Stock price forecasting using secondary self-regression model and wavelet neural networks

Autor:	Chi-I Yang, Kuei-Fang Chang, Kai-Cheng Wang
Rok vydání:	2015
Předmět:	Wavelet Wavelet neural network Artificial neural network Computer science Econometrics Particle swarm optimization Regression analysis Stock forecasting Stock price forecasting Stock (geology)
Zdroj:	ICDIP
ISSN:	0277-786X
DOI:	10.1117/12.2196914
Popis:	We have established a DWT-based secondary self-regression model (AR(2)) to forecast stock value. This method requires the user to decide upon the trend of the stock prices. We later used WNN to forecast stock prices which does not require the user to decide upon the trend. When comparing these two methods, we could see that AR(2) does not perform as well if there are no trends for the stock prices. On the other hand, WNN would not be influenced by the presence of trends.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::40a7113d5a19b2d9a4e0bfe36197d4bf https://doi.org/10.1117/12.2196914 Zobrazit plný text záznamu