A Study on the Valuation Growth Model for Early-stage Enterprises in the Next-generation of Information Technology Sector

ABSTRACTThis study delves into the valuation growth model of early-stage technology companies in the next-generation information technology sector, aiming to decipher the key drivers of the annualized valuation growth rate. By constructing a complex model with 24 independent variables, this research integrates linear regression, random forest, and XGBoost machine learning techniques to conduct an empirical analysis of 203 non-listed early-stage technology companies meeting specific criteria. The findings reveal that industry compound annual growth rate (IndCAGR), industry size (IndSize), entrepreneurial experience (EntrepExp), research and development personnel ratio (RDPct), historical financing amount (HistFin), and more than 10 years of management experience in large companies (MgmtExp10) are significantly positively correlated with the company's valuation growth rate. Notably, 5 years of management experience in large companies (MgmtExp5) shows a significant negative correlation with valuation growth, potentially due to insufficient managerial experience. Additionally, this study discovers that university technology transfer (UniTrans) and the number of intellectual property counts (IPCount) positively impact valuation growth. The research outcomes provide a theoretical basis for investment decisions in early-stage technology companies and offer empirical support for the project selection criteria of investment institutions. The innovation of this study lies in the combination of traditional statistical methods with modern machine learning techniques, along with a comprehensive analysis of the factors influencing the valuation growth of early-stage technology companies.