Tone of Textual Information in Annual Reports and Regulatory Inquiry Letters: Data from China

Yang, Fan; Huang, Jiayu; Cai, Yongbin

doi:10.1080/1540496x.2021.1903870

Cited by 12 publications

(7 citation statements)

References 20 publications

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“…However, it brings a certain amount of noise, which leads to a slight decrease in the overall mean value of AUC, ACC, SEN, and SPE. This is in line with some literature findings that management can manipulate and modify texts to increase the positive level of the emotional tone, which can be very disruptive to the early warning effect [42][43][44]. It is clear that texts disclosed internally by firms are vulnerable to manipulation by management.…”

Section: Further Discussionsupporting

confidence: 88%

“…The positive tone formed by this manipulative behavior affected the assessment of the firm's operating conditions, so it did not necessarily improve the early warning effect of the model [42,43]. Yang et al [44] also pointed out that company management may release positive information by modifying the text, and it would in turn reduce the accuracy of the early warning model of a company's financial crisis by adding emotional tone.…”

Section: The Emotional Tone Of Text and Financial Crisis Early Warningmentioning

confidence: 99%

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Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Zhang

Luo

et al. 2022

Applied Sciences

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Nowadays, China is faced with increasing downward pressure on its economy, along with an expanding business risk on listed companies in China. Listed companies, as the solid foundation of the national economy, once they face a financial crisis, will experience hazards from multiple perspectives. Therefore, the construction of an effective financial crisis early warning model can help listed companies predict, control and resolve their risks. Based on textual data, this paper proposes a web crawler and textual analysis, to assess the sentiment and tone of financial news texts and that of the management discussion and analysis (MD&A) section in annual financial reports of listed companies. The emotional tones of the two texts are used as external and internal information sources for listed companies, respectively, to measure whether they can improve the prediction accuracy of a financial crisis early warning model based on traditional financial indicators. By comparing the early warning effects of thirteen machine learning models, this paper finds that financial news, as external texts, can provide more incremental information for prediction models. In contrast, the emotional tone of MD&A, which can be easily modified by the management, will distort predictions. Comparing the early warning effect of machine learning models with different input feature variables, this paper also finds that DBGT, AdaBoost, random forest and Bagging models maintain stable and accurate sample recognition ability. This paper quantifies financial news texts, unraveling implied information hiding behind the surface, to further improve the accuracy of the financial crisis early warning model. Thus, it provides a new research perspective for related research in the field of financial crisis warnings for listed companies.

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Section: Further Discussionsupporting

confidence: 88%

Section: The Emotional Tone Of Text and Financial Crisis Early Warningmentioning

confidence: 99%

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Zhang

Luo

et al. 2022

Applied Sciences

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“…In other words, according to this view, managers may become optimistic after experiencing a few failures/negative events. A strand of financial and accounting studies has shown that an inquiry letter issued by regulators is associated with high litigation risk (Yang et al , 2022). Some studies show that the reaction of the stock market to a listed company’s reception of an inquiry letter is negative (Gietzmann and Isidro, 2013; Cao et al , 2022).…”

Section: Additional Analyses and Robustness Testsmentioning

confidence: 99%

Managerial sentiment and corporate risk-taking: evidence from Chinese listed companies’ annual reports

Qin,

Cui,

Yan

et al. 2023

MRR

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Purpose This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory. Design/methodology/approach By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment. Findings Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking. Research limitations/implications Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures. Practical implications To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class. Originality/value This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.

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“…Their analysis was based on the Australian Stock Exchange and excluded firms from the financial sector. As such, the study on the sentiment and audit pricing are rare across the literature (H. Chen et al, 2021;Dr agus , in et al, 2021;Gupta et al, 2020;Jokar & Daneshi, 2020;Luo & Zhou, 2020;Yang et al, 2021), that opens a research gap in respect of the financial industry which led us to the following working hypothesis: H1. Banks that provide positive sentiment in annual reports are associated with lower audit fees than those with negative.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Audit fee and banks’ communication sentiment

Staszkiewicz

Karkowska

2021

Economic Research-Ekonomska Istraživanja

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The study verifies the relationship between the audit fee and reporting sentiment in the banking industry. The research adopts panel data from a sample of 490 commercial banks across 43 US states for years 2000-2016. The results indicate that the audit fee is affected by the sentiment and is subject to time development. The research shed light on the nature of auditor relations in the specific economic context of the US. The results add to the science new and robust evidence on attitudes towards asymmetry friction in the supervised market. The Financial Authorities might benefit from the presented results while preselecting the entities for on-site inspection.

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Tone of Textual Information in Annual Reports and Regulatory Inquiry Letters: Data from China

Cited by 12 publications

References 20 publications

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Managerial sentiment and corporate risk-taking: evidence from Chinese listed companies’ annual reports

Audit fee and banks’ communication sentiment

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