Abstract. A task of a stock index prediction is presented in this paper. Several issues are considered. The data is gathered at the concerned stock market (NIKKEI) and two other markets (NASDAQ and DAX). The data contains not only original numerical values from the markets but also indicators pre-processed in terms of technical analysis, i.e. the oscillators are calculated and the structures of a value chart are extracted. Selected data is input to a neural network that is functionally divided into separate modules. The prediction goal was next day opening value of Japanese stock market index NIKKEI with consideration of German and USA stock markets ’ indexes. The average prediction error on the test set equals 43 points and the average per...
This research attempts to explore the usefulness of neural networks in stock index forecasting in th...
Neural Networks are very good in learning patterns from a lot of information. Thus, it is generally ...
This research investigated the feasibility and capability of neural network-based approaches for pre...
We predict stock markets using information contained in articles published on the Web. Mostly textua...
Predicting stock data with traditional time series analysis has become one popular research issue. A...
We predict stock markets using information contained in articles published on the Web. Mostly textua...
AbstractIn this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ...
In this paper, we give a prediction algorithm for the stock market. A basis of this algorithm is the...
The aim of this paper is to develop new neural network algorithms to predict trading signals: buy, h...
Accurate prediction of stock market price is of great importance to many stakeholders. Artificial ne...
In the business sector, it has always been a difficult task to predict the exact daily price of the ...
This paper presents a study of artificial neural nets for use in stock index forecasting. The data f...
This paper presents a computational approach for predicting the Australian stock market index - AORD...
Stock price index is the initial significant factor influencing on investors' financial decision ma...
Stock market prediction has been an area of great interest to financial researchers and practitioner...
This research attempts to explore the usefulness of neural networks in stock index forecasting in th...
Neural Networks are very good in learning patterns from a lot of information. Thus, it is generally ...
This research investigated the feasibility and capability of neural network-based approaches for pre...
We predict stock markets using information contained in articles published on the Web. Mostly textua...
Predicting stock data with traditional time series analysis has become one popular research issue. A...
We predict stock markets using information contained in articles published on the Web. Mostly textua...
AbstractIn this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ...
In this paper, we give a prediction algorithm for the stock market. A basis of this algorithm is the...
The aim of this paper is to develop new neural network algorithms to predict trading signals: buy, h...
Accurate prediction of stock market price is of great importance to many stakeholders. Artificial ne...
In the business sector, it has always been a difficult task to predict the exact daily price of the ...
This paper presents a study of artificial neural nets for use in stock index forecasting. The data f...
This paper presents a computational approach for predicting the Australian stock market index - AORD...
Stock price index is the initial significant factor influencing on investors' financial decision ma...
Stock market prediction has been an area of great interest to financial researchers and practitioner...
This research attempts to explore the usefulness of neural networks in stock index forecasting in th...
Neural Networks are very good in learning patterns from a lot of information. Thus, it is generally ...
This research investigated the feasibility and capability of neural network-based approaches for pre...