Technology Startup Value Prediction: A Machine Learning Approach

• Main Authors: Yu-Fan Hsu, 許予帆
• Other Authors: 魏志平
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/cpznq3

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 106 === Venture Capital is an important financing strategy for helping startups to survive in their early stage. Recently, the growth of VC investments and deals has been showing the increasing importance of startups. However, when selecting potential startups for investments, VCs are exposed to great risks, including the high failure rate of startups and the information asymmetry between VCs and startups. Thus, it is essential to develop a prediction technique that can effectively identify the value of a technology startup. In this research, we follow the machine learning approach to develop a predictive model that encompasses a more comprehensive set of predictors (independent variables) for predicting the value of technology startups. We categorize these variables into three categories, including basic variables, patent-related variables, and last-round related variables. As expected, our evaluation results show that adding patent-related variables or last-round related variables improves the accuracy of startup value prediction. In addition, combining all the features performs the best. Our proposed prediction technique is expected to benefit VCs in evaluating the value of technology startups, especially in unfamiliar industries. At the same time, it is expected to help startups to develop the fundraising negotiation strategies and identify the most appropriate timing for fundraising.