Article2019

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

Lu Wei · Guowen Li · Xiaoqian Zhu · Jianping Li

85 citations0/0 files storedAccounting and Finance

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Banking stability, regulation, efficiency Financial Distress and Bankruptcy Prediction Computational and Text Analysis Methods

Abstract

Abstract This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi‐supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.

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References

2013Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts 2008Bank governance, regulation and risk taking 2010The Information Content of Mandatory Risk Factor Disclosures in Corporate Filings 2017The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation 2014Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures 2004A General Approach to Integrated Risk Management with Skewed, Fat-tailed Risks*2003Cross-border bank mergers: What lures the rare animal?2000The intersection of market and credit risk

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