## 1 Introduction  
Information about the current conditions on the Stock Exchange and future closing prices is crucial for investors, who wish to maximize their profits and make good investment decisions. Over the years, different theories and stock models have been developed, some of which contradicted each other. Until the second half of twentieth century, it was believed that price changes were random and thus could not be predicted. This was a basic assumption of the random walk hypothesis [1], which is consistent with the efficient market hypothesis (EMH) formulated by [PERSON] [2]. This hypothesis states that share prices fully reflect all the available information, and stocks always trade at a fair value. It is therefore impossible to outperform the market overall, and higher returns can be only obtained by chance or by purchasing riskier investments. This hypothesis was not in line with experience, as evidenced by the profits amassed by professional financial institutions trading on stock markets. It was also refuted by a large group of academics, who observed and analyzed market prices over long periods of time and proved that market prices were not random, but exhibited trends [3, 4]. Moreover, these trends repeated over time and some followed a sinusoidal shape [5]. These results supported the view that the market is, to some degree, predictable, and that future prices and trends can be predicted on the basis of past prices. This opened the way for technical analysis. Thus, the problem of stock price prediction is closely related to time series analysis and modeling trends. These days, not only spectral analysis of trade signals [5], but also other more complex methods, such as joint time-frequency-shape analysis, are available [6]. The financial market provides a variety of software products supporting investors. Traditional prediction algorithms, such as autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH), are based on financial time series modeling using the stochastic process theory [7]. A linear model ARIMA [8] has dominated financial time series forecasting methods for more than half a century. Popular financial instruments offered by stock exchanges may help investors to make a profit. However, there is also the risk of losing assets [9]. With the increasing complexity of trading systems, soft computing methods, i.e., artificial neural networks (ANN) [7; 10; 11] or fuzzy logic [12], are gaining greater popularity. The stock quotes time series can be considered as representations of nonlinear dynamic systems [11]. Artificial neural networks, which are nonlinear universal approximators [13], are appropriate tools for such complex modeling tasks. Models based on ANN were introduced into financial forecasting two decades ago [14]. In these mainly autoregressive (AR) models, a time series is modeled by an ANN and the next time series value is predicted on the basis of several past observations. [PERSON], [PERSON], and [PERSON] [15] applied ARIMA and ANN for periodic time series modeling and concluded that ANN-based forecasting is more accurate. The prediction of trading signals has been mostly implemented using feed-forward neural networks (FNN) of the multilayer perceptron (MLP) type [7; 14; 16]. However, new ANN structures and ANN-based methods have also been developed, such as: A probabilistic neural network [11], a dynamic artificial neural network (DAN2) [17], a functional link artificial neural network [18], a state space wavelet network [19], or a neural-wavelet analysis [20]. Uses of ANNs include predicting future exchange rates [11], stock market indices [10; 19; 21], and share prices [22]. A hybrid approach was proposed by [PERSON] [23], combining ARIMA and ANN for time series modeling. This hybrid methodology takes advantage of statistical and neural methods and outperforms both. Today, hybrid modeling is becoming more and more popular for share price forecasting.