Forecasting Stock Market Neural Networks
Mostrando 1-3 de 3 artigos, teses e dissertações.
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1. Previsão de demanda de autopeças com redes neurais
This paper presents a methodology for forecasting demand parts based on Artificial Neural Networks (ANN). To validate it, we performed a comparative study on a reference work in the literature, which is based on exponential smoothing and moving average methods. The products are grouped into 10 categories according to proximity, resulting on 72 monthly observ
IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia. Publicado em: 20/08/2010
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2. MEAN AND REALIZED VOLATILITY SMOOTH TRANSITION MODELS APPLIED TO RETURN FORECASTING AND AUTOMATIC TRADING / MODELOS DE TRANSIÇÃO SUAVE PARA MÉDIA E VOLATILIDADE REALIZADA APLICADOS À PREVISÃO DE RETORNOS E NEGOCIAÇÃO AUTOMÁTICA
The main goal of this dissertation is to compare the performance of linear and nonlinear models to forecast 23 assets of the American Stocks Market. The Heteroscedastic STAR-Tree Model is proposed using the STAR- Tree (Smooth Transition AutoRegression Tree) methodology applied to heteroscedastic time series. As assets returns and realized volatility intraday
Publicado em: 2008
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3. O uso de redes neurais artificiais na previsÃo de tendÃncias no mercado de aÃÃes
Stock markets are considered a high return investment option, dominated by uncertainty and volatility. The forecast of the movement of that market is a difficult task, because is influenced by many economical, political and even psychological factors. The traditional statistical methods and the known analysis (technical and fundamental) are not capable to id
Publicado em: 2006