Textual analysis and sentiment analysis in accounting

  1. Gandía, Juan L.
  2. Huguet, David
Journal:
Revista de contabilidad = Spanish accounting review: [RC-SAR]

ISSN: 1138-4891

Year of publication: 2021

Volume: 24

Issue: 2

Pages: 168-183

Type: Article

DOI: 10.6018/RCSAR.386541 DIALNET GOOGLE SCHOLAR lock_openDIGITUM editor

More publications in: Revista de contabilidad = Spanish accounting review: [RC-SAR]

Sustainable development goals

Abstract

In spite of the relatively scarce use of textual analysis and sentiment analysis techniques in finance and accounting, they have great potential in accounting, both because of the volume of documents used for the communication of information and due to the growth in the use of digital tools and social media. In that regard, these techniques of analysis may help researchers to analyse hidden clues or look for additional information to that one observed through financial information, increasing the quantity and quality of the information traditionally used, and providing a new perspective of analysis. The aim of this study is to review the use of textual analysis and sentiment analysis in accounting. After presenting the concepts of textual analysis and sentiment analysis and expose their interest in accounting, we perform a review of the previous literature on the use of these techniques in finance and accounting and describe the main techniques of sentiment analysis, as well as the procedure to be followed for the use of this methodology. Finally, we suggest three lines of future research that may benefit from the use of textual and sentiment analysis.

