Advertisement

Say It Right: IS Prototype to Enable Evidence-Based Communication Using Big Data

  • Simon Alfano
  • Nicolas Pröllochs
  • Stefan Feuerriegel
  • Dirk Neumann
Chapter
Part of the Annals of Information Systems book series (AOIS)

Abstract

Financial investors face an increasing information abundance when making their valuation decisions of financial assets. As Information Systems research demonstrated, valuation not only builds on quantitative facts, but also on qualitative information such as the language used in financial disclosures and the readability of the texts. As an originator of financial disclosures, e.g. a company, it is thus essential to thoughtfully steer the creation of new textual information. While regulators provide guidelines on what content to publish, corporate communication departments can flexibly steer how they communicate. We have developed an IS prototype that accounts for the importance of textual information and provides corporate communications with a decision-support tool to assure a high readability and a positive sentiment. Our IS prototype builds on a two-step process. First, we extract a dictionary with the most relevant words for investors from a large inventory of regulatory filings with Bayesian learning algorithms. Second, we use this dictionary as input for a Microsoft Word add-in that highlights positively or negatively connoted words and suggests alternative words with a more positive investor perception to corporate communications professionals.

Keywords

Bayesian learning Capital markets communication Decision-support system Evidence-based communication IS prototype Readability Sentiment 

Notes

Acknowledgements

We want to acknowledge the support of the start-up TonalityTech, our research collaboration partner on this IS prototype development project, for the provision of the above screenshot (Fig. 15.2).

References

  1. Antweiler W, Frank MZ (2004) Is all that talk just noise? The information content of internet stock message boards. J Financ 59(3):1259–1294CrossRefGoogle Scholar
  2. Cornelissen J (2014) Corporate communication: a guide to theory & practice. Sage, LondonGoogle Scholar
  3. Hastie TJ, Tibshirani RJ, Friedman JH (2013) The elements of statistical learning: data mining, inference, and prediction. Springer, New YorkGoogle Scholar
  4. Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT Press, CambridgeGoogle Scholar
  5. Pröllochs N, Feuerriegel S, Neumann D (2015) Generating domain-specific dictionaries using bayesian learning. In: 23rd European conference on information systems (ECIS 2015), Münster, GermanyGoogle Scholar
  6. Rennekamp K (2012) Processing fluency and investors’ reactions to disclosure readability. J Account Res 50(5):1319–1354CrossRefGoogle Scholar
  7. Schumaker RP, Chen H (2009) Textual analysis of stock market prediction using breaking financial news. ACM Trans Inf Syst 27(2):1–19CrossRefGoogle Scholar
  8. Tan HT, Ying Wang E, Zhuo BO (2014) When the use of positive language backfires: the joint effect of tone, readability, and investor sophistication on earnings judgments. J Account Res 52(1):273–302CrossRefGoogle Scholar
  9. Tetlock PC (2007) Giving content to investor sentiment: the role of media in the stock market. J Financ 62(3):1139–1168CrossRefGoogle Scholar
  10. Tetlock PC, Saar-Tsechansky M, Macskassy S (2008) More than words: quantifying language to measure firms’ fundamentals. J Financ 63(3):1437–1467CrossRefGoogle Scholar
  11. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J Royal Statist Soc B 67(2):301–320CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Simon Alfano
    • 1
  • Nicolas Pröllochs
    • 1
    • 2
  • Stefan Feuerriegel
    • 1
  • Dirk Neumann
    • 1
  1. 1.Department of Information Systems Research, Faculty for EconomicsAlbert-Ludwigs-University FreiburgFreiburgGermany
  2. 2.TonalityTechFreiburgGermany

Personalised recommendations