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

Shunquan Zhu

doi:10.4236/ti.2017.83013

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.

Highlights

Financial forecasting has been widely draw attention by the academic, financial worker and government
We mainly consider the earnings per share (EPS), return on equity (ROE), net asset value per share (NAVPS), Operating revenue per share, net cash flow per share and other financial factors as the financial indicators of listed companies
Notice that compared with the traditional statistical and neural network methods, least squares support vector machine has the following advantages:

Summary

Introduction

Financial forecasting has been widely draw attention by the academic, financial worker and government. In order to protect the sustainable development of capital market, it becomes a key issue that how to apply advanced scientific approaches to forecast listed company’s financial situation. This can help to create a fair contended market atmosphere and enhance the credit awareness of listed company as well as regulate the company’s finance more correctly. A new financial forecasting approach which is based on Least Squares Support Vector Machines is in need to adapt to the complex capital market This classification can overcome the problems listed above so as to improve the quality of financial forecasting. The quality of the financial forecast will have a direct impact on the company’s risk management and cost control; it generates a profound influence on the financial regulations, commercial banks, investment banks, fund companies, insurance companies as well as other listed companies

Literature

LSSVM Method

Variable Selection and Modeling Samples

Analysis and Conclusion