03920ntm a22005777i 4500 000681553 CZ-PrVSE 20210206111302.0 m d cr n|||||||||| 210206s2020 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Drašković, Aleksandra ISIS:119055 dis Je umělá inteligence lepší v předvídání výnosů z akciových trhů než klasické metody? : eng Is Artificial Intelligence Better in ForecastingStock Market Returns than Classical Methods? : / Aleksandra Drašković 2020 ?? stran : digital, PDF soubor Vedoucí práce: Aleš Maršál Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Národohospodářská fakulta, 2021 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2021 The thesis analyzes the issue of predictions of stock market returns. In particular, it concentrates on the discussion about which methods and models can predict yields in the foremost efficient manner. Apart from the initial introduction of AI and the investment theories, the study includes both an overview of statistical models (ARMA, ARIMA, ARCH, ARCH, NMA, TAR, and MSAR) and computationally intelligent models and approaches (such as artificial neural networks, support vector machines, fuzzy set theory, evolutionary algorithms, expert systems, singular spectrum analysis, naive Bayes, k-nearest neighbors, and hybrid models). The empirical part of the thesis aims to observe the issue of predicting stock returns while considering statistical and AI methods. It comprises of two sections. The first segment is dedicated to formulating and generating a systematic review. From the obtained results, it can be seen that about 73 % of all included studies assist in not rejecting the null hypothesis of the superior performance of models based on machine learning. The second subdivision is devoted to introducing own constructed models, namely ARIMA models and an LSTM model. Both types of models are initially introduced, then the models are formed, and subsequently, forecasts are generated. According to the results of the own construction of comparison models, the LSTM model, which belongs to the AI category, proved to be more efficient and accurate in its predicted future values. Therefore, the second part of the empirical research also does not reject the null hypothesis of the better performance of the AI methods. Způsob přístupu: Internet ekonomická analýza [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas artificial intelligence ARIMA prediction of stock market returns LSTM ANN stock market forecast of financial time series investment theories systematic review Maršál, Aleš ISIS:8588 ths Potužák, Pavel vse20231203939 opn Vysoká škola ekonomická v Praze. Národohospodářská fakulta kn20010709401 dgg https://insis.vse.cz/zp/73132/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/73132 Hlavní práce https://insis.vse.cz/zp/73132/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/73132/posudek/oponent/69118 Oponentura https://insis.vse.cz/zp/73132/priloha/21470 Přiloha k práci https://insis.vse.cz/zp/73132/podrobnosti dc:identifier NEPOSILAT VSKP vse73132 210205 73132