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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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Automated Weight Tuning for Rule-Based Knowledge Bases
Valenta, Jan ; Pokorný, Miroslav (referee) ; Zbořil, František (referee) ; Jirsík, Václav (advisor)
This dissertation thesis introduces new methods of automated knowledge-base creation and tuning in information and expert systems. The thesis is divided in the two following parts. The first part is focused on the legacy expert system NPS32 developed at the Faculty of Electrical Engineering and Communication, Brno University of Technology. The mathematical base of the system is expression of the rule uncertainty using two values. Thus, it extends information capability of the knowledge-base by values of the absence of the information and conflict in the knowledge-base. The expert system has been supplemented by a learning algorithm. The learning algorithm sets weights of the rules in the knowledge base using differential evolution algorithm. It uses patterns acquired from an expert. The learning algorithm is only one-layer knowledge-bases limited. The thesis shows a formal proof that the mathematical base of the NPS32 expert system can not be used for gradient tuning of the weights in the multilayer knowledge-bases. The second part is focused on multilayer knowledge-base learning algorithm. The knowledge-base is based on a specific model of the rule with uncertainty factors. Uncertainty factors of the rule represents information impact ratio. Using a learning algorithm adjusting weights of every single rule in the knowledge base structure, the modified back propagation algorithm is used. The back propagation algorithm is modified for the given knowledge-base structure and rule model. For the purpose of testing and verifying the learning algorithm for knowledge-base tuning, the expert system RESLA has been developed in C#. With this expert system, the knowledge-base from medicine field, was created. The aim of this knowledge base is verify learning ability for complex knowledge-bases. The knowledge base represents heart malfunction diagnostic base on the acquired ECG (electrocardiogram) parameters. For the purpose of the comparison with already existing knowledge-basis, created by the expert and knowledge engineer, the expert system was compared with professionally designed knowledge-base from the field of agriculture. The knowledge-base represents system for suitable cultivar of winter wheat planting decision support. The presented algorithms speed up knowledge-base creation while keeping all advantages, which arise from using rules. Contrary to the existing solution based on neural network, the presented algorithms for knowledge-base weights tuning are faster and more simple, because it does not need rule extraction from another type of the knowledge representation.
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Petri nets for expert systems
Million, Pavel ; Pohl, Jan (referee) ; Jirsík, Václav (advisor)
Purpose of this master thesis is description of base parts of expert system with using Petri nets. Attention is mainly concentrate to knowledge base, way of storing knowledge. Next parts are describing main different between production base knowledge for planning or diagnostic expert system from Petri nets view. In this thesis conditions of using Petri nets and way of interpretation knowledge for inference mechanism in planning and diagnostic expert system are described. Using of high level Petri nets and language describing Petri nets structure and behaviour are demonstrated in next part of this thesis.
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Expert system for choice of proper method for waste utilization
Fikar, Josef ; Jícha, Jaroslav (referee) ; Dvořák, Jiří (advisor)
This work consists in development of expert system intended for choosing appropriate method of waste processing. The software is created in VisiRule software which is built on Prolog language and is part of WinProlog 4.900 development tool. It also deals with problematics of creating of knowledge base for applications of this type and judging of suitability of possible approaches to creating an expert system for given purpose.
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Scheduling System in Healthcare
Janíčková, Natália ; Kolářová, Jana (referee) ; Sekora, Jiří (advisor)
The Bachelor thesis is dedicated to expert scheduling systems in healthcare. The thesis describes all parts of system design: algorithm, software tools used in a process, and the developed application itself. The system developed in this thesis generates a schedule of the services that respect requirements given by law, employer, and also employees in the healthcare organizations. Application outputs involve basic overviews for employees and employers.