Bibliographic References

  • Abrahamson, E., & Amir, E. (1996). The information content of the president's letter to shareholders. Journal of Business, Finance and Accounting, 23(8), 1157-1181. https://doi.org/10.1111/j.1468-5957.1996.tb01163.x.
  • Alcaide Muñoz, L., Rodríguez Bolívar, M.P., & Sánchez, R.G. (2014). Estudio cienciométrico de la investigación en transparencia informativa, participación ciudadana y prestación de servicios públicos mediante la implementación del e-gobierno. Revista de Contabilidad -- Spanish Accounting Review, 17(2), 130-142. https://doi.org/10.1016/j.rcsar.2014.05.001
  • Allee, K.D., & DeAngelis, M.D. (2015). The structure of voluntary disclosure narratives: Evidence from tone dispersion. Journal of Accounting Research, 53(2), 241-274. https://doi.org/10.1111/1475-679X.12072
  • Amani, F. A., & Fadlalla, A. M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32-58. https://doi.org/10.1016/j.accinf.2016.12.004
  • Amernic, J., Craig, R., & Tourish, D. (2010). Measuring and assessing tone at the top using annual report CEO letters. The Institute of Chartered Accountants of Scotland. https://researchportal.port.ac.uk/portal/en/publications/measuring%2dand%2dassessing%2dtone%2dat%2dthe%2dtop%2dusing%2dannual%2dreport%2dceo%2dletters(5f009fa3%2d76fe%2d441f%2d91c3%2dc8535273d71f).html
  • Antweiler, W., & Frank, M.Z. (2004). Is all that talk just noise? The information content of Internet stock message boards. The Journal of Finance, 59(3), 1259-1294. https://doi.org/10.1111/j.1540-6261.2004.00662.x
  • Asay, H.S., Elliott, W.B., & Rennekamp, K. (2017). Disclosure readability and the sensitivity of investors' valuation judgments to outside information. The Accounting Review, 92(4), 1-25. https://doi.org/10.2308/accr-51570
  • Asay, H.S., Libby, R., & Rennekamp, K. (2018). Firm performance, reporting goals, and language choices in narrative disclosures. Journal of Accounting and Economics, 65(2-3), 380-398. https://doi.org/10.1016/j.jacceco.2018.02.002
  • Baker, M., & Wugler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-151. https://doi.org/10.3386/w13189
  • Ball, C., Hoberg, G., & Maksimovic, V. (2015). Disclosure, business change and earnings quality. Working Paper. Available at SSRN: https://ssrn.com/abstract=2260371.
  • Barkemeyer, R., Comyns, B., Figge, F., & Napolitano, G. (2014). CEO statements in sustainability reports: Substantive information or background noise? Accounting Forum, 38(4), 241-257. https://doi.org/10.1016/j.accfor.2014.07.002
  • Barron, E.E., Kile, C.O., O'keefe, T.B. (1999). MD&A quality as measured by the SEC and analysts' earnings forecasts. Contemporary Accounting Research, 16(1), 75-109. https://doi.org/10.1111/j.1911-3846.1999.tb00575.x
  • Bharath, S.T., Sunder, J., & Sunder, S.V. (2008). Accounting quality and debt contracting. The Accounting Review, 83(1), 1-28. https://doi.org/10.2139/ssrn.591342
  • Blei, D.M., Ng, A. Y., & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(Jan), 993-1022.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. https://doi.org/10.1016/j.jocs.2010.12.007
  • Bonsall, S.B., Leone, A.J., Miller, B.P., & Rennekamp, K. (2017). A plain English measure of financial reporting readability. Journal of Accounting and Economics, 63(2-3), 329-357. https://doi.org/10.1016/j.jacceco.2017.03.002
  • Bonsón, E., & Flores, F. (2011). Social media and corporate dialogue: The response of the global financial institutions. Online Information Review, 35(1), 34-49. https://doi.org/10.1108/14684521111113579
  • Bonsón, E., Royo, S., & Ratkai, M. (2015). Citizen's engagement on local governments' Facebook sites. An empirical analysis: The impact of different media and content types in Western Europe. Government Information Quarterly, 32(1), 52-62. https://doi.org/10.1016/j.giq.2014.11.001
  • Bonsón, E., Torres, L., Royo, S., & Flores, F. (2012). Local e-government 2.0: Social media and corporate transparency in municipalities. Government Information Quarterly, 29(2), 123-132. https://doi.org/10.1016/j.giq.2011.10.001
  • Boudoukh, J., Feldman, R., Kogan, S., & Richardson, M. (2019). Information, trading and volatility: Evidence from firm-specific news. The Review of Financial Studies, 32(3), 992-1033. https://doi.org/10.1093/rfs/hhy083
