|
|
Algorithmic Trading Using Artificial Neural Networks
Chlud, Michal ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.
|
|
|
The Use of Artificial Intelligence on Stock Market
Lajczyk, Pavel ; Budík, Jan (referee) ; Dostál, Petr (advisor)
This master's thesis deals with artificial neural networks and possibilities of their use on stock market. In next chapters of this thesis there are provided design and implementation of stock prices prediction tool. The implementation is done with use of the MATLAB software. The created prediction tool is then tested in a simple trading simulation and achieved results are discussed in the end
|
|
|
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.
|
|
|
Financial Evaluation of a Company
Štoudková, Julie ; Oulehla, Jiří (referee) ; Luňáček, Jiří (advisor)
The bachelor’s thesis will focus on the evaluation of the financial situation of three selected companies from different sectors and their impact on covid measures in the short term. The work will contain a theoretical and practical part. The theoretical part will list all the theoretical background needed to achieve the main goal of the work. In the practical part, an analysis of three companies in the years 2017-2020 will be performed using the methods listed in the theoretical part. This analysis will be complemented by a bankruptcy and creditworthiness model and time series predictions. Subsequently, all the findings and summarized proposals for what companies should focus on effective company management are summarized.
|
|
|
Predictive Control of DHW and Household Heating Using Machine Learning Methods
Necpál, Dávid ; Zemčík, Pavel (referee) ; Materna, Zdeněk (advisor)
The thesis is devoted to the development of a system for the control of domestic hot water (DHW) and heating. The system uses machine learning methods to predict future data - learning and predicting repetitive routines of people in the home, predicting hot water consumption and temperature trends in the home. The work is divided into two parts, where the first part is the control of domestic hot water heating, and the second part is the control of home heating. Both parts of the system use established hardware devices that implement the data collection and the actual control of the given activities. The devices collect water temperatures from the electric boiler, air temperatures from the rooms of the household and information about the presence of people in the household. The measured data is used to create a model for data prediction, which is used for the final decision on whether DHW heating or home heating should be implemented. The first subsystem for DHW heating achieves energy savings of 20 to 24 %, the second subsystem for domestic heating 18 to 30 %. The benefit of the developed system is the possibility of predictive automatic control of domestic hot water heating and heating, which leads to a reduction of energy consumption required for the above-mentioned activities, as well as a possible increase in comfort in the home.
|
|
|
Reservoir Computing for Industrial Applications
Brhel, Jakub ; Bražina, Jakub (referee) ; Kovář, Jiří (advisor)
In this bachelor thesis, the field of reservoir computing and its application in the industrial sector is investigated. The main objective of the thesis is to verify the effectiveness and accuracy of the echo state network method in time series prediction. To achieve this objective, a dataset from a manufacturing machine is used and the echo state network algorithm is implemented. The results are analysed and are also compared with the results of previous studies in the field of reservoir computing.
|
|
|
Financial Evaluation of a Company
Štoudková, Julie ; Oulehla, Jiří (referee) ; Luňáček, Jiří (advisor)
The bachelor’s thesis will focus on the evaluation of the financial situation of three selected companies from different sectors and their impact on covid measures in the short term. The work will contain a theoretical and practical part. The theoretical part will list all the theoretical background needed to achieve the main goal of the work. In the practical part, an analysis of three companies in the years 2017-2020 will be performed using the methods listed in the theoretical part. This analysis will be complemented by a bankruptcy and creditworthiness model and time series predictions. Subsequently, all the findings and summarized proposals for what companies should focus on effective company management are summarized.
|
|
|
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.
|
|
|
Algorithmic Trading Using Artificial Neural Networks
Chlud, Michal ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.
|
|
|
The Use of Artificial Intelligence on Stock Market
Lajczyk, Pavel ; Budík, Jan (referee) ; Dostál, Petr (advisor)
This master's thesis deals with artificial neural networks and possibilities of their use on stock market. In next chapters of this thesis there are provided design and implementation of stock prices prediction tool. The implementation is done with use of the MATLAB software. The created prediction tool is then tested in a simple trading simulation and achieved results are discussed in the end
|
guest ::
