Financial report similarity and the likelihood of administrative punishment:based on the empirical evidence of textual analysis

Qian, Aimin; Zhu, Daoben

doi:10.1080/21697213.2019.1642604

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…Underperforming companies may intentionally obscure negative operational information and conceal unfavorable news by reducing readability [ 13 , 14 ]. Moreover, such companies usually exhibit negative disclosure attitudes and limited operational progress, resulting in higher MD&A text similarity [ 15 , 59 ]. In our study, we explore the early warning effect of these two indicators on financial crises using MD&A texts from Chinese listed companies.…”

Section: Further Discussionmentioning

confidence: 99%

“…Analysis of empirical results of financial crisis early warning based on traditional financial indicators and MD&A text similarity indicator. High MD&A text similarity indicates that listed companies engage in passive and perfunctory information disclosure, treating it as a mere formality, lacking objective analysis and timely updates on their actual operating situation during the reporting period, or showing limited progress compared to the past [59]. In the original data, ST-listed companies exhibit higher MD&A text similarity, with a mean value of 0.6377, while non-ST-listed companies have a mean value of 0.5666 for this indicator.…”

Section: Plos Onementioning

confidence: 99%

“…Text similarity often indicates the company management’s disclosure willingness, capability, or operational aspects. Firstly, motivated by the desire to conceal negative news, company management may adopt a passive stance towards information disclosure, prioritizing form over substance [ 59 ]. Secondly, higher text similarity may stem from low transparency within the company, hindering effective information transfer, or management’s lack of sufficient and effective analytical skills [ 60 ].…”

Section: Related Studiesmentioning

confidence: 99%

“…Secondly, higher text similarity may stem from low transparency within the company, hindering effective information transfer, or management’s lack of sufficient and effective analytical skills [ 60 ]. Thirdly, high similar information may suggest that the company’s operating conditions in the current or future periods show little significant changes compared to the past [ 59 ]. Cohen et al [ 15 ], in their study of the U.S. market, found that changes in the language and structure of financial reports have a strong impact on future returns and operations of companies, with the degree of these changes predicting future earnings, profitability, financial crises and even company bankruptcies.…”

Section: Related Studiesmentioning

confidence: 99%

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Financial crisis early warning of Chinese listed companies based on MD&A text-linguistic feature indicators

Zhang,

Liu,

Niu

2023

PLoS ONE

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Nowadays, the international situation is severe and complex, and the structural issues within the Chinese economy are prominent. Consequently, the financial risks faced by Chinese listed companies continue to escalate. Hence, it is of great practical significance to furnish effective early warnings for financial crises in listed companies. In this paper, we first employ web crawler technology and natural language processing technology to assess four text-linguistic features in the Management Discussion and Analysis (MD&A) section of the annual financial reports of listed companies in China from 2011 to 2020. These features are text tone, forward-looking, readability and similarity. Subsequently, we combine these features with traditional financial indicators and explore thirteen mainstream machine learning models to comparatively analyze their effectiveness in predicting financial crises in listed companies. The empirical findings of this research reveal that MD&A text readability and similarity indicators contribute valuable incremental information to prediction models, whereas text tone and forward-looking indicators exhibit the opposite effect. The latter two indicators can be manipulated more effortlessly by management, as this study’s empirical findings indicate no evidence of their contributions in incremental informational value. In fact, the forward-looking indicator even introduces additional noise to the prediction. Finally, by comparing the early warning effects of thirteen machine learning models, it is found that RF, Bagging, CatBoost, GBDT, XGBoost and LightGBM models maintain stable and accurate sample recognition ability. In general, this paper constructs a more effective financial crisis early warning model by exploring the MD&A text-linguistic feature indicators, thereby offering a fresh research perspective for further investigations in this field.

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Section: Further Discussionmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

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Financial crisis early warning of Chinese listed companies based on MD&A text-linguistic feature indicators

Zhang,

Liu,

Niu

2023

PLoS ONE

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“…Despite the importance of textual analysis in fraud prediction, its application in emerging countries remains limited. Notably, a study in China focused on textual analysis to investigate financial reports of listed companies, primarily examining the likelihood of administrative punishments based on non-financial influence [23].…”

Section: Introductionmentioning

confidence: 99%

Predicting likelihood of fraud among financial distressed firms in Malaysia using textual analysis

Marzuki,

Md Nasir,

Mat Zain

et al. 2024

IJEECS

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This research paper aims to analyze and predict fraud patterns among failed companies in Malaysia. The approach involves utilizing textual analysis on the management discussion and analysis (MD&A) section within the annual reports. The dataset is subjected to text clustering to group companies based on similar financial characteristics. This clustering process entails several steps, including data conversion, collation, and summarization into a structured format, followed by text pre-processing to cleanse the dataset. Notably, RapidMiner Studio software was utilized to extract data for the study. Subsequently, the documents are clustered using both the K-means and latent dirichlet allocation (LDA) methods. Upon examining a sample of 22 failed companies in the year 2020, the study reveals that financially distressed companies exhibit prominent financial negativity and utilize litigious financial terms within their MD&A sections. These linguistic traits are found to be closely associated with seven distinct characteristics of fraudulent firms. This preliminary findings provide compelling evidence that financial pressure may serve as a triggering factor for fraudulent activities within companies.

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Tone, Readability, and Firm Performance: A Study of Chairman’s Letter Across India and China

Bhuyan,

Baid,

Baid

et al. 2023

Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth

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Financial report similarity and the likelihood of administrative punishment:based on the empirical evidence of textual analysis

Cited by 8 publications

References 34 publications

Financial crisis early warning of Chinese listed companies based on MD&A text-linguistic feature indicators

Financial crisis early warning of Chinese listed companies based on MD&A text-linguistic feature indicators

Predicting likelihood of fraud among financial distressed firms in Malaysia using textual analysis

Tone, Readability, and Firm Performance: A Study of Chairman’s Letter Across India and China

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