Financial Forecasting with Multivariate Adaptive Regression Splines and Queen Genetic Algorithm-Support Vector Regression

Yuh Jen Chen, Jou An Lin, Yuh Min Chen, Jyun Han Wu

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

In consideration of the financial indicators of financial structure, solvency, operating ability, profitability, and cash flow as well as the non-financial indicators of firm size and corporate governance, the algorithms of multivariate adaptive regression splines (MARS) and queen genetic algorithm-support vector regression (QGA-SVR) are used in this study to create a comprehensive financial forecast of operating revenue, earnings per share, free cash flow, and net working capital to help enterprises forecast their future financial situation and offer investors and creditors a reference for investment decision-making. This study's objectives are achieved through the following steps: (i) establishment of feature indicators for financial forecasting, (ii) development of a financial forecasting method, and (iii) demonstration of the proposed method and comparison with existing methods.

Original languageEnglish
Article number8756170
Pages (from-to)112931-112938
Number of pages8
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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