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User interface for an expert system
Kořínek, Lukáš ; Boštík, Ondřej (referee) ; Jirsík, Václav (advisor)
This bachelor's thesis is about design and implementation of an expert system user interface. Concerned system is a universal rule-based diagnostic expert system called NPS, which has been developed at FEEC BUT. The interface has been created as a bilingual web application based on modern technologies, which might bring the opportunity to reach variety of devices and a broad user audience. It also enhances the system with means of user authorization, history browsing, user management, knowledge management and more. The application runs on faculty's infrastructure and is undergoing active testing by both physical users and automated tests. Theoretical part of the document describes knowledge and expert system problematics, followed by properties of the NPS system and discussion about web technologies (actual trends in the field, client to server communication, testing strategies and security). Practical part then consists of created interface overview, system architecture description, implementation details, testing and steps used to deploy the application on faculty's network infrastructure.
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Expert Systems in the eHealth
Němcová, Michaela ; Provazník, Ivo (referee) ; Sekora, Jiří (advisor)
This work focuses on the use of expert systems in engineering and medicine with the use of eHealth. The aim is the creation of an expert system that utilizes available systems for measuring physiological parameters of a patient, and helps him with the primary examination before visiting the doctor. Part of this work is a description of the problems of expert systems, descriptions of the eHealth and system testing in a doctor’s office. Work created in collaboration with Honeywell.
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Artificial Intelligence in Power Oil Transformers Diagnostics
Janda, Ondřej ; Szabó,, Radek (referee) ; Kratochvíl, Petr (referee) ; Hammer, Miloš (advisor)
This dissertation thesis deals with the application of expert systems and soft computing methods in field of power oil transformers. The main work is divided into theoretical and practical part. First, the theoretical part presents the basic elements of the transformer, and approaches to its diagnosis. The work focused mainly on the diagnostics of the insulation system, and diagnostic methods and approaches in this specific area. Next part describes the basics of expert systems and other soft computing methods such as: fuzzy logic, neural networks, genetic algorithms and their combinations and extensions. At the end of the theoretical part, the possibility of optimization approaches by means of artificial intelligence and its application in fuzzy model optimization are described. The practical part begins with description of the used data file that runs through the entire work. The work is then divided into four parts, namely in parts which deal with the expert system for transformer diagnostics, DGA module, prediction module, and optimization using artificial intelligence. The section describing the expert system gives specific information about the particular expert system. The means and techniques used for constructing given system are described, and then the complete system design and description of all subsystems and modules are presented. The next section describes the developed DGA module and all selected approaches to its implementation and expansion. At the end of the chapter, the results of comparison between all implemented methods are evaluated. The third part deals with the prediction module and describes its design and construction, including description of the main parts which are based on the selected predictive approaches. Also, the predictions of selected quantities from the data file are included. There are two predictive approaches being used: the one step prediction, and the multiple step prediction. The comparison of prediction accuracy and computational cost of given methods is presented at the end of this chapter. The last part deals with the possibilities of optimization using artificial intelligence methods, namely differential evolution, PSO, and genetic algorithms. Both the single-objective and the multi-objective optimization are considered. The methods are compared in a series of synthetic tests and then applied to optimize the fuzzy models of DGA tests from an earlier part of this work. The dissertation also includes chapters: "The Aims", "The Contribution of the Work", and a list of publications, products, and projects of the author.
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Evaluation of Investment with the Usage of Fuzzy Logic
Machová, Kamila ; Šimon, Ondřej (referee) ; Dostál, Petr (advisor)
Master ’s thesis deals with evaluation of investment using advanced methods of analysis and modeling with regards to the best offer. The content of the thesis consists of creating two decision making systems based on fuzzy logic. The thesis also contains a theoretical basics which are necessary for the creation of both systems and evaluating the benefits of the solution.
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Information System Selection
Šebesta, Petr ; doc. Ing. Petr Sodomka, Ph.D., MBA (referee) ; Koch, Miloš (advisor)
The thesis deals with problems of enterprise information system selection. Based on theoretical knowledge in the field of information systems and their development is the analysis of enterprise information system selection and situation on czech market. Solution suggestions consist of optimal enterprise information system project description and description of created expert system knowledge base for pre-selection of enterprise information system.
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