Do emotional strategies work? Evidence from rumor clarification announcement
DOI: https://doi.org/10.3846/jbem.2025.23790Abstract
Financial markets are filled with rumors because of information asymmetry. Although issuing clarification announcements is the most straightforward approach for organizations, previous research has mostly focused on analyzing the influence of rumors and the heterogeneity of their clarification statements on the efficacy of rumor management. This study investigates how mood elements influence the effectiveness of 335 rumor clarification statements in China's A-share market from 2019 to 2023. By employing textual sentiment analysis, event study method, and fixed-effects regression models, the primary results indicate that rumors vary in their characteristics and have diverse effects on stock price volatility. Furthermore, we find that clarification announcements effectively restore stock values, though their influence on negative rumors is somewhat restricted. Announcements with a positive mood greatly improve the effectiveness of clarification, particularly when addressing favorable rumors. The level of transparency and the characteristics of the firm's information influence the impact of sentiment. Furthermore, the positive impact of sentiment is more noticeable in firms that are extremely transparent or not owned by the state.
Keywords:
clarification announcements, event study method, text analysis, emotional language, emotional strategies, rumorsHow to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abdi, A., Shamsuddin, S. M., Hasan, S., & Piran, J. (2019). Deep learning-based sentiment classification of evaluative text based on Mul-ti-feature fusion. Information Processing & Management, 56(4), 1245–1259. https://doi.org/10.1016/j.ipm.2019.02.018> https://doi.org/10.1016/j.ipm.2019.02.018
Agarwal, P., Al Aziz, R., & Zhuang, J. (2022). Interplay of rumor propagation and clarification on social media during crisis events-A game-theoretic approach. European Journal of Operational Research, 298(2), 714–733. https://doi.org/10.1016/j.ejor.2021.06.060> https://doi.org/10.1016/j.ejor.2021.06.060
Ahern, K. R., & Sosyura, D. (2015). Rumor has it: Sensationalism in financial media. The Review of Financial Studies, 28(7), 2050–2093. https://doi.org/10.1093/rfs/hhv006> https://doi.org/10.1093/rfs/hhv006
Alzahrani, A. I., Sarsam, S. M., Al-Samarraie, H., & Alblehai, F. (2023). Exploring the sentimental features of rumor messages and investors’ intentions to invest. International Review of Economics & Finance, 87, 433–444. https://doi.org/10.1016/j.iref.2023.05.006> https://doi.org/10.1016/j.iref.2023.05.006
Betton, S., Davis, F., & Walker, T. (2018). Rumor rationales: The impact of message justification on article credibility. International Review of Financial Analysis, 58, 271–287. https://doi.org/10.1016/j.irfa.2018.03.013> https://doi.org/10.1016/j.irfa.2018.03.013
Cai, W., Quan, X., & Zhu, Z. (2023). Rumors in the sky: Corporate rumors and stock price synchronicity. International Review of Financial Analy-sis, 88, Article 102683. https://doi.org/10.1016/j.irfa.2023.102683> https://doi.org/10.1016/j.irfa.2023.102683
Do, H. H., Prasad, P. W. C., Maag, A., & Alsadoon, A. (2019). Deep learning for aspect-based sentiment analysis: A comparative review. Expert Systems with Applications, 118, 272–299. https://doi.org/10.1016/j.eswa.2018.10.003> https://doi.org/10.1016/j.eswa.2018.10.003
Fama, E. F. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575–1617. https://doi.org/10.1111/j.1540-6261.1991.tb04636.x> https://doi.org/10.1111/j.1540-6261.1991.tb04636.x
Ji, P., Chen, X., & Yu, L. (2024). Research on the effect of corporate rumor clarification based on text analysis. Asia-Pacific Journal of Accounting & Economics, 31(4), 654–671. https://doi.org/10.1080/16081625.2024.2333750> https://doi.org/10.1080/16081625.2024.2333750
Ji, P., Yan, X., & Yu, G. (2020). Can rumor clarification eliminate the effects of rumors?: Evidence from China. International Journal of Asian Busi-ness and Information Management (IJABIM), 11(1), 48–62. https://doi.org/10.4018/IJABIM.2020010103> https://doi.org/10.4018/IJABIM.2020010103
Jia, M., Ruan, H., & Zhang, Z. (2017). How rumors fly. Journal of Business Research, 72, 33–45. https://doi.org/10.1016/j.jbusres.2016.11.010> https://doi.org/10.1016/j.jbusres.2016.11.010
Jia, W., Redigolo, G., Shu, S., & Zhao, J. (2020). Can social media distort price discovery? Evidence from merger rumors. Journal of Accounting and Economics, 70(1), Article 101334. https://doi.org/10.1016/j.jacceco.2020.101334> https://doi.org/10.1016/j.jacceco.2020.101334
