Effectiveness of Neural Networks in Forecasting B.S.E Sensex Index
Since stock markets are volatile, dynamic and complicated, forecasting stock market return is considered as a challenging task. Nevertheless, researchers have developed various linear and nonlinear methods for effective forecasting. Among these neural networks are most suitable for forecasting non linear and chaotic relationships among variables. The current study attempts to forecast the future returns of B.S.E, highly volatile index, with the help of conventional method i.e. ARIMA (Auto Regression Integrated Moving Average) and Artificial Neural Network M.L.P (Multilayer Perceptron).To examine the efficiency of the models, MAD (Mean Absolute Deviation) and MSE (Mean Square Error) of the two models are compared. The study results revealed that neural network is better for forecasting in comparison to ARIMA.
yazar | Tarun Soni |
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Boyutlar ve boyutlar | 15 x 0,4 x 22 cm |
Tarafından yayınlandı | 10 Kasım 2011 |
15,2 x 0,6 x 22,9 cm 28 Şubat 2018 Icon Group International Kolektif 1 Ocak 2013 15 x 0,4 x 22 cm 21,6 x 0,6 x 27,9 cm 31 Ağustos 2012 20,3 x 0,2 x 26 cm Gerardus Blokdyk 20,3 x 0,3 x 26 cm 3 Haziran 2009 NAN RIEWALDT 20,3 x 0,4 x 26 cm 29 Ağustos 2009 Aeryn Cartwright 25 Mayıs 2010 N&S Collection
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Sürüm ayrıntıları
yazar | Tarun Soni |
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isbn 10 | 9783846555606 |
isbn 13 | 978-3846555606 |
Yayımcı | LAP LAMBERT Academic Publishing |
Dilim | İngilizce |
Boyutlar ve boyutlar | 15 x 0,4 x 22 cm |
DE OLDUĞU GİBİ | 3846555606 |
Tarafından yayınlandı Effectiveness of Neural Networks in Forecasting B.S.E Sensex Index | 10 Kasım 2011 |
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