Je umělá inteligence lepší v předvídání výnosů z akciových trhů než klasické metody?

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.