Forecasting the market value of power battery industry chain: A novel RRMIDAS-SVR model

Abstract

The emergence of electric vehicles has contributed to mitigating air pollution and greenhouse effects caused by traditional fuel vehicles. The power battery industry chain, which is a primary component of electric vehicles, requires more attention to monitor its development status. This study proposes a novel method for forecasting the development status of the power battery industry chain by monitoring the market value index of all listed companies in the power battery industry. It proposes a new forecasting model, RRMIDAS-SVR, which outlines reverse-restricted mixed data sampling (RRMIDAS) into support vector regression (SVR) to end the data-driven challenges of mixed-frequency data and nonlinear relationships. We estimate the RRMIDAS-SVR model using a quadratic programming problem and mixed-frequency West Texas Intermediate crude oil futures prices, electric vehicle sales, and the consumer price index as predictors of the market value of all listed companies in the power battery industry chain. The experimental findings reveal that the RRMIDAS-SVR model outperforms the other models, as evidenced by its lower mean absolute error and root-mean-square error. This study contributes to understanding the development status of the power battery industry value chain by proposing and developing a new approach, RRMIDAS-SVR, to monitor the industry's development status that considers a multi-source information set. Moreover, this study provides strategic insights for stakeholders in the power battery industry.