Temat: stock price forecasting - Katalog OPAC zbiorów

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Tytuł:: Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Artificial Bee Colony Algorithm
Autorzy:: Khuat, T. T.
Le, Q. C.
Nguyen, B. L.
Le, M. H.
Powiązania:: https://bibliotekanauki.pl/articles/308651.pdf
Data publikacji:: 2016
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: Artificial Bee Colony algorithm
Artificial Neural Network
back-propagation algorithm
stock price forecasting
wavelet transform
Opis:: Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions.
Źródło:: Journal of Telecommunications and Information Technology; 2016, 2; 43-52
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Porównanie podejścia aproksymującego i klasyfikującego w prognozowaniu kursów wybranych akcji na GPW w Warszawie S.A. z użyciem jednokierunkowych sieci neuronowych
Forecasting Stock Prices Using Feed-Forward Neural Network - a Comparison of Approximation and Classification Approaches
Autorzy:: Kasznia, Anna
Powiązania:: https://bibliotekanauki.pl/articles/589117.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Ekonomiczny w Katowicach
Tematy:: Giełda papierów wartościowych
Kurs akcji
Prognozowanie
Sieci neuronowe
Szeregi czasowe
Forecasting
Neural networks
Share price
Stock market
Time-series
Opis:: In this paper two approaches to financial time series forecasting using neural networks were compared. First one, the function approximation approach, in which neural networks are trained to forecast the exact one day ahead value of stock price. And the second one, classification approach, in which the output variable is the direction of future stock price movements. The aim of this work was to check if using the classification models can lead to better results in terms of direction of change forecasting and profits generated by their forecasts. This research was conducted on the basis of the time series of daily closing stock prices for three companies listed on the Warsaw Stock Exchange. Simulations show that some of the approximating models achieved satisfactory results in terms of the directional symmetry measure, although the best results for each of the analyzed company have been achieved for classification models.
Źródło:: Studia Ekonomiczne; 2013, 146; 59-67
2083-8611
Pojawia się w:: Studia Ekonomiczne
Dostawca treści:: Biblioteka Nauki

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