  • Brown, S., Hillegeist, S.A., & Lo, K. (2004). Conference calls and information asymmetry. Journal of Accounting and Economics, 37(3), 343-366. https://doi.org/10.1016/j.jacceco.2004.02.001
  • Brown, S.V., & Tucker, J.W. (2011). Large-sample evidence on firms' year-over-year MD&A modifications. Journal of Accounting Research, 49(2), 309-346. https://doi.org/10.1111/j.1475-679X.2010.00396.x
  • Bryan, S.H. (1997). Incremental information content of required disclosures contained in Management Discussion and Analysis. The Accounting Review, 72(2), 285-301.
  • Buehlmaier, M.M.M., & Whited, T.M. (2018). Are financial constraints priced? Evidence from textual analysis. The Review of Financial Studies, 31(7), 2693-2728. https://doi.org/10.1093/rfs/hhy007
  • Bushee, B.J., Matsumoto, D.A., & Miller, G.S. (2003). Open versus closed conference calls: The determinants and effects of broadening access to disclosure. Journal of Accounting and Economics, 34, 149-180. https://doi.org/10.1016/S0165-4101(02)00073-3
  • Bushee, B.J., Matsumoto, D.A., & Miller, G.S. (2004). Managerial and investor responses to disclosure regulation: The case of Reg FD and conference calls. Journal of Accounting Reseach, 79(3), 617-643. https://doi.org/10.2139/ssrn.310233
  • Cecchini, M., Aytug, H., Koehler, G.J., & Pathak, P. (2010). Detecting management fraud in public companies. Management Science, 56(7), 1146-1160. https://doi.org/10.1287/mnsc.1100.1174
  • Chakraborty, V., Chiu, V., & Vasarhelyi, M. (2014). Automatic classification of accounting literature. International Journal of Accounting Information Systems, 15(2), 122-148. https://doi.org/10.1016/j.accinf.2014.01.001
  • Chen, H., De, P., Hu, J., & Wang, B.H. (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. The Review of Financial Studies, 27(5), 1367-1403. https://doi.org/10.1093/rfs/hhu001
  • Chen, Y.J., Wu, C.H., Chen, Y.M., Li, H.Y., & Chen, H.K. (2017). Enhancement of fraud detection for narratives in annual reports. International Journal of Accounting Information Systems, 26(1), 32-45. https://doi.org/10.1016/j.accinf.2017.06.004
  • Cho, C.H., Roberts, R.W., & Patten, D.M. (2010). The language of US corporate environmental disclosure. Accounting, Organizations and Society, 35(4), 431-443. https://doi.org/10.1016/j.aos.2009.10.002
  • Clatworthy, M., & Jones, M.J. (2003). Financial reporting of good news and bad news: Evidence from accounting narratives. Accounting and Business Research, 33(3), 171-185. https://doi.org/10.1080/00014788.2003.9729645
  • Cohen, K.B., & Hunter, L. (2008). Getting Started in Text Mining. PLoS Computational Biology. 4(1), e20. https://doi.org/10.1371/journal.pcbi.0040020
  • Das, S., & Chen, M. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science, 53(9), 1375-1388. https://doi.org/10.1287/mnsc.1070.0704
  • Das, S. (2014). Text and context: Language analytics in Finance. Foundations and Trends in Finance, 8(3), 145-261. https://doi.org/10.1561/0500000045
  • Davis, A.K., Piger, J.M., & Sedor, L.M. (2012). Beyond the numbers: Measuring the information content of earning press release language. Contemporary Accounting Research, 29(3), 845-868. https://doi.org/10.1111/j.1911-3846.2011.01130.x
  • Davis, A.K., & Tama-Sweet, I. (2012). Managers' use of language across alternative disclosure outlets: Earnings press releases versus MD&A. Contemporary Accounting Research, 29(3), 804-837. https://doi.org/10.1111/j.1911-3846.2011.01125.x
  • Debreceny, R.S., Want, T., & Zhou, M. (2019). Research in social media: Data sources and methodologies. Journal of Information Systems, 33(1), 1-28. https://doi.org/10.2308/isys-51984
  • Doran, J.S., Peterson, D.R., & Price, S.M. (2012). Earnings conference call content and stock price: The case of REITs. Journal of Real Estate Finance and Economics, 45(2), 402-434. https://doi.org/10.1007/s11146-010-9266-z
  • Duan, W., Gu, B., & Whinston, A.B. (2008a). Do online reviews matter? An empirical investigation of panel data. Decision Support Systems, 45(4), 1007-106. https://doi.org/10.1016/j.dss.2008.04.001
  • Duan, W., Gu, B., & Whinston, A.B. (2008b). The dynamics of online word-of-mouth and product sales -- An empirical investigation of the movie industry. Journal of Retailing, 84(2), 233-242. https://doi.org/10.1016/j.jretai.2008.04.005
