Artificial neural networks (ANN) is an approach to solving different tasks. In this paper we forecast U.S. stock market movements using two types of artificial neural networks: a network based on the Levenberg-Marquardt learning mechanism and a synergetic network which was described by German scientist Herman Haken. The LevenbergMarquardt ANN is widely used for forecasting financial markets, wh...

There is a consensus that conventional continuous demand distribution methods are not appropriate for forecasting replacement parts. However, many forecasting tools available in market still use them. This work presents an application of the Poisson distribution to forecast the needs of electronic spare parts. Using basic stock management notions and usual concepts of reliability, availability ...

The presence of stock market efficiency is a distinctive characteristic of the effectively functioning market economy. Investigation of the market efficiency of seven emerging East-European stock exchanges is carried out as their major stock indices (BELEX15, BET, CROBEX, ISE100, PFTS, RTSI, SOFIX) are studied in respect of long-range dependence (LRD), persistency, and forecasting possibili...

In this study, a hybrid intelligent data mining methodology, genetic algorithm based support vector machine (GASVM) model, is proposed to explore stock market tendency. In this hybrid data mining approach, GA is used for variable selection in order to reduce the model complexity of SVM and improve the speed of SVM, and then the SVM is used to identify stock market movement direction based on th...

This paper develops and estimates a heteroskedastic variant of Campbell s [Campbell, J., 1993. Intertemporal asset pricing without consumption data. American Economic Review 83, 487–512] ICAPM, in which risk factors include a stock market return and variables forecasting stock market returns or variance. Our main innovation is the use of a new set of predictive variables, which not only have su...

Developing forecasting models for estimating the behavior of capital markets is one of the most challenging tasks in financial decision support system research. Besides time series models, artificial neural network approaches and genetic algorithms, text mining technologies represent a promising approach to support financial decision-making. In this paper, the authors address the problem field ...

Investors’ search for successful forecasting models leads the data generating process for financial returns to change over time which means that individual return forecasting models can at best hope to uncover evidence of ‘local’ predictability. We illustrate this point on a suite of forecasting models used to predict US stock returns and propose an adaptive forecast combination approach. Most ...

Abstract: It is very important to minimize the risk in portfolio selection. For minimizing risk of portfolio at a given expected returns, it is efficient to compose portfolio with stocks which have low cross-correlation among them. In this regard, forecasting the cross-correlations among stock prices has attracted much interest among investors and financial market researchers. Most of studies i...

We forecast high resolution solar irradiance time series using an exponential smoothing state space (ESSS) model. To stationarize the irradiance data before applying linear time series models, we propose a novel Fourier trend model and compare the performance with other popular trend models using residual analysis and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test. Using the opt...