Mining risk-related sentiment in corporate annual reports and its effect on financial performance
Abstract
Models that predict corporate financial risk are important early-warning systems for corporate stakeholders. Most models to date have been developed using financial indicators. However, in financial decision-making, increasing attention is being paid to the role of textual information, which may provide additional insight into managerial opinions and intentions and which has recently been used to more effectively predict corporate financial performance. Previous approaches in this regard have predominantly focused on sentiment analysis of managerial communication. However, the role of context-related sentiment remains poorly understood in the financial risk domain. Here, we investigate how risk-related sentiment in verbal managerial communication might predict corporate financial performance, including indebtedness, profitability, market value and bankruptcy risk. To ensure deductive content validity, we propose specific word lists for each type of corporate financial risk and assign each word with positive / negative labels. Our findings provide evidence for a major role of risk-related sentiment as an indicator of corporate performance in terms of financial risks. Notably, using novel risk-related word lists in regression models, we show that a proactive and opportunity-seeking risk management has a significantly positive impact on financial performance, implying that stakeholders should carefully consider the risk-related managerial communication in corporate annual reports.
First published online 19 November 2020
Keyword : sentiment, financial risk, annual report, managerial communication, financial performance
How to Cite
Myšková, R., & Hájek, P. (2020). Mining risk-related sentiment in corporate annual reports and its effect on financial performance. Technological and Economic Development of Economy, 26(6), 1422-1443. https://doi.org/10.3846/tede.2020.13758
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Džunić, M., & Golubović, N. (2018). Perceived corruption in business environment: Exploring the underlying causes. E&M Economics and Management, 21(4), 48–64. https://doi.org/10.15240/tul/001/2018-4-004
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Frank, M. Z., & Goyal, V. K. (2009). Capital structure decisions: which factors are reliably important? Financial Management, 38(1), 1–37. https://doi.org/10.1111/j.1755-053X.2009.01026.x
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Gastineau, G. L., & Kritzman, M. P. (1999). Dictionary of financial risk management (Vol. 52). John Wiley & Sons.
Ghosh, C. A. (1993). Regret-theoretic explanation of corporate dividend policy. Journal of Business Finance and Accounting, 20(4), 559–573. https://doi.org/10.1111/j.1468-5957.1993.tb00274.x
Goel, S., & Uzuner, O. (2016). Do sentiments matter in fraud detection? Estimating semantic orientation of annual reports. Intelligent Systems in Accounting, Finance and Management, 23(3), 215–239. https://doi.org/10.1002/isaf.1392
Gray, G. L., & Debreceny, R. S. (2014). A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), 357–380. https://doi.org/10.1016/j.accinf.2014.05.006
Greco, M., Cricelli, L., & Grimaldi, M. (2013). A strategic management framework of tangible and intangible assets. European Management Journal, 31(1), 55–66. https://doi.org/10.1016/j.emj.2012.10.005
Groth, S. S., & Muntermann, J. (2011). An intraday market risk management approach based on textual analysis. Decision Support Systems, 50(4), 680–691. https://doi.org/10.1016/j.dss.2010.08.019
Guenster, N., Bauer, R., Derwall, J., & Koedijk, K. (2011). The economic value of corporate eco- efficiency. European Financial Management, 17(4), 679–704. https://doi.org/10.1111/j.1468-036X.2009.00532.x
Haimes, Y. Y. (2009). On the complex definition of risk: A systems‐based approach. Risk Analysis, 29(12), 1647–1654. https://doi.org/10.1111/j.1539-6924.2009.01310.x
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