  • Dyer, T., Lang, M., & Stice-Lawrence, L. (2017). The evolution of 10-K textual disclosure. Evidence from Latent Dirichlet Allocation. Journal of Accounting and Economics, 64(2-3), 221-245. https://doi.org/10.1016/j.jacceco.2017.07.002
  • Elrod, G.B. (2009). Is there predictive value in the words managers use? A key word analysis of the annual reports' Management Discussion and Analysis (Doctoral dissertation). University of Texas at Arlington.
  • Ferguson, N.J., Philip, D., Lam, H.Y.T., & Guo, J.M. (2015). Media content and stock returns: The predictive power of press. Multinational Finance Journal, 19(1), 1-31. https://doi.org/10.17578/19-1-1
  • Fisher, I.E., Garnsey, M.R., & Hughes, M.E. (2016). Natural Language Processing in accounting, auditing, and finance: A synthesis of the literature with a roadmap for future research. Intelligent Systems in Accounting, Finance and Management, 23(3), 157-214. https://doi.org/10.1002/isaf.1386
  • Francis, J.R., Khurana, I.K., & Pereira, R. (2005). Disclosure incentives and effects on cost of capital around the world. The Accounting Review, 80(4), 1125-1162. https://doi.org/10.2308/accr.2005.80.4.1125
  • Francis, J., Dhananjay, N., & Olsson, P. (2008). Voluntary disclosure, earnings quality, and cost of capital. Journal of Accounting Research, 46(1), 53-99. https://doi.org/10.1111/j.1475-679X.2008.00267.x
  • Frankel, R., Jennings, J., & Lee, J. (2016). Using unstructured and qualitative disclosures to explain accruals. Journal of Accounting and Economics, 62(2-3), 209-227. https://doi.org/10.1016/j.jacceco.2016.07.003
  • Gálvez-Rodríguez, M.M., Caba-Pérez, C., & López-Godoy, M. (2016). Drivers of Twitter as a strategic communication tool for non-profit organizations. Internet Research, 26(5), 1052-1071. https://doi.org/10.1108/IntR-07-2014-0188
  • Gandía, J.L. (2011). Internet disclosure by non-profit organizations: Empirical evidence of nongovernmental organizations for development in Spain. Nonprofit and Voluntary Sector Quarterly, 40(1), 57-78. https://doi.org/10.1177/0899764009343782
  • Gandía, J.L., & Huguet, D. (2018). Differences in audit pricing between voluntary and mandatory audits. Academia Revista Latinoamericana de Administración, 31(2), 336-359. https://doi.org/10.1108/ARLA-01-2016-0007
  • Gandía, J.L., Marrahí, L., & Huguet, D. (2016). Digital transparency and Web 2.0 in Spanish city councils. Government Information Quarterly, 33(1), 28-39. https://doi.org/10.1016/j.giq.2015.12.004
  • García, D. (2013). Sentiment during recessions. The Journal of Finance, 68 (3), 1267-1300. https://doi.org/10.1111/jofi.12027
  • García Osma, B., Gill de Albornoz, B., & Gisbert, A. (2005). La investigación sobre earnings management (Research on earnings management). Spanish Journal of Finance and Accounting, 34(127), 1001-1033. https://doi.org/10.1080/02102412.2005.10779570
  • Goel, S., Gangolly, J., Faerman, S.R., & Uzuner, O. (2010). Can linguistic predictors detect fraudulent financial filings? Journal of Emerging Technologies in Accounting, 7(1), 25-46. https://doi.org/10.2308/jeta.2010.7.1.25
  • Goel, S., & Gangolly, J. (2012). Beyond the numbers: Mining the annual report for hidden cues indicative of financial statement fraud. Intelligent Systems in Accounting, Finance and Management, 19(2), 75-89. https://doi.org/10.1002/isaf.1326
  • 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
  • Guo, C., & Saxton, G. (2014). Tweeting social change: How social media are changing Nonprofit advocacy. Nonprofit and Voluntary Sector Quarterly, 43(1), 57-79. https://doi.org/10.1177/0899764012471585
  • Hájek, P., & Olej, V. (2013). Evaluating sentiment in annual reports for financial distress prediction using Neural Networks and Support Vector Machines. Engineering Applications of Neural Networks, pp 1-10, in International Conference on Engineering Applications of Neural Networks. https://doi.org/10.1007/978-3-642-41016-1%5f1
  • Hájek, P. (2018). Combining bag-of-words and sentiment features of annual reports to predict abnormal stock returns. Neural Computing and Applications, 29(7), 343-358. https://doi.org/10.1007/s00521-017-3194-2
