A Novel Methodology for Estimating Technology Value and Importance of Factors in Market-Based Approach

Abstract

Technology valuation methods are classified into income-based, cost-based, and market-based approaches depending on the perspective of valuing technology. The market approach evaluates the value of technology by referring to cases in which similar technologies have been traded before. In this study, we use prior technology transaction data to estimate the technology value based on the market approach and to identify influential factors to the estimated value. To this end, we adopt a multivariate k-nearest neighbor (MKNN) regression model to accommodate mixed-type input variables aiming at estimating multivariate technology values, selecting influencing factors, and the relative importance of the selected factors. In addition, we can optimize the number of transaction cases k in k-NN regression. Our proposed regression model outperforms an embedding model with cosine similarity in predicting multivariate response variables. In addition, we illustrate how to select and assess the influential factors based on the real-life dataset.

Keywords

• feature selection
• genetic algorithms
• market approach
• multivariate regression
• technology valuation