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Yu, L.A. and Wang, S.Y. (2009) Fuzzy Least Squares Support Vector Machine Model and Its Application Based on Kernel Principal Component Analysis with Variable Penalty Factor. System Science and Mathematics, 10, 1311-1326.

has been cited by the following article:

• TITLE: The Corporate Financial Forecasting Based on Least Squares Support Vector Machines Methods

  AUTHORS: Shunquan Zhu

  KEYWORDS: Financial Data Samples, Support Vector Machines, Financial Forecasting, Modeling and Simulations

  JOURNAL NAME: Technology and Investment, Vol.8 No.3, August 9, 2017

  ABSTRACT: This paper analyzed the present domestic and foreign financial forecasting situation of listed companies and it is based on least squares support vector machines. According to our country’s capital markets, 44 listed companies are modeling data samples, 10 listed companies are forecasting data samples, and building financial forecasting model of listed companies obtains satisfaction financial forecasting results. The empirical study results show that we may use entirely least squares support vector machines methods to build financial forecasting models, and to distinguish financial credit risks of listed companies; comparing to traditional statistical methods and neural network methods, financial forecasting method based on least squares support vector machines is an ideal listed company’s financial forecasting method. It is used to extensive fields that have high extending value.