Skip to main content
SpringerLink
Log in

Social media disclosure and reputational damage

  • Original Research
  • Published:
Review of Quantitative Finance and Accounting Aims and scope Submit manuscript

Abstract

We provide new evidence on the effects of social media in the context of a financial scandal using a sample of banks that were accused of manipulating the London Interbank Offered Rate (LIBOR). We find that increased bank Twitter activity when the scandal surfaced has a positive moderating effect on equity returns. However, the dissemination of content operated by social media users has a negative counterbalancing effect, thus amplifying the impact of the scandal. In particular, tweets that are unrelated to the scandal and characterized by positive sentiment contribute to exacerbating the reputational damage suffered by banks. We contribute to the emerging literature on the role of social media in capital markets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Notes

  1. We are not arguing that an abnormal increase in banks’ tweets during the event window would necessarily translate into a positive market reaction. Rather, we observe that, on average, banks that exhibit abnormally higher Twitter activity experience less negative market reaction to the scandal.

  2. Although we do not frame this as a repeated game (our main objective is, in fact, examining the short-term implications of social media disclosure), the very nature of the scandal event involves stalled news and the potential for reputational spillovers.

  3. EURIBOR is a reference rate overseen by the European Banking Federation. The EUROBOR contributor panel consisted of approximately 42 to 48 banks. Thomson Reuters also acts as an agent for the calculation and publishing of this rate. The administration process of this rate is highly similar to the one of LIBOR. However, only the highest and lowest 15% of all quotes are trimmed in EURIBOR calculation.

  4. See Monticini and Thornton (2013) for the effect of misrepresenting LIBOR rates.

  5. “In the matter of: The Royal Bank of Scotland PLC and RBS Securities Japan Limited”. US Commodity Futures Trading Commission. February 6, 2013.

  6. “Study casts doubt on key rate”. The Wall Street Journal, May 29, 2008.

  7. We restricted our sample to banks that are regular Twitter users by removing banks with less than 100 tweets per year during the sample period to rule out the possibility of capturing the effect of inactive Twitter users.

  8. We employ a propensity-score-matched sample rather than using all banks outside the LIBOR panel as a baseline group because different bank characteristics such as bank size (and associated information environment) and stock beta may yield different market reactions.

  9. We include the day before the event to account for the time difference between Europe and North America, and information leakage before the event date.

  10. Once a bank is categorized as an event bank, it cannot be classified as a nonevent bank any longer.

  11. We searched the following string using the Factiva free text form: Bank name and (“LIBOR” or “scandal” or “LIBOR scandal” or “manipulat*”). For publication sources, we selected “All Sources” to maximize our search results. To reduce potential measurement bias, we excluded article duplicates from our count.

  12. Within the LIBOR-related category we found different types of statements which can be linked to four types of events: settlement with authorities, management changes, board changes, and responsibility statement.

  13. There are two possible reasons for this: First, some banks have no Twitter activity in the event date interval; Second, some banks have no Twitter activity during the control window.

  14. To demonstrate that the market reaction to the LIBOR manipulation is negative, we also estimate a baseline model that ignores the effect of social media and document a negative and significant coefficient on the variable \(event\).

  15. In terms of the economic significance of our results, we base on the result in column 5 of Table 4 and interpret the finding as one standard deviation increase in the abnormal tweet volume being associated with a reduction in the negative returns due to the event, from –15% to –6%.

  16. TextBlob has been employed by various studies (e.g., Gauba et al., (2017); Perikos and Hatzilygeroudis, (2016)) and sentiment analysis on Twitters (e.g., Usha and Thampi, (2017); Hawkins et al., (2016)), as a proven sentiment analysis tool.

  17. To check the robustness of our results to the choice of the sentiment dictionary, we re-estimated the baseline model using Linguistic Inquiry and Word Count (LIWC) as an alternative dictionary for sentiment measurement, and the results remain qualitatively similar.