  • Hales, J., Kuang, X.I., & Venkataraman, S. (2011). Who believes the hype? An experimental examination of how language affects investor judgments. Journal of Accounting Research, 49(1), 223-255. https://doi.org/10.1111/j.1475-679X.2010.00394.x
  • Hanley, K.W., & Hoberg, G. (2010). The information content of IPO prospectuses. Review of Financial Studies, 23, 2821-2864. https://doi.org/10.1093/rfs/hhq024
  • Henry, E. (2006). Market reaction to verbal components of earnings press releases: Event study using a predictive algorithm. Journal of Emerging Technologies in Accounting, 3(1), 1-19. https://doi.org/10.2308/jeta.2006.3.1.1
  • Henry, E. (2008). Are investors influenced by how earnings press releases are written? The Journal of Business Communication, 45(4), 363-407. https://doi.org/10.1177/0021943608319388
  • Hoberg, G., & Phillips, G. (2016). Text-based network industries and endogenous product differentiation. Journal of Political Economy, 124(5), 1423-1465. https://doi.org/10.3386/w15991
  • Huang, A.H., Zang, A.Y., & Zheng, R. (2014). Evidence on the information content of text in analyst reports. The Accounting Review, 89(6), 2151-2180. https://doi.org/10.2308/accr-50833
  • Huang, A.H., Lehavy, R., Zang, A.Y., & Zheng, R. (2018). Analyst information discovery and interpretation roles: A topic modeling approach. Management Science, 64(6), 2833-2855. https://doi.org/10.1287/mnsc.2017.2751
  • Huguet, D. and Gandía, J.L. (2014). Cost of debt capital and audit in Spanish SMEs. Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad, 43(3), 266-289. https://doi.org/10.1080/02102412.2014.942154
  • Huguet, D. and Gandía, J.L. (2016). Audit and earnings management in Spanish SMEs. Business Research Quarterly, 19(3), 171-187. https://doi.org/10.1016/j.brq.2015.12.001
  • Humpherys, S.L., Moffitt, K.C., Burns, M.B., Burgoon, J.K., & Felix, W.F. (2011). Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50(3), 585-594. https://doi.org/10.1016/j.dss.2010.08.009
  • Hutchison, P.D., Daigle, R.J., & George, B. (2018). Application of latent semantic analysis in AIS academic research. International Journal of Accounting Information Systems, 31(1), 83-96. https://doi.org/10.1016/j.accinf.2018.09.003
  • Jegadeesh, N., & Wu, D. (2013). Word power: A new approach for content analysis. Journal of Financial Economics, 110(3), 712-729. https://doi.org/10.1016/j.jfineco.2013.08.018
  • Jiang, Y., Raghupathi, V., & Raghupathi, W. (2009). Content and design of corporate governance web sites. Information Systems Management, 26(1), 13-27. https://doi.org/10.1080/10580530802384704
  • Jorgensen, P. (2005). Incorporating context in text analysis by interactive activation with competition artificial neural networks. Information Processing and Management: An International Journal, 41(5), 1081-1099. https://doi.org/10.1016/j.ipm.2004.10.003
  • Karim, K.E., Lim, K.J., Pinsker, R.E., & Zhu, H. (2019). Using linguistics to mine unstructured data from FASB exposure drafts. Journal of Information Systems, 33(1), 67-83. https://doi.org/10.2308/isys-51928
  • Kearney, C., & Liu, S. (2014). Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis, 33, 171-185. https://doi.org/10.1016/j.irfa.2014.02.006
  • Kim, S.H., & Kim, D. (2014). Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior and Organization, 107(B), 708-729. https://doi.org/10.1016/j.jebo.2014.04.015
  • Koo, D.S., Wu, J.J., & Yeung, P.E. (2017). Earnings attribution and information transfers. Contemporary Accounting Research, 34(3), 1547-1579. https://doi.org/10.1111/1911-3846.12308
  • Koppel, M., & Schler, J. (2006). The importance of neutral examples for learning sentiment. Computational Intelligence, 22(2), 100-109. https://doi.org/10.1111/j.1467-8640.2006.00276.x
  • Kothari, S.P., Li, X., & Short., J.E., (2009). The effect of disclosures by management, analyst, and business press on cost of capital, return volatility, and analyst forecast: A study using content analysis. The Accounting Review, 84(5), 1639-1670. https://doi.org/10.2308/accr.2009.84.5.1639
  • Lang, M., & Stice-Lawrence, L. (2015). Textual analysis and international financial reporting: Large sample evidence. Journal of Accounting and Economics, 60(2-3), 110-135. https://doi.org/10.1016/j.jacceco.2015.09.002
