![]() ![]() The effect is especially prominent based on the increasing impact of the internet and social media, where bad news in particular spreads quickly (Gatzert, 2015). Due to the very large amplification effect reputation damage has on losses, reputational risk has received increasing attention in recent years by managers, regulators, and academics (Gatzert et al., 2016 Vig et al., 2017 Cornejo et al., 2019). For example, the rogue trader scandal that impacted the United Bank of Switzerland (UBS) in 2011 led to an operational loss of ~2 billion dollars moreover, the company’s reputation deteriorated, and it eventually lost 6.3 billion dollars’ in market value (Eckert and Gatzert, 2017). The necessity of managing reputational risk is especially important for financial institutions whose business models are based on trust (Gatzert, 2015 Heidinger and Gatzert, 2018 Scholtens and Klooster, 2019). The reputation of a company is its most important asset but the most difficult asset to recover once it is lost (Scandizzo, 2011). 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. The importance of reputational risk drivers and their dynamic evolutions are also quantified to discover the drivers of greatest concern. financial institutions from 2006 to 2019, a total of 13 reputational risk drivers are identified to extend upon existing studies. Based on 352,326 risk headings extracted from 11,921 annual reports released by 1570 U.S. 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. 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. ![]() Therefore, the objective of this paper is to systemically identify reputational risk drivers from textual risk disclosures in financial reports. The Basel Committee on Banking Supervision encourages financial institutions to systematically identify reputational risk drivers however, such drivers still represent an unsolved problem. By the time you explained Bill's hesitant ellipses, the effect of Hughes' parentheses, and the significance of the word "wanted," you'd surely have three lines.The drivers of reputational risk are still far from explicit, making proactive risk management and quantitative research rather difficult. Or you could try to identify what exactly made you think her age was all he could think about. Instead of claiming that Bill thinks Mary is young and beautiful, the voice says "Well, sure, he thinks she's old, but that's not the only thing he thinks about." At that point, you could modify your claim. So try imagining a more complex voice disagreeing with you. The truth is, no one could read that story and imagine that Bill thinks Mary is young and beautiful. ![]() The Langston Hughes example above provides a good example of how you can expand your ideas. ![]() Do any of the words sometimes have multiple meanings? What are the connotations of each word? What is the tone? Notice that "stating the obvious" will help you meet the three-to-one rule. This can seem really daunting, but try to examine every word of the quotation. For every line you quote, you should plan to write at least three lines explaining what the quotation means and how it relates to the larger point of your paper. ![]()
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