DWX Values & Trends prediction Using Artificial Neural networks
Abstract
إنّ الهدف من هذا البحث هو بناء شبكة عصبية اصطناعية ذات مواصفات مناسبة اعتماداً على خبرة الباحث وقاعدة التجربة والخطأ، وذلك من أجل التنبؤ بقيم مؤشر سوق دمشق للأوراق المالية واتجاهاتها ليوم التداول التالي.
لقد تمّ تدريب الشبكة العصبية وفق خوارزمية التكاثر الارتدادي على مجموعة من المتغيرات التي جرى تحديدها وحسابها في ضوء الدراسات السابقة وأساليب التحليل الفني.
البيانات المستخدمة جميعها تمّ الحصول عليها من الموقع الرسمي الالكتروني لسوق دمشق للأوراق المالية خلال الفترة الواقعة بين (4/1/2010 و7/3/2013) والتي تمت معايرتها لتسهيل عملية التدريب.
وقد أظهرت النتائج بأنّ الشبكة العصبية المستخدمة قد نجحت في التنبؤ بقيمة مؤشر سوق دمشق للأوراق المالية ليوم التداول التالي بدرجة عالية من الدقة ودرجة خطأ أقل من واحد بالمائة، وبالتالي إمكانية اتخاذ قرارات استثمارية ناجحة في السوق المدروسة اعتماداً على هذه النتائج.
the purpose of this research is to build a suitable artificial neural network depending on the researcher's experience, and trial & error base for predicting the values and trends of Damascus Securities Exchange Weighted Index (DWX) in the next trading day.
The neural network trained by back propagation algorithm on the set of variables determined and computed according to the literature review and technical analysis techniques.
All used data was driven from the website of (DSE) Damascus Securities Exchange through the period (4/1/2010 – 7/3/2013) which is also normalized to facilitate the training process.
The results showed that the trained neural network successfully had predicted the value of (DWX) in the next trading day with high degree of accuracy and less than 1% of error, therefore it may be made successful investment decisions in the studied market due to these results.
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