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Forecasting Stock Market Trends by Logistic Regression and Neural Networks: Evidence from KSA Stock Market

Euro-Asian Journal of Economics and Finance

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Field Value
 
Title Forecasting Stock Market Trends by Logistic Regression and Neural Networks: Evidence from KSA Stock Market
 
Creator ZAIDI, Dr. Makram
AMIRAT Grace Ofori-Abebrese, Dr. Amina
 
Subject forecasting, logistic model, neural networks, stock index, trends
 
Description Forecasting stock market trends is very vital for investors to take action for the next period for sustainable competition. It is especially important for policy makers to predict actions for development. KSA stock market is evolving rapidly. Due to increasing importance; the aim of this study is to forecast the stock market trends by using logistic model and artificial neural network.  Logistic model is a type of probabilistic statistical classification model. It is also used to predict a binary response from a binary predictor, used for predicting the outcome of a categorical dependent variable(i.e., a class label) based on one or more predictor variables (features). Artificial neural networks are models which are used for forecasting because of their capabilities of pattern recognition and machine learning. Both methods are used to forecast the stock prices of upcoming period. The model has used the preprocessed data set of closing value of TASA Index. The data set encompassed the trading days from 5th April, 2007 to 1st January, 2015. Both methods give us estimation with up to 80% accuracy.
 
Publisher Academy of Business & Scientific Research
 
Date 2016-04-19
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://absronline.org/journals/index.php/eajef/article/view/666
 
Source Euro-Asian Journal of Economics and Finance; Vol 4 No 2 (2016): April; 50-58
2310-4929
2310-0184
 
Language eng
 
Relation http://absronline.org/journals/index.php/eajef/article/view/666/685
 
Rights Copyright (c) 2016 Euro-Asian Journal of Economics and Finance