文献类型：期刊文献

英文题名：A novel approach to screening patents for securitization: a machine learning-based predictive analysis of high-quality basic asset

作者：Liu, Cheng[1];Shi, Yi[1];Xie, Wenjing[1];Bao, Xinzhong[2]

第一作者：Liu, Cheng

通讯作者：Bao, XZ[1]

机构：[1]Univ Sci & Technol Beijing, Sch Econ & Management, Beijing, Peoples R China;[2]Beijing Union Univ, Sch Management, Beijing, Peoples R China

第一机构：Univ Sci & Technol Beijing, Sch Econ & Management, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Sch Management, Beijing, Peoples R China.|[1141755]北京联合大学管理学院;[11417]北京联合大学;

年份：2023

外文期刊名：KYBERNETES

收录：;EI(收录号：20233714722933);Scopus(收录号：2-s2.0-85170714202);WOS:【SCI-EXPANDED(收录号:WOS:001064830200001)】；

基金：Beijing Social Science Foundation of PR China (20ZDA03).

语种：英文

外文关键词：Patent securitization; Machine learning; High-value patents; Enterprise credit; Underlying assets

摘要：PurposeThis paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.Design/methodology/approachThis paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.FindingsThe machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.Originality/valueThe main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.