Ke, Y., Zhu, L., Wu, P., & Shi, L. (2022). Dynamics of a reaction-diffusion rumor propagation model with non-smooth control. Applied Mathe-matics and Computation, 435, Article 127478. https://doi.org/10.1016/j.amc.2022.127478> https://doi.org/10.1016/j.amc.2022.127478
Li, J., Jiang, H., Yu, Z., & Hu, C. (2019). Dynamical analysis of rumor spreading model in homogeneous complex networks. Applied Mathematics and Computation, 359, 374–385. https://doi.org/10.1016/j.amc.2019.04.076> https://doi.org/10.1016/j.amc.2019.04.076
Li, Z., Zhang, Q., Du, X., Ma, Y., & Wang, S. (2021). Social media rumor refutation effectiveness: Evaluation, modelling and enhancement. Infor-mation Processing & Management, 58(1), Article 102420. https://doi.org/10.1016/j.ipm.2020.102420> https://doi.org/10.1016/j.ipm.2020.102420
Lin, W. T., Tsai, S. C., & Lung, P. Y. (2013). Investors’ herd behavior: Rational or irrational? Asia-Pacific Journal of Financial Studies, 42(5), 755–776. https://doi.org/10.1111/ajfs.12030> https://doi.org/10.1111/ajfs.12030
Liu, W., Wu, X., Yang, W., Zhu, X., & Zhong, S. (2019). Modeling cyber rumor spreading over mobile social networks: A compartment ap-proach. Applied Mathematics and Computation, 343, 214–229. https://doi.org/10.1016/j.amc.2018.09.048> https://doi.org/10.1016/j.amc.2018.09.048
Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35–65. https://doi.org/10.1111/j.1540-6261.2010.01625.x> https://doi.org/10.1111/j.1540-6261.2010.01625.x
Mantyla, M. V., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis – A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32. https://doi.org/10.1016/j.cosrev.2017.10.002> https://doi.org/10.1016/j.cosrev.2017.10.002
Pal, A., Chua, A. Y., & Goh, D. H.-L. (2017). Does KFC sell rat? Analysis of tweets in the wake of a rumor outbreak. Aslib Journal of Information Management, 69(6), 660–673. https://doi.org/10.1108/AJIM-01-2017-0026> https://doi.org/10.1108/AJIM-01-2017-0026
Radechovsky, J., Berger, P., & Wolling, J. (2019). Nothing’s gonna change my world – or do journalistic clarifications help against rumors? SCM Studies in Communication and Media, 8(4), 497–522. https://doi.org/10.5771/2192-4007-2019-4-497> https://doi.org/10.5771/2192-4007-2019-4-497
Rezaeinia, S. M., Rahmani, R., Ghodsi, A., & Veisi, H. (2019). Sentiment analysis based on improved pre-trained word embeddings. Expert Systems with Applications, 117, 139–147. https://doi.org/10.1016/j.eswa.2018.08.044> https://doi.org/10.1016/j.eswa.2018.08.044
Rubin, V. L. (2017). Deception detection and rumor debunking for social media. In L. Sloan & A. Quan-Haase, The SAGE handbook of social media research methods (pp. 342–363). Sage. https://doi.org/10.4135/9781473983847.n21> https://doi.org/10.4135/9781473983847.n21
Shapiro, A. H., Sudhof, M., & Wilson, D. J. (2022). Measuring news sentiment. Journal of Econometrics, 228(2), 221–243. https://doi.org/10.1016/j.jeconom.2020.07.053> https://doi.org/10.1016/j.jeconom.2020.07.053
Soleymani, M., Garcia, D., Jou, B., Schuller, B., Chang, S.-F., & Pantic, M. (2017). A survey of multimodal sentiment analysis. Image and Vision Computing, 65, 3–14. https://doi.org/10.1016/j.imavis.2017.08.003> https://doi.org/10.1016/j.imavis.2017.08.003
Tan, Y., Zhang, W., & Kong, X. (2023). Market manipulation by rumormongers: Evidence from insiders’ stock selling. China Journal of Ac-counting Research, 16(3), Article 100318. https://doi.org/10.1016/j.cjar.2023.100318> https://doi.org/10.1016/j.cjar.2023.100318
Tavor, T. (2013). Target price rumor and its effect on market efficiency. The Journal of Investing, 22(4), 93–102. https://doi.org/10.3905/joi.2013.22.4.093> https://doi.org/10.3905/joi.2013.22.4.093
Tian, Y., & Ding, X. (2019). Rumor spreading model with considering debunking behavior in emergencies. Applied Mathematics and Computa-tion, 363, Article 124599. https://doi.org/10.1016/j.amc.2019.124599> https://doi.org/10.1016/j.amc.2019.124599
Voas, J. M. (2002). Corporate rumors and conspiracy theories. IT Professional, 4(2), 62–64. https://doi.org/10.1109/MITP.2002.1000472> https://doi.org/10.1109/MITP.2002.1000472
Wang, J., Xie, Z., Li, Q., Tan, J., Xing, R., Chen, Y., & Wu, F. (2019). Effect of digitalized rumor clarification on stock markets. Emerging Markets Finance and Trade, 55(2), 450–474. https://doi.org/10.1080/1540496X.2018.1534683> https://doi.org/10.1080/1540496X.2018.1534683