  18. We also develop a more stringent measure for \(sentiment\_res\) by regressing daily average sentiment on a set of common risk factors (i.e., \(ltb\), \(corp\), \(hy\), \(sov\), \(reit\), \(forex\), \(com\), and \(plt\)) that are additional to market risk. Results, unreported for brevity, remain qualitatively the same.

  19. To enhance the validity of the tweet sentiment test, we conducted an additional analysis. We parsed all the scandal-related tweets and identified a set of the most frequently occurring scandal-related keywords (e.g., “LIBOR”, “scandal”, “manipulation”, “settlement”, “investigation”, “sanction”). Subsequently, we performed textual analysis on the entire subsample of tweets characterized by positive sentiment, searching for these keywords. Any tweets containing at least one of these identified keywords was flagged as scandal related. Out of the 105,352 tweets characterized as having a positive sentiment, only 19 tweets were identified as containing misconduct-related content. This results in a ratio of 0.018%, providing reassurance regarding the nature of the majority of positive tweets as being unrelated to the scandal.

References

  • Al Guindy M, Naughton JP, Riordan R (2023) The evolution of corporate twitter usage. J Bus Financ Account 20:e45

    Google Scholar 

  • Alexander CR (1999) On the nature of the reputational penalty for corporate crime: evidence. J Law Econ 42(S1):489–526

    Article  Google Scholar 

  • Allen L, Saunders A (2004) Incorporating systemic influences into risk measurements: a survey of the literature. J Financ Serv Res 26(2):161–191

    Article  Google Scholar 

  • Amin MH, Mohamed EK, Elragal A (2021) CSR disclosure on twitter: evidence from the UK. Int J Account Inf Syst 40:100500

    Article  Google Scholar 

  • Araujo M, Reis J, Pereira A, Benevenuto F (2016). An evaluation of machine translation for multilingual sentence-level sentiment analysis. In: proceedings of the 31st annual ACM symposium on applied computing,

  • Armour J, Mayer C, Polo A (2017) Regulatory sanctions and reputational damage in financial markets. J Financ Quant Anal 52(4):1429–1448

    Article  Google Scholar 

  • Ball R, Brown P (1968) An empirical evaluation of accounting income numbers. J Account Res 6(2):159–178

    Article  Google Scholar 

  • Barakat A, Ashby S, Fenn P, Bryce C (2019) Operational risk and reputation in financial institutions: does media tone make a difference? J Bank Finance 98:1–24

    Article  Google Scholar 

  • Barber BM, Odean T (2008) All that glitters: the effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies 21(2):785–818

    Article  Google Scholar 

  • Bartov E, Faurel L, Mohanram PS (2018) Can twitter help predict firm-level earnings and stock returns? Account Rev 93(3):25–57

    Article  Google Scholar 

  • Beaver WH (1968) The information content of annual earnings announcements. J Account Res 6:67–92

    Article  Google Scholar 

  • Beaver WH, Clarke R, Wright WF (1979) The association between unsystematic security returns and the magnitude of earnings forecast errors. J Account Res 17(2):316–340

    Article  Google Scholar 

  • Bessler W, Kurmann P (2014) Bank risk factors and changing risk exposures: Capital market evidence before and during the financial crisis. J Financ Stab 13:151–166

    Article  Google Scholar 

  • Bessler W, Kurmann P, Nohel T (2015) Time-varying systematic and idiosyncratic risk exposures of US bank holding companies. J Int Finan Markets Inst Money 35:45–68

    Article  Google Scholar 

  • Biell L, Muller A (2013) Sudden crash or long torture: the timing of market reactions to operational loss events. J Bank Finance 37(7):2628–2638

    Article  Google Scholar 

  • Blankespoor E (2018) Firm communication and investor response: a framework and discussion integrating social media. Acc Organ Soc 68–69:80–87

    Article  Google Scholar 

  • Blankespoor E, Miller GS, White HD (2014) The role of dissemination in market liquidity: evidence from firms’ use of twitter™. Account Rev 89(1):79–112

