The goal of companies and investors in financial markets is more or less the same, achieving the highest possible profits. This is the main reason for the development of methods, which would predict returns of invested assets as accurately as possible. Initially, the movement of securities prices was considered completely unpredictable, which led to the development of efficient capital market theory. During the years, empirical researches in the financial field has shown, that the capital market does not meet all assumptions of efficient capital market and that there exist certain anomalies.
With the help of the analysis of past price movement, investor can set a trading strategy that will bring him above-average returns compared to the market in the long run. Many methods have been developed for analyzing the movement of securities prices and forecasting prices in the future. With the development of computer science, machine learning is becoming more and more popular. One of the most commonly used methods of machine learning are artificial neural
networks. Machine learning enables more precise modeling of anomalies in the capital market and easier definition of possible connections between securities prices, compared to the traditional methods.
In the master thesis the theoretical background of predictability in the movement of share prices and methods for forecasting their movement in the future are presented. The focus is on Autoregressive integrated moving average model (ARIMA model) and artificial neural networks.
ARIMA model is a combination of time series linear models, namely autoregressive model (AR model) and moving average model (MA model). Artificial neural networks are intelligent systems that imitate the functions of nerve cells in the human brains. They are built from artificial neurons and their main characteristic is the ability to learn the connection between input and output data.
Both mentioned methods are applied on the actual USA market data, namely on Dow Jones Industrial Average (DJIA) data from the period 2009-2014. DJIA index trade on the New York Stock Exchange (NYSE) and on NASDAQ. It consists of thirty major shares traded on the NYSE. We made the forecast of the DJIA index value movement in the year 2014. The comparison of the obtained results has shown, that artificial neural networks provide better results than ARIMA model. Based on our findings, we would propose an investor to choose artificial neural networks for share prices movement forecasting. Consequently, the investor could choose the best trading strategy.