Wang, Q., & Song, P. (2015). Is positive always positive? The effects of precrisis media coverage on rumor refutation effectiveness in social media. Journal of Organizational Computing and Electronic Commerce, 25(1), 98–116. https://doi.org/10.1080/10919392.2015.990785> https://doi.org/10.1080/10919392.2015.990785
Wang, X., Chao, F., & Yu, G. (2021). Evaluating rumor debunking effectiveness during the COVID-19 pandemic crisis: Utilizing user stance in comments on Sina Weibo. Frontiers in Public Health, 9, Article 770111. https://doi.org/10.3389/fpubh.2021.770111> https://doi.org/10.3389/fpubh.2021.770111
Wang, Y., Tang, Y., Zuo, J., & Bartsch, K. (2022). Exploring rumor combating behavior of social media on NIMBY conflict: Temporal modes, frameworks and strategies. Environmental Impact Assessment Review, 96, Article 106839. https://doi.org/10.1016/j.eiar.2022.106839> https://doi.org/10.1016/j.eiar.2022.106839
Wang, Z., Zhao, H., & Nie, H. (2020). Bibliometric analysis of rumor propagation research through Web of Science from 1989 to 2019. Journal of Statistical Physics, 178, 532–551. https://doi.org/10.1007/s10955-019-02440-y> https://doi.org/10.1007/s10955-019-02440-y
Wu, C., Xiong, X., Gao, Y., & Zhang, J. (2022). Does social media distort price discovery? Evidence from rumor clarifications. Research in Inter-national Business and Finance, 62, Article 101749. https://doi.org/10.1016/j.ribaf.2022.101749> https://doi.org/10.1016/j.ribaf.2022.101749
Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Natural language based financial forecasting: A survey. Artificial Intelligence Review, 50(1), 49–73. https://doi.org/10.1007/s10462-017-9588-9> https://doi.org/10.1007/s10462-017-9588-9
Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019a). Sentiment analysis of comment texts based on BiLSTM. IEEE Access, 7, 51522–51532. https://doi.org/10.1109/ACCESS.2019.2909919> https://doi.org/10.1109/ACCESS.2019.2909919
Xu, G., Yu, Z., Yao, H., Li, F., Meng, Y., & Wu, X. (2019b). Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access, 7, 43749–43762. https://doi.org/10.1109/ACCESS.2019.2907772> https://doi.org/10.1109/ACCESS.2019.2907772
Xu, Q., Chang, V., & Hsu, C.-H. (2020). Event study and principal component analysis based on sentiment analysis – A combined methodolo-gy to study the stock market with an empirical study. Information Systems Frontiers, 22(5), 1021–1037. https://doi.org/10.1007/s10796-020-10024-5> https://doi.org/10.1007/s10796-020-10024-5
Yang, A. S. (2020). Misinformation corrections of corporate news: Corporate clarification announcements. Pacific-Basin Finance Journal, 61, Article 101315. https://doi.org/10.1016/j.pacfin.2020.101315> https://doi.org/10.1016/j.pacfin.2020.101315
Yang, X., & Luo, Y. (2014). Rumor clarification and stock returns: Do bull markets behave differently from bear markets? Emerging Markets Finance and Trade, 50(1), 197–209. https://doi.org/10.2753/REE1540-496X500111> https://doi.org/10.2753/REE1540-496X500111
Zhang, H., Chen, Y., Rong, W., Wang, J., & Tan, J. (2022a). Effect of social media rumors on stock market volatility: A case of data mining in China. Frontiers in Physics, 10, Article 987799. https://doi.org/10.3389/fphy.2022.987799> https://doi.org/10.3389/fphy.2022.987799
Zhang, Y., Xu, J., Nekovee, M., & Li, Z. (2022b). The impact of official rumor-refutation information on the dynamics of rumor spread. Physica A: Statistical Mechanics and its Applications, 607, Article 128096. https://doi.org/10.1016/j.physa.2022.128096> https://doi.org/10.1016/j.physa.2022.128096
Zhang, W., & Wang, C. (2024). Rumors and price efficiency in stock market: An empirical study of rumor verification on investor Interactive platforms. China Journal of Accounting Research, 17(2), Article 100356. https://doi.org/10.1016/j.cjar.2024.100356> https://doi.org/10.1016/j.cjar.2024.100356
Zhang, Z., Mei, X., Jiang, H., Luo, X., & Xia, Y. (2023). Dynamical analysis of hyper-SIR rumor spreading model. Applied Mathematics and Com-putation, 446, Article 127887. https://doi.org/10.1016/j.amc.2023.127887> https://doi.org/10.1016/j.amc.2023.127887
Zhu, L., Zhu, Z., Zhang, C., Xu, Y., & Kong, X. (2023). Multimodal sentiment analysis based on fusion methods: A survey. Information Fusion, 95, 306–325. https://doi.org/10.1016/j.inffus.2023.02.028> https://doi.org/10.1016/j.inffus.2023.02.028
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2018). Detection and resolution of rumours in social media: A survey. ACM Computing Surveys (CSUR), 51(2), 1–36. https://doi.org/10.1145/3161603> https://doi.org/10.1145/3161603
View article in other formats
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.