Abstract
Stock prices are developing very dynamically and nonlinearly. The stock price is affected by a number of factors. Stocks are therefore characterized by asymmetric volatility, non-stationarity, and sensitivity. Given these facts and the unpredictability of a global crisis, it is logical that the process of stock price prediction is a complex task. Traditional methods for price prediction are no longer enough; new applications and techniques, such as artificial neural networks, are coming to the forefront. The aim of this contribution is to analyze and predict the evolution of the stock price of Unipetrol, a.s. on the Prague Stock Exchange using artificial neural networks. Stock price data is available between January 2006 and April 2018. The data file is first analyzed. Subsequently, a total of 10,000 multilayer perceptron networks (MLPs) and a basic radial function network (RBF) are generated. A total of five neuron structures with the best characteristics are preserved. Using statistical interpretation, it is found that in practice, the MLP 1-17-1 network is applicable in one business day prediction.
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We will follow the least squares method. Networks generation will be finished when there is no improvement, i.e., there is no decrease in the sum of the squares. We will retain the neural structures whose sum of the squares residuals to the actual gold price development will be the lowest (zero in ideal case).
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Machová, V., Vochozka, M. (2019). Using Artificial Intelligence in Analyzing and Predicting the Development of Stock Prices of a Subject Company. In: Ashmarina, S., Vochozka, M. (eds) Sustainable Growth and Development of Economic Systems. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-11754-2_18
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