The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Accuracy in financial forecasting is a key determinant of profits in the financial markets. This paper proposes improvements to existing Artificial Neural Network based forecasting approaches using de-noising in frequency domain and the Hodrick-Prescott Filter. Traditionally used technical indicators are replaced with open, close, high, and low prices only. Forecasts achieved via these improvements...
This paper aims to forecasting and analyzing the securities market, which is a crucial problem in modern society development. Firstly, the structure of the securities market forecasting and analyzing system is provided. Moreover, this system is designed based on a trade statistics database, in which the real time transaction data are memorized, and then the original data are put forward to the neural...
In recent years there has been a significant growth of interest in the incorporation of historical series of variables related to stock prediction into mathematical models or computational algorithms in order to generate predictions or indications about expected price movements. The objective of this study was to utilize artificial neural networks to predict the closing price of the stock PETR4 which...
The neural network model presents a new procedure and structure in nonlinear interpolation and demonstrates a potential of stock market prediction with incomplete, imprecise and noisy data. However, a random selection of model parameters might cause the ??over-fitting?? or ??under-fitting?? problem in generalization or prediction. This paper presents a sensitivity analysis of optimal hidden layers...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.