Feature selection for neural network based stock prediction

We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Prompong Sugunnasil, Samerkae Somhom
التنسيق: Book Series
منشور في: 2018
الموضوعات:
الوصول للمادة أونلاين:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78650150569&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/50690
الوسوم: إضافة وسم
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المؤسسة: Chiang Mai University
الوصف
الملخص:We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction. © 2010 Springer-Verlag.