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

• Main Authors: Yuh-Jen Chen, Jou-An Lin, Yuh-Min Chen, Jyun-Han Wu
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Financial forecasting; multivariate adaptive regression splines (MARS); queen genetic algorithm (QGA); support vector regression (SVR)
• Online Access: https://ieeexplore.ieee.org/document/8756170/

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.