讲座题目：《What drives reputational risk? Evidence fromtextual risk disclosures in financial statements》
The drivers of reputational risk are still far from explicit, making proactive risk management and quantitative research rather difficult. The Basel Committee on Banking Supervision encourages financial institutions to systematically identify reputational risk drivers; however, such drivers still represent an unsolved problem. Therefore, the objective of this paper is to systemically identify reputational risk drivers from textual risk disclosures in financial reports. We find that textual risk disclosures in financial reports contain abundant information about the causes of reputational risk, thus indicating the possibility of systematically identifying the reputational risk drivers. To accurately extract reputational risk drivers from massive and unstructured textual risk disclosure data, we modify a text mining method to make it more suitable for this type of textual data with noise words. Based on 352,326 risk headings extracted from 11,921 annual reports released by 1570 U.S. financial institutions from 2006 to 2019, a total of 13 reputational risk drivers are identified to extend upon existing studies. The importance of reputational risk drivers and their dynamic evolutions are also quantified to discover the drivers of greatest concern. This paper can clarify the sources of reputational risk to help companies realize proactive reputational risk management and provide a theoretical basis for further quantitative studies, especially the measurement of reputational risk.
王颖慧，管理学博士，中国科学院大学经济与管理学院特别研究助理，获NSFC青年基金、博士后面上项目。围绕金融风险管理、操作风险、声誉风险领域开展了一系列研究工作，成果发表于Humanities & Social Sciences Communications, Journal of Operational Risk,《系统工程理论与实践》、《金融监管研究》等国内外主流学术期刊。获得第9届信息技术与量化管理国际会议、第10届商务智能与金融工程国际会议最佳论文奖等奖励。