Decoding corporate communication strategies: Analysing mandatory published information under Pillar 3 across turbulent periods with unsupervised machine learning.

IF 2.6 3区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
PLoS ONE Pub Date : 2025-07-31 eCollection Date: 2025-01-01 DOI:10.1371/journal.pone.0328841
Anna Pilková, Michal Munk, Lívia Kelebercová
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引用次数: 0

Abstract

This study explores the communication patterns of Slovak banks with stakeholders through mandatory disclosures mandated by Basel III's Pillar 3 framework and annual reports in 2007-2022. Our primary objective is to identify key topics communicated by banks and analysing the sentiment of this communication during turbulent periods (i.e., alternating periods of stability and crisis) in 2007-2022. Textual data was collected from Pillar 3 disclosures, annual reports, and additional regulatory reports. A hybrid model was developed to extract the most important keywords from each collected document chapter. This hybrid model (model combining multiple approaches) combines elements of statistical approaches to keyword extraction, (keyword frequency dictionary), linguistic approaches (pair-of-speech tagging in order to select noun-phrases), and machine-learning based approaches (BERT) to extract meaningful keywords. Subsequently, a sentiment analysis was performed on the extracted keywords using a Loughran-McDonald lexicon (list of words labelled with sentiment) specially designed for financial texts. Based on the adjusted univariate results, we can reject the global null hypothesis of independence of the sentiment category of keywords from time for negative sentiment at p = 0.0000 for positive sentiment at p = 0.0005, and for neutral sentiment at p = 0.0000 significant level. The multilevel comparison revealed that negative sentiment was most frequent during the global financial crisis and the COVID-19 pandemic, likely impacting stakeholder confidence and trust. Conversely, positive sentiment dominated during periods of financial stability, potentially enhancing stakeholder satisfaction and investment decisions. This research points out that the sentiment of the selected commercial bank documents changes depending on the years. A commercial bank can use this knowledge and include sentiment information as predictors when modelling financial distress. For bank management of selected commercial bank the examined documents are an important communication tool, the wording of which can have a significant impact on stakeholder behaviour towards the bank, their styling is very important.

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解码企业沟通策略:利用无监督机器学习分析第三支柱下的强制性发布信息。
本研究通过巴塞尔协议III第三支柱框架和2007-2022年年度报告的强制披露,探讨了斯洛伐克银行与利益相关者的沟通模式。我们的主要目标是确定银行沟通的关键主题,并分析2007-2022年动荡时期(即稳定和危机交替时期)这种沟通的情绪。文本数据收集自支柱3披露、年度报告和其他监管报告。开发了一个混合模型,从每个收集到的文档章节中提取最重要的关键字。这个混合模型(结合多种方法的模型)结合了关键字提取的统计方法(关键字频率字典)、语言学方法(选择名词短语的语音对标记)和基于机器学习的方法(BERT)的元素来提取有意义的关键字。随后,使用专门为金融文本设计的Loughran-McDonald词典(带有情感标签的单词列表)对提取的关键词进行情感分析。基于调整后的单变量结果,我们可以在p = 0.0000显著水平下,对p = 0.0005显著水平下的消极情绪、p = 0.0000显著水平下的积极情绪和p = 0.0000显著水平下的中性情绪,拒绝关键词情绪类别独立性的全局零假设。多层对比显示,负面情绪在全球金融危机和2019冠状病毒病大流行期间最为频繁,可能影响利益相关者的信心和信任。相反,在金融稳定时期,积极情绪占主导地位,可能会提高利益相关者的满意度和投资决策。本研究指出,所选商业银行文件的情绪随年份而变化。商业银行可以利用这些知识,并在建模财务困境时将情绪信息作为预测因素。对于选定商业银行的银行管理层来说,审查文件是一种重要的沟通工具,其措辞会对利益相关者对银行的行为产生重大影响,其风格非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PLoS ONE
PLoS ONE 生物-生物学
CiteScore
6.20
自引率
5.40%
发文量
14242
审稿时长
3.7 months
期刊介绍: PLOS ONE is an international, peer-reviewed, open-access, online publication. PLOS ONE welcomes reports on primary research from any scientific discipline. It provides: * Open-access—freely accessible online, authors retain copyright * Fast publication times * Peer review by expert, practicing researchers * Post-publication tools to indicate quality and impact * Community-based dialogue on articles * Worldwide media coverage
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