    Article  Google Scholar 

  • Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Computat Sci 2(1):1–8

    Article  Google Scholar 

  • Cade NL (2018) Corporate social media: how two-way disclosure channels influence investors. Acc Organ Soc 68:63–79

    Article  Google Scholar 

  • Chakravarthy J, deHaan E, Rajgopal S (2014) Reputation repair after a serious restatement. Account Rev 89(4):1329–1363

    Article  Google Scholar 

  • Chen H, De P, Hu Y, Hwang B-H (2014) Wisdom of crowds: the value of stock opinions transmitted through social media. Rev Financ Stud 27(5):1367–1403

    Article  Google Scholar 

  • Cummins JD, Lewis CM, Wei R (2006) The market value impact of operational loss events for US banks and insurers. J Bank Finance 30(10):2605–2634

    Article  Google Scholar 

  • Da Z, Engelberg J, Gao P (2011) In search of attention. J Financ 66(5):1461–1499

    Article  Google Scholar 

  • Dhaliwal DS, Li OZ, Tsang A, Yang YG (2011) Voluntary nonfinancial disclosure and the cost of equity capital: the initiation of corporate social responsibility reporting. Account Rev 86(1):59–100

    Article  Google Scholar 

  • Drake MS, Moon JR, Twedt BJ, Warren JD (2023) Social media analysts and sell-side analyst research. Rev Acc Stud 28(2):385–420

    Article  Google Scholar 

  • Fabrizi M, Huan X, Parbonetti A (2021) When LIBOR becomes LIEBOR: Reputational penalties and bank contagion. Financ Rev 56(1):157–178

    Article  Google Scholar 

  • Fama EF (1991) Efficient capital markets: II. J Financ 46(5):1575–1617

    Article  Google Scholar 

  • Fang L, Peress J (2009) Media coverage and the cross-section of stock returns. J Financ 64(5):2023–2052

    Article  Google Scholar 

  • Ferilli S, Esposito F, Grieco D (2014) Automatic learning of linguistic resources for stopword removal and stemming from text. Procedia Comput Sci 38:116–123

    Article  Google Scholar 

  • Fiordelisi F, Soana M-G, Schwizer P (2014) Reputational losses and operational risk in banking. Eur J Financ 20(2):105–124

    Article  Google Scholar 

  • Gandhi P, Golez B, Jackwerth JC, Plazzi A (2019) Financial market misconduct and public enforcement: the case of LIBOR manipulation. Manag Sci 65(11):5268–5289

    Article  Google Scholar 

  • Gao H, Wang J, Wang Y, Wu C, Dong X (2020) Media coverage and the cost of debt. J Financ Quant Anal 55(2):429–471

    Article  Google Scholar 

  • Gauba H, Kumar P, Roy PP, Singh P, Dogra DP, Raman B (2017) Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Netw 92:77–88

    Article  Google Scholar 

  • Gillet R, Hübner G, Plunus S (2010) Operational risk and reputation in the financial industry. J Bank Finance 34(1):224–235

    Article  Google Scholar 

  • Groves M, Mundt K (2015) Friend or foe? Google translate in language for academic purposes. Engl Specif Purp 37:112–121

    Article  Google Scholar 

  • Hail L, Tahoun A, Wang C (2018) Corporate scandals and regulation. J Account Res 56(2):617–671

    Article  Google Scholar 

  • Hassan OA, Marston C (2019) Corporate financial disclosure measurement in the empirical accounting literature: a review article. Int J Account 54(02):1950006

    Article  Google Scholar 

  • Hawkins JB, Brownstein JS, Tuli G, Runels T, Broecker K, Nsoesie EO, McIver DJ, Rozenblum R, Wright A, Bourgeois FT (2016) Measuring patient-perceived quality of care in US hospitals using twitter. BMJ Qual Saf 25(6):404–413

