of investments in the stock market. The thesis also contains a definition of evaluation criteria and a description of the creation process of the decision-making system, from which investment recommendations are derived.
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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
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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