ELECTRONIC STOCK PRICE PREDICTOR (ESPP) FOR STOCK MARKET PRICES PREDICTION

المصدر:	مجلة البحوث الإدارية
الناشر:	أكاديمية السادات للعلوم الإدارية - مركز البحوث والاستشارات والتطوير
المؤلف الرئيسي:	Ahmed, Abeer Bader Aldin (AUTH.)
مؤلفين آخرين:	Ali, Shahera Saad Ali (AUTH.)
المجلد/العدد:	مج31, ع3
محكمة:	نعم
الدولة:	مصر
التاريخ الميلادي:	2013
الشهر:	يوليو
الصفحات:	37 - 57
ISSN:	1110-225x
رقم MD:	661032
نوع المحتوى:	بحوث ومقالات
قواعد المعلومات:	EcoLink
مواضيع:	الأسواق المالية \| أسعار الأسهم \| الشركات المساهمة \| الإلكترونيات \| التبؤات المستقبلية
رابط المحتوى:	PDF (صورة) PDF (نص) HTML

18

المستخلص:	Predicting financial markets has been one of the biggest challenges for AI community in the last two decades. The objective of this prediction research has been largely beyond the capability of traditional AI because AI has mainly focused on developing intelligent systems which are supposed to emulate human intelligence. However, the majority of human traders cannot win consistently on the financial markets. In other words, human intelligence for predicting financial markets may well be inappropriate. Therefore, developing AI systems for this kind of prediction is not simply a matter of re-engineering human expert knowledge, but rather an iterative process of knowledge discovery and system improvement through data mining, knowledge engineering, theoretical and data-driven modelling, as well as trial and error experimentation. This paper presents a proposed neural network based model to efficiently forecast and assure the extent of credibility based on historical data "Electronic stock price predictor"(ESPP). This Part presents the neural network model and the steps used to predict the stock expected future trend direction. \
وصف العنصر:	اصل المقال باللغة الإنجليزية
ISSN:	1110-225x