Identifying the Characteristics and Dimensions of Emotional Tone in Companies’ Annual Reports and Examining Its Relationship with Financial Distress

Authors

    Majid Jahan Tigh Department of Accounting, Ya.C., Islamic Azad University, Yazd, Iran
    Akram Taftian * Department of Accounting, Ya.C., Islamic Azad University, Yazd, Iran Taftiyan@iau.ac.ir
    Mahmoud Moeinaddin Department of Accounting, Ya.C., Islamic Azad University, Yazd, Iran

Keywords:

Financial Distress, Emotional Tone, Annual Reports, Thematic Analysis

Abstract

This study aims to identify the dimensions of emotional tone in annual reports and examine their relationship with the probability of corporate financial distress to provide qualitative indicators complementing traditional predictive models. A qualitative approach using thematic analysis was employed. Data were collected through semi-structured interviews with 15 financial managers and expert analysts selected purposefully based on at least five years of experience analyzing financial reports. The analysis process included open, axial, and selective coding. Credibility and reliability were ensured through Guba and Lincoln’s criteria, and inter-coder agreement was measured using Krippendorff’s alpha and Cohen’s kappa. The results revealed that negative emotional tone—including financial concerns, profitability decline, liquidity issues, and economic crises—acts as an early warning indicator of financial distress. In contrast, positive tone, reflected in sustainable growth, improved cash flow, and reduced debt, indicates financial stability and lower distress risk. Ambiguity regarding economic outlook and long-term strategies, as well as references to managerial weaknesses, were also associated with increased distress risk. Moreover, external factors such as economic volatility, sanctions, and legal issues significantly influenced the tone of reports and distress prediction. Analyzing the emotional tone of annual reports can serve as a complementary tool to quantitative models for predicting financial distress, enabling analysts, managers, and investors to identify early warning signals of financial crises. It is recommended that companies and regulators integrate thematic analysis and natural language processing into financial health assessment frameworks.

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References

Abedin, M. Z., Guotai, C., & Hajek, P. (2022). Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk. Complex Intelligent Systems, 1-21. https://doi.org/10.1007/s40747-021-00614-4

Alaka, H. A., Oyedele, L. O., & Owolabi, H. A. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. https://doi.org/10.1016/j.eswa.2017.10.040

Ali, M., Mirarab Bayegi, S. A., & Farjian, N. (2022). A model for predicting bankruptcy risk in listed and non-listed companies using machine learning algorithms.

Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.

Bhavan, A., Chauhan, P., & Shah, R. R. (2019). Bagged support vector machines for emotion recognition from speech. Knowledge-Based Systems, 184, 886. https://doi.org/10.1016/j.knosys.2019.104886

Byrne, D. (2022). A worked example of Braun and Clarke's approach to reflexive thematic analysis. Quality & Quantity, 56(3), 1391-1412.

Cao, S., Jiang, W., & Yang, B. (2020). How to talk when a machine is listening: Corporate disclosure in the age of AI. et al. (Ed.),

Chen, Y. S., Lin, C. K., Lo, C. M., Chen, S. F., & Liao, Q. J. (2021). Comparable studies of financial bankruptcy prediction using advanced hybrid intelligent classification models to provide early warning in the electronics industry. Mathematics, 9(20), 2622. https://doi.org/10.3390/math9202622

Garain, A., Ray, B., & Giampaolo, F. (2022). Grann: Feature selection with golden ratio-aided neural network for emotion, gender, and speaker identification from voice signals. Neural Computing and Applications, 34(17), 14463-14486. https://doi.org/10.1007/s11042-021-01182-y

Goel, D. P., Mahajan, K., & Nguyen, N. D. (2023). Towards an efficient backbone for preserving features in speech emotion recognition: Deep-shallow convolution with recurrent neural network. Neural Computing and Applications, 35(3), 2457-2469. https://doi.org/10.1007/s00521-022-07723-2

Gregova, E., Valaskova, K., Adamko, P., Tumpach, M., & Jaros, J. (2020). Predicting financial distress of Slovak enterprises: Comparison of selected traditional and learning algorithms methods. Sustainability, 12(10), 3954. https://doi.org/10.3390/su12103954

Gullo, F., Ferreira, P. M., & Roqueiro, D. (2023). Machine learning and principles and practice of knowledge discovery in databases. In et al. (Ed.), (pp. 53-61). https://doi.org/10.1007/978-3-031-23633-4_5

Hajek, P., Barushka, A., & Munk, M. (2020). Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Computing and Applications, 32(23), 17259-17274. https://doi.org/10.1007/s00521-020-04757-2

Hobson, J. L., Mayew, W. J., & Venkatachalam, M. (2012). Analyzing speech to detect financial misreporting. Journal of Accounting Research, 50(2), 349-392. https://doi.org/10.1111/j.1475-679X.2011.00433.x

