Kho, Henry and Lukas, Lukas and Widiputra, Harya Damar (2009) Predicting Single Stock Value Movement by Using Clustering Based on Linear Regression and Polynomial Equation. Bachelor thesis, Swiss German University.

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Abstract

Time-series prediction has been an active research topic in recent studies. Some popular approaches to this problem are the traditional statistical method (e.g. multiple linear regression and moving average), and neural network with the Multi Layer Perceptron which has shown its supremacy in time-series prediction. In this thesis, a new approach based on linear and polynomial regression clustering for predicting a single stock value exchange is proposed. Every stock movement has shown repeating patterns of movement from the past. To illustrate chaotic time-series data, five stocks exchange and one benchmark data are used to test the strength of the algorithm.

Item Type:	Thesis (Bachelor)
Subjects:	H Social Sciences > HG Finance > HG4551 Stock exchanges T Technology > T Technology (General) > T58.5 Information technology
Divisions:	Faculty of Engineering and Information Technology > Department of Information Technology
Depositing User:	Astuti Kusumaningrum
Date Deposited:	04 Nov 2020 02:42
Last Modified:	04 Nov 2020 02:42
URI:	http://repository.sgu.ac.id/id/eprint/995

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