  • Leuz, C., & Wysocki, P.D. (2016). The economics of disclosure and financial reporting regulation: Evidence and suggestions for future research. Journal of Accounting Research, 54(2), 525-622. https://doi.org/10.1111/1475-679X.12115
  • Levin, I.P., Schneider, S.L., & Gaeth, G.J. (1998). All frames are not created equal: A typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes, 76(2), 149-188. https://doi.org/10.1006/obhd.1998.2804
  • Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics, 45(2-3), 221-247. https://doi.org/10.1016/j.jacceco.2008.02.003
  • Li, F. (2010). Textual analysis of corporate disclosures: A survey of the literature. Journal of Accounting Literature, 29, 143-165.
  • Li, F. (2010). The information content of forward-looking statements in corporate filings -- A naïve Bayesian Machine Learning approach. Journal of Accounting Research, 48(5), 1049-1102. https://doi.org/10.1111/j.1475-679X.2010.00382.x
  • Li, Q., Wang, T., Li, P., Liu, L., Gong., Q., & Chen, Y. (2014a). The effect of news and public mood on stock movements. Information Sciences, 278, 826-840. https://doi.org/10.1016/j.ins.2014.03.096
  • Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2014b). New impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69, 14-23. https://doi.org/10.1016/j.knosys.2014.04.022
  • Lim, E.K.Y., Chalmers, K., & Hanlon, D. (2018). The influence of business strategy on annual report readability. Journal of Accounting and Public Policy, 37(1), 65-81. https://doi.org/10.1016/j.jaccpubpol.2018.01.003
  • Lo, K. (2008). Earnings management and earnings quality. Journal of Accounting and Economics, 45(2-3), 350-357. https://doi.org/10.1016/j.jaccpubpol.2018.01.003
  • Lo, K., Ramos, F., & Rogo, R. (2017). Earnings management and annual report readability. Journal of Accounting and Economics, 63(1), 1-25. https://doi.org/10.1016/j.jacceco.2016.09.002
  • Loughran, T., McDonald, B., & Yun, H. (2009). A wolf in sheep's clothing: The use of ethics-related terms in 10-K reports. Journal of Business Ethics, 89(Supplement), 39-49. https://doi.org/10.1007/s10551-008-9910-1
  • 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
  • Loughran, T., & McDonald, B. (2014). Measuring readability in financial disclosures. The Journal of Finance, 69(4), 1643-1671. https://doi.org/10.1111/jofi.12162
  • Loughran, T., & McDonald, B. (2016). Textual analysis in accounting and finance: A survey. Journal of Accounting Research, 54(4), 1187-1230. https://doi.org/10.1111/1475-679X.12123
  • Lovejoy, K., & Saxton, G.D. (2012). Information, community and action: How non-profit organizations use social media. Journal of Computer-Mediated Communication, 17(3), 337-353. https://doi.org/10.1111/j.1083-6101.2012.01576.x
  • Luo, X., Zhang, J., & Duan, W. (2013). Social media and firm equity value. Information Systems Research, 24(1), 146-163. https://doi.org/10.2139/ssrn.2162167
  • Malo, P., Sinha, A., Takala, P., Korhonen, P., & Wallenius, J. (2014). Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology, 65(4), 782-796. https://doi.org/10.1002/asi.23062
  • Mayew, W.J., Sethuraman, M., & Venkatachalam, M. (2015). MD&A disclosure and the firm's ability to continue as a going concern. The Accounting Review, 90(4), 1621-1651. https://doi.org/10.2139/ssrn.2272463
  • McCallum, A. (1996). Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. Working paper: School of Computer Science, Carnegie-Mellon University.
  • Melloni, G., Caglio, A., & Perego, P. (2017). Saying more with less? Disclosure conciseness, completeness and balance in Integrated Reports. Journal of Accounting and Public Policy, 36(3), 220-238. https://doi.org/10.1016/j.jaccpubpol.2017.03.001
  • Mo, S.Y.K., Liu, A., & Yand, S.Y. (2016). New sentiment to market impact and its feedback effect. Environment Systems and Decisions, 36(2), 158-166. https://doi.org/10.1007/s10669-016-9590-9
  • Nguyen, T.H., Shirai, K., & Velcin, J. (2015). Sentiment analysis on social media for stock movement prediction. Expert Systems with Applications, 42(24), 9603-9611. https://doi.org/10.1016/j.eswa.2015.07.052
  • Noecker, J., Ryan, M., & Juola, P. (2013). Psychological profiling through textual analysis. Literary and Linguistic Computing, 28(3), 382-387. https://doi.org/10.1093/llc/fqs070
