03619ntm a22005297i 4500 000699887 CZ-PrVSE 20230618075638.0 m d cr n|||||||||| 230618s2023 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Jouda, Jan ISIS:141453 dis Aplikace strojového učení v obchodování na kapitálových trzích eng Application of machine learning in capital markets trading / Jan Jouda 2023 ?? stran : digital, PDF soubor Vedoucí práce: Milan Fičura Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví, 2023 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2023 The thesis focuses on the application of chosen machine learning methods to daily trading in thirty randomly selected stocks. As explanatory variables, only standardized stock data and technical analysis indicators are used. The aim is mainly focused on the comparability across the approaches and different time frames. The comparisons are made between the machine learning approaches and the technical indicators, in between the machine learning approaches and across the different time frames. In the beginning chapters, the idea of trading is put into perspective with time, and a bit of a historical excursion is showcased. The work continues with the theoretical groundwork for the approaches utilized and the descriptions and definitions of their processing. Finally, the results chapters are divided, due to a larger volume of results, into two chapters. The first one goes more into detail and serves as a reference point, and the second one includes most of the important analytical work. As some summaries are done throughout the results chapters, the conclusions chapter’s main goal is to provide the answers to the selected hypotheses. With all the settings, the work concludes that machine learning methods have outperformed technical analysis indicators furthermore, that a more sophisticated approach does not necessarily always achieves better results. Finally that the time periods used for training may influence the results significantly, and the most extensive dataset might not always be the best choice for the training of the models. Způsob přístupu: Internet bankovnictví a pojišťovnictví [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Deep Learning Machine Learning Algorithmic Trading Technical Analysis Returns and Accuracy Comparisons Differing Training Time Frames Fičura, Milan vse2015859798 ths Drahokoupil, Jakub ISIS:120064 opn Vysoká škola ekonomická v Praze. Fakulta financí a účetnictví kn20010709398 dgg https://insis.vse.cz/zp/82679/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/82679 Hlavní práce https://insis.vse.cz/zp/82679/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/82679/posudek/oponent/78916 Oponentura https://insis.vse.cz/zp/82679/podrobnosti dc:identifier NEPOSILAT VSKP vse82679 230616 82679