Clues from networks:quantifying relational risk for credit risk evaluation of SMEs

Owing to information asymmetry,evaluating the credit risk of small-and mediumsized enterprises(SMEs)is difficult.While previous studies evaluating the credit risk of SMEs have mostly focused on intrinsic risk generated by SMEs,our study considers both intrinsic and relational risks generated by neighbor firms’publicly available risk events.We propose a framework for quantifying relational risk based on publicly available risk events for SMEs’credit risk evaluation.Our proposed framework quantifies relational risk by weighting the impact of publicly available risk events of each firm in an interfirm network—considering the impact of interfirm network type,risk event type,and time dependence of risk events—and combines the relational risk score with financial and demographic features to evaluate SMEs credit risk.Our results reveal that relational risk score significantly improves both discrimination and granting performances of credit risk evaluation of SMEs,providing valuable managerial and practical implications for financial institutions.

基于中文社交媒体文本的领域情感词典构建方法研究

【目的】从社交媒体用户生成内容中发现未知情感词,构造领域情感词典,应用于汽车评论的情感分析。【方法】选取HowNet情感词典作为种子,以实际汽车评论作为语料,分别利用PMI和Word2Vec算法识别新词情感极性,根据集成规则对二者识别结果综合判定,通过情感分类实验对比显示本文算法的有效性。【结果】按照该方法构造的情感词典准确率比How Net情感词典提高21.6%,较分别使用PMI和Word2Vec算法构建的词典分别提升3.7%和2.1%,同时正面、负面情感词数量均有大幅增加。【局限】语料来源单一,应用于其他领域具有一定局限性。【结论】该方法构造的情感词典可有效应用于社交媒体文本情感分析。