Partitioning the stocks of a portfolio by k-medoids clusteringapproach
Publish place: The 11th Seminar on Linear Algebra and its Applications
Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
SLAA11_021
تاریخ نمایه سازی: 16 اسفند 1401
Abstract:
Machine learning is mainly used in practice because of the existence of large set of data.The target of this article is to study partitioning a large set of stocks inside a portfolio bythe simple yet efficient k-medoids procedure. An algorithm is developed for this purpose.The unsupervised model is capable to receive financial returns and to illustrate the most andleast risky clusters of stocks to manage the risk.
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Authors
F. Soleymani
Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan۴۵۱۳۷–۶۶۷۳۱, Iran