    Article  Google Scholar 

  • Highhouse S, Brooks ME, Gregarus G (2009) An organizational impression management perspective on the formation of corporate reputations. J Manag 35(6):1481–1493

    Google Scholar 

  • Jia W, Redigolo G, Shu S, Zhao J (2020) Can social media distort price discovery? Evidence from merger rumors. J Account Econ 70(1):101334

    Article  Google Scholar 

  • Jung MJ, Naughton JP, Tahoun A, Wang C (2018) Do firms strategically disseminate? Evidence from corporate use of social media. Account Rev 93(4):225–252

    Article  Google Scholar 

  • Karpoff JM, Lott JR (1993) The reputational penalty firms bear from committing criminal fraud. J Law Econ 36(2):757–802

    Article  Google Scholar 

  • Karpoff JM, Lee DS, Martin GS (2008) The cost to firms of cooking the books. J Financ Quant Anal 43(3):581–611

    Article  Google Scholar 

  • Kothari SP, Warner JB (2007) Econometrics of event studies. In: Eckbo BE (ed) Handbook of empirical corporate finance. Elsevier, pp 3–36

    Chapter  Google Scholar 

  • Lee LF, Hutton AP, Shu S (2015) The role of social media in the capital market: Evidence from consumer product recalls. J Account Res 53(2):367–404

    Article  Google Scholar 

  • Li H, Graesser AC, Cai Z (2014) Comparison of Google translation with human translation. The 27th International Flairs Conference,

  • Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) How social influence can undermine the wisdom of crowd effect. Proc Natl Acad Sci 108(22):9020–9025

    Article  Google Scholar 

  • Loria S, Keen P, Honnibal M, Yankovsky R, Karesh D and Dempsey E (1994). Textblob: Simplified text processing. https://textblob.readthedocs.io/en/dev/index.html

  • McConnell P (2013) Systemic operational risk: the LIBOR manipulation scandal. J Operat Risk 8(3):59–99

    Article  Google Scholar 

  • Miller GS, Skinner DJ (2015) The evolving disclosure landscape: How changes in technology, the media, and capital markets are affecting disclosure. J Account Res 53(2):221–239

    Article  Google Scholar 

  • Miner G (2012) Practical text mining and statistical analysis for non-structured text data applications. Academic Press

    Google Scholar 

  • Mohammad SM, Salameh M, Kiritchenko S (2016) How translation alters sentiment. J Artif Intell Res 55:95–130

    Article  Google Scholar 

  • Monticini A, Thornton DL (2013) The effect of underreporting on LIBOR rates. J Macroecon 37:345–348

    Article  Google Scholar 

  • Morsing M (2006) Corporate social responsibility as strategic auto-communication: on the role of external stakeholders for member identification. Bus Ethics Europ Rev 15(2):171–182

    Article  Google Scholar 

  • Peress J (2014) The media and the diffusion of information in financial markets: Evidence from newspaper strikes. J Financ 69(5):2007–2043

    Article  Google Scholar 

  • Perikos I, Hatzilygeroudis I (2016) Recognizing emotions in text using ensemble of classifiers. Eng Appl Artif Intell 51:191–201

    Article  Google Scholar 

  • Perry, J., & De Fontnouvelle, P. (2005). Measuring reputational risk: The market reaction to operational loss announcements. Federal Reserve Bank of Boston Working Paper.

