Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/179781
Authors: 
Hedayati Moghaddam, Amin
Hedayati Moghaddam, Moein
Esfandyari, Morteza
Year of Publication: 
2016
Citation: 
[Journal:] Journal of Economics, Finance and Administrative Science [ISSN:] 2218-0648 [Volume:] 21 [Year:] 2016 [Issue:] 41 [Pages:] 89-93
Abstract: 
In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term historical stock prices as well as the day of week as inputs. Daily stock exchange rates of NASDAQ from January 28, 2015 to 18 June, 2015 are used to develop a robust model. First 70 days (January 28 to March 7) are selected as training dataset and the last 29 days are used for testing the model prediction ability. Networks for NASDAQ index prediction for two type of input dataset (four prior days and nine prior days) were developed and validated.
Abstract (Translated): 
En este estudio se investigó la capacidad de previsión del índice bursátil diario NASDAQ, por parte de la red neuronal artificial (RNA). Se evaluaron diversas RNA proalimentadas, que fueron entrenadas mediante un algoritmo de retropropagación. La metodología utilizada en este estudio consideró como inputs los precios bursátiles históricos a corto plazo, así como el día de la semana. Se utilizaron los índices bursátiles diarios de NASDAQ del 28 de enero al 18 de junio de 2015, para desarrollar un modelo robusto. Se seleccionaron los primeros 70 días (del 28 de enero al 7 de marzo) como conjuntos de datos de entrenamiento, y los últimos 29 días para probar la capacidad del modelo de predicción. Se desarrollaron y validaron redes para la predicción del índice NASDAQ, para dos tipos de conjuntos de datos de input (los cuatro y los nueve días previos).
Subjects: 
NASDAQ
ANN
Prediction
NASDAQ
ANN
Predicción
JEL: 
C33
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by-nc-nd/4.0/
Document Type: 
Article
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