Abstract
The high demand of the competitive market for innovation has brought the increase of research and development (R&D) investment. High-tech enterprises can reasonably control R&D cost and effectively manage R&D activities by accurately predicting R&D investment. Given the characteristics that high-tech enterprises have high uncertainty and frequently changing information in R&D investment, this paper uses the grey metabolic GM (1, 1) model and the exponential smoothing method in time series to establish a single prediction model of R&D investment in high-tech enterprises. With the analysis of the advantages and disadvantages of each single model, a combined forecast model of R&D investment in high-tech enterprises is thus established. The model was applied to the forecast of R&D investment of a high-tech enterprise in China from 2019 to 2023, and the results verified the higher accuracy and practicability of this model. The establishment of this model can provide effective support for high-tech enterprises in R&D cost management.
Highlights
In the era of knowledge economy, market competition for innovative products is becoming increasingly fierce
From the statistics on science and technology expenditures released by the Ministry of Science and Technology of China, it can be seen that the intensity of corporate research and development (R&D) expenditure has been increasing in recent years as shown in Table 1. is phenomenon shows that, in the construction of an innovative country, the role of enterprises as the main body of technological innovation has become more prominent
According to the characteristics of R&D investment of high-tech enterprises and the applicable scope of the forecasting model, the metabolic GM (1, 1) model optimized by the grey system and the exponential smoothing method of the time series model is selected as the single model for forecasting
Summary
In the era of knowledge economy, market competition for innovative products is becoming increasingly fierce. The prediction accuracy of a single prediction model is poor, according to the advantages and disadvantages of each model, a combined forecasting model for R&D funds investment of high-tech enterprises is established, which uses variance reciprocal method to assign weights. E exponential smoothing method based on time series can consider new information and historical data Both of these two forecasting methods can better meet the requirements of high-tech enterprises’ R&D investment forecasting. According to the characteristics of R&D investment of high-tech enterprises and the applicable scope of the forecasting model, the metabolic GM (1, 1) model optimized by the grey system and the exponential smoothing method of the time series model is selected as the single model for forecasting. E grey metabolic GM (1, 1) model and exponential smoothing method are both suitable for R&D investment forecasting of high-tech enterprises. Where xt is the observed value of time t and x(i) is the predicted value of the i-th method in time t
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