  • Rennekamp KM, Witz PD (2021) Linguistic formality and audience engagement: Investors’ reactions to characteristics of social media disclosures. Contemp Account Res 38(3):1748–1781

    Article  Google Scholar 

  • Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55

    Article  Google Scholar 

  • Russell FM, Hendricks MA, Choi H, Stephens EC (2015) Who sets the news agenda on Twitter? Digit J 3(6):925–943

    Google Scholar 

  • Scott SV, Walsham G (2005) Reconceptualizing and managing reputation risk in the knowledge economy: toward reputable action. Organ Sci 16(3):308–322

    Article  Google Scholar 

  • Sprenger TO, Sandner PG, Tumasjan A, Welpe IM (2014) News or noise? using Twitter to identify and understand company-specific news flow. J Bus Financ Acc 41(7–8):791–830

    Article  Google Scholar 

  • Sturm P (2013) Operational and reputational risk in the European banking industry: the market reaction to operational risk events. J Econ Behav Organ 85:191–206

    Article  Google Scholar 

  • Tang VW (2018) Wisdom of crowds: cross-sectional variation in the informativeness of third-party-generated product information on Twitter. J Account Res 56(3):989–1034

    Article  Google Scholar 

  • Toubia O, Stephen AT (2013) Intrinsic vs image-related utility in social media: why do people contribute content to Twitter? Market Sci 32(3):368–392

    Article  Google Scholar 

  • Usha A, Thampi SM (2017) Authorship analysis of social media contents using tone and personality features. Security, privacy, and anonymity in computation, communication, and storage, Cham.

  • Vaughan L, Finch G (2017) The fix: how bankers lied, cheated and colluded to rig the world’s most important number. John Wiley & Sons

    Book  Google Scholar 

  • Wang Y, Zhang M, Li S, McLeay F, Gupta S (2021) Corporate responses to the coronavirus crisis and their impact on electronic-word-of-mouth and trust recovery: Evidence from social media. Br J Manag 32(4):1184–1202

    Article  Google Scholar 

  • Zhou M, Lei L, Wang J, Fan W, Wang AG (2015) Social media adoption and corporate disclosure. J Inf Syst 29(2):23–50

    Google Scholar 

Download references

Acknowledgements

We thank the editor, Cheng-Few Lee, two anonymous reviewers, Manuel Adelino, Amir Amel-Zadeh, Niamh Brennan, Michele Fabrizi, Frank Fagan, Miguel Ferreira, Sok-Hyon Kang, April Klein, Aziza Laguecir, Francesco Mazzi, Facundo Mercado, Siva Nathan, Reining Petacchi, Amedeo Pugliese, Margarida Soares, Marco Trombetta, and Zhifang Zhang for helpful comments and suggestions. This paper also benefited from participant comments at the Accounting Summer Camp 2019 (Bolzano), the 2019 American Accounting Association Annual Meeting, the 2019 European Accounting Association Annual Congress, the 2019-2020 ESADE Alumni Events (Milan, Shanghai), and seminars at EDHEC Business School, ESADE Business School, IE Business School, NHH Norwegian School of Economics, Nova Business School, and the University of Padova. We thank Giovanni Berton for the excellent research assistance. Giulia Redigolo acknowledges the financial support received from ESADE Business School.

Author information

Authors and Affiliations

  1. EDHEC Business School, 393 Promenade Des Anglais, Nice, France

    Xing Huan

  2. Department of Economics & Management, University of Padova, Via del Santo 33, 35123, Padua, Italy

    Antonio Parbonetti

  3. ESADE Business School, Ramon Llull University, Av.da de Pedralbes 60-62, 08034, Barcelona, Spain

    Giulia Redigolo

  4. Warwick Business School, University of Warwick, Scarman Road, Coventry, CV4 7AL, UK

    Zhewei Zhang

Authors
  1. Xing Huan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Parbonetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giulia Redigolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhewei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giulia Redigolo.

Ethics declarations

Conflict of interest

Xing Huan, Antonio Parbonetti, Giulia Redigolo, Zhewei Zhang declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 13, 14 and 15

Table 13 List of events
Full size table
Table 14 Variable definition
Full size table
Table 15 Example Tweets
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huan, X., Parbonetti, A., Redigolo, G. et al. Social media disclosure and reputational damage. Rev Quant Finan Acc 62, 1355–1396 (2024). https://doi.org/10.1007/s11156-023-01239-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11156-023-01239-z

Keywords

JEL Classification

Search

Navigation