Information Systems and Stock Return Volatility

Measuring Information Systems (IS) value has been constantly attracting much attention and debate within the IS research community. Since information systems effects are often difficult to quantify, traditional payoff evaluation methods often yield conflicting results. In this paper we suggest that some information systems can be evaluated on the basis of their effect on stock return volatility. Systems which facilitate information sharing and decision-making can improve the quality of company information to stakeholders, thus reducing surprise levels in financial markets. Specifically, these systems can lead to more consistent and predictable company performance. Hence, we hypothesize that information systems can help to reduce a company’s stock return volatility. To test this hypothesis, we have conducted an empirical analysis on a sample of firms that have deployed a Business Intelligence (BI) system. The results indicate a significant reduction in stock return volatility after BI deployment.

[1]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[2]  Kenneth L. Kraemer,et al.  Information technology and economic performance , 2003, ACM Comput. Surv..

[3]  Thomas K. Landauer,et al.  The trouble with computers , 1995 .

[4]  Izak Benbasat,et al.  Evaluating the Impact of DSS, Cognitive Effort, and Incentives on Strategy Selection , 1999, Inf. Syst. Res..

[5]  Kimberly D. Elsbach,et al.  The Effects of Mood on Individuals' Use of Structured Decision Protocols , 1999 .

[6]  E. Brynjolfsson,et al.  Paradox Lost? Firm-Level Evidence on the Returns to Information Systems Spending , 1996 .

[7]  Αικατερίνη Ανδρεοπούλου,et al.  Have individual stocks become more volatile : an empirical exploration of idiosyncratic risk , 2010 .

[8]  Robert Savickas,et al.  Event‐Induced Volatility and Tests for Abnormal Performance , 2003 .

[9]  Rajiv D. Banker,et al.  The new productivity paradox , 2003, Commun. ACM.

[10]  David Pettifer,et al.  CFO: architect of the corporation's future , 1997 .

[11]  Christopher G. Lamoureux,et al.  Endogenous Trading Volume and Momentum in Stock-Return Volatility , 1994 .

[12]  E. Fama,et al.  Risk, Return, and Equilibrium: Empirical