Huang, B., Yao, X., & Luo, Y. (2022). Improving financial distress prediction using textual sentiment of annual reports. Annals of Operations Research, 1-28. https://doi.org/10.1007/s10479-022-04633-3

Issa, D., Demirci, M. F., & Yazici, A. (2020). Speech emotion recognition with deep convolutional neural networks. Biomedical Signal Processing and Control, 59, 101894. https://doi.org/10.1016/j.bspc.2020.101894

Li, S., Shi, W., & Wang, J. (2021). A deep learning-based approach to constructing a domain sentiment lexicon: A case study in financial distress prediction. Information Processing & Management, 58(5), 102673. https://doi.org/10.1016/j.ipm.2021.102673

Liang, D., Tsai, C. F., & Lu, H. Y. R. (2020). Combining corporate governance indicators with stacking ensembles for financial distress prediction. Journal of Business Research, 120, 137-146. https://doi.org/10.1016/j.jbusres.2020.07.052

Livieris, I. E., Stavroyiannis, S., & Iliadis, L. (2021). Smoothing and stationarity enforcement framework for deep learning time-series forecasting. Neural Computing and Applications, 33(20), 14021-14035. https://doi.org/10.1007/s00521-021-06043-1

Lotfi, B., Bahri Thaleth, J., Jabarzadeh Kangerlouei, S., & Heydari, M. (2024). Predicting financial distress using a combined model (Case study: Companies listed in the Tehran Stock Exchange). Investment Science Journal, 13(50), 349-370.

Martono, N. P., & Ohwada, H. (2023). Financial distress model prediction using machine learning: A case study on Indonesia's consumer cyclical companies. https://doi.org/10.1007/978-3-031-23633-4_5

Marzuki, H., Hasnan, S., & Ali, M. M. (2022). Contemporary review of corruption risk studies. Corporate Governance and Organizational Behavior Review, 6(2), 255-267. https://doi.org/10.22495/cgobrv6i2sip10

Mehrabi, R., Hematfar, M., & Safati, F. (2024). Predicting financial distress of companies using a combined artificial immune system model and wavelet neural network (Artificial Intelligence). Technology in Entrepreneurship and Strategic Management, 3(4). https://doi.org/10.61838/kman.jtesm.3.4.10

Mohammadi, S., & Sirani, M. (2023). Predicting company financial risk based on embedded systems and deep learning. First International Conference on Management, Accounting, and Economics with a Focus on the Future, Bushehr.

Nazarian, R., Taftian, A., & Heyrani, F. (2023). Application of Sterling's content analysis in analyzing environmental reporting indicators. Value and Behavioral Accounting Journal, 7(14), 405-432. https://doi.org/10.61186/aapc.7.14.405

Nguyen, L. Q. T., & Ahmed, R. (2023). The impact of economic sanctions on foreign direct investment: Empirical evidence from global data. Journal of Economics and Development, 25(1), 79-99. https://doi.org/10.1108/JED-10-2022-0206

Rahman, M., Sa, C. L., & Masud, M. A. K. (2023). Predicting firms' financial distress: An empirical analysis using the F-score model. Journal of Risk and Financial Management, 14(5), 199. https://doi.org/10.3390/jrfm14050199

Sethi, S. R. (2025). Forecasting financial distress for organizational sustainability: An empirical analysis. Sustainable Futures, 9, 100429. https://doi.org/10.1016/j.sftr.2024.100429

Sheikhzadeh, M., & Bani Asad, R. (2020). Content analysis: Concepts, approaches, and applications. Logos Publications.

Toakeli, S., & Ashtab, A. (2023). Comparison of the performance of machine learning models and statistical models in financial risk prediction. Financial Management Strategy, 11(1), 53-76.

Wang, G., Ma, J., & Chen, G. (2020). Financial distress prediction: Regularized sparse-based random subspace with ER aggregation rule incorporating textual disclosures. Applied Soft Computing, 90, 152. https://doi.org/10.1016/j.asoc.2020.106152

Zeng, Y., Mao, H., & Peng, D. (2019). Spectrogram-based multi-task audio classification. Multimedia Tools and Applications, 78, 3705. https://doi.org/10.1007/s11042-017-5539-3

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Published

2025-11-06

Submitted

2025-06-24

Revised

2025-09-15

Accepted

2025-09-28

Issue

Section

Articles

How to Cite

Jahan Tigh, M. ., Taftian, A., & Moeinaddin, M. . . (1404). Identifying the Characteristics and Dimensions of Emotional Tone in Companies’ Annual Reports and Examining Its Relationship with Financial Distress. Accounting, Finance and Computational Intelligence, 1-17. https://jafci.com/index.php/jafci/article/view/188

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