Adebiyi ayodele (4 risultati)
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. A Model for Stock Price Prediction using the Soft Computing Approach | Neural Networks and ARIMA Model to Stock Price Prediction | Ayodele Adebiyi | Taschenbuch | Englisch | LAP Lambert Academic Publishing | EAN 9783846509098 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Lan…dstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Adebiyi AyodeleDr. Adebiyi Ayodele Ariyo holds B.Sc. in Computer Science, MBA, M.Sc. and Ph.D in Management Information System. He is a lecturer in the department of Computer and Information Sciences,…Covenant University, Ota. His re.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A number of research efforts had been devoted to forecasting stock price based on technical indicators which rely purely on historical stock price data. However, the performances of such technical indicators have not always satisfactory…. The fact is, there are other influential factors that can affect the direction of stock market which form the basis of market experts opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, investors confidence, government policy and political effects, among others. In this study, the effect of using hybrid market indicators such as technical and fundamental parameters as well as experts opinions for stock price prediction was examined. Values of variables representing these market hybrid indicators were fed into the artificial neural network (ANN) model for stock price prediction. The empirical results obtained with published stock data show that the proposed model is effective in improving the accuracy of stock price prediction. Also, the performance of the neural network predictive model developed in this study was compared with the conventional Box-Jenkins autoregressive integrated moving.