  • Piñeiro-Chousa, Vizcaíno-González, M., & Pérez-Pico, A.M. (2017). Influence of social media over the stock market. Psychology and Marketing, 34(1), 101-108. https://doi.org/10.1002/mar.20976
  • Price, S. M., Doran, J. S., Peterson, D. R., & Bliss, B.A. (2012). Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking and Finance, 36(4), 992--1011. https://doi.org/10.1016/j.jbankfin.2011.10.013
  • Saxton, G.D., & Wang, L. (2014). The social network effect: The determinants of giving through social media. Nonprofit and Voluntary Sector Quarterly, 43(5), 850-868. https://doi.org/10.1177/0899764013485159
  • Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2017). Facebook's daily sentiment and international stock markets. Journal of Economic Behavior and Organization, 107(B), 730-743. https://doi.org/10.1016/j.jebo.2014.06.004
  • Souza, T., Kolchyna, O., Treleaven, P.C., & Aste, T. (2016). Twitter sentiment analysis applied to Finance: a case study in the retail industry. Handbook of Sentiment Analysis in Finance. Mitra, G. and Yu, X. (Eds.). (2016). ISBN 1910571571.
  • Sprenger, T.O., Sandner, P.G., Tumasjan, A., & Welpe, I. (2014). News or noise? Using Twitter to identify and understand company-specific news flow. Journal of Business Finance and Accounting, 41(7-8), 791-830. https://doi.org/10.1111/jbfa.12086
  • Sprenger, T.O., Tumasjan, A., Sandner, P.G., & Welpe, I.M., (2014). Tweets and trades: The information content of stock microblogs. European Financial Management, 20(5), 926-957. https://doi.org/10.1111/j.1468-036X.2013.12007.x
  • Schroeder, N., & Gibson, C. (1990). Readability of Management's Discussion and Analysis. Accounting Horizons, 4(4), 78-87.
  • Sudhahar, S., Veltri, G.A., & Cristianini, N. (2015). Automated analysis of the US presidential elections using Big Data and network analysis. Big Data and Society, 2(1), 1-28. https://doi.org/10.1177/2053951715572916
  • Sul, H.K., Dennis, A.R., & Yuan, L. (2017). Trading on Twitter: Using social media sentiment to predict stock returns. Decision Sciences, 48(3), 454-488. https://doi.org/10.1111/deci.12229
  • Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267-307. https://doi.org/10.1162/COLI%5fa%5f00049
  • Taboada, M. (2016). Sentiment analysis: An overview from linguistics. Annual Review of Linguistics, 2, 325-347. https://doi.org/10.1146/annurev-linguistics-011415-040518
  • Tetlock, P.C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168. https://doi.org/10.2139/ssrn.685145
  • Tetlock, P.C., Saar-Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms' fundamentals. The Journal of Finance, 63(3), 1437-1467. https://doi.org/10.1111/j.1540-6261.2008.01362.x
  • Tetlock, P.C. (2011). All the news that's fit to reprint: Do investors react to stale information? The Review of Financial Studies, 24(5), 1481-1512. https://doi.org/10.1093/rfs/hhq141
  • Tsai, M.F., & Wang, C.J. (2017). On the risk predicition and analysis of soft information in finance reports. European Journal of Operational Research, 257(1), 243-250. https://doi.org/10.1016/j.ejor.2016.06.069
  • Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M. (2011). Election forecasts with Twitter: How 140 characters reflect the political landscape. Social Science Computer Review, 29(4), 402-418. https://doi.org/10.1177/0894439310386557
  • Twedt, B., & Rees, L. (2012). Reading between the lines: An empirical examination of qualitative attributes of financial analysts' reports. Journal of Accounting and Public Policy, 31(1), 1-21. https://doi.org/10.1016/j.jaccpubpol.2011.10.010
  • Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28(1), 180-182. https://doi.org/10.1016/j.ijhm.2008.06.011
  • Zhang, X. Fuehres, H., & Gloor, P.A. (2011). Predicting stock market indicators through Twitter "I hope it is not as bad as I fear". Procedia -- Social and Behavioural Sciences, 26, 55-62. https://doi.org/10.1016/j.sbspro.2011.10.562
  • Zhang, J.L., Härdle, W.K., Chen, C.Y., & Bommes, E. (2016). Distillation of news flow into analysis of stock reactions. Journal of Business and Economic Statistics, 34(4), 547-563. https://doi.org/10.1080/07350015.2015.1110525
  • Zheludev, I., Smith, R., & Aste, T. (2014). When can social media lead financial markets? Scientific Reports, 4. https://doi.org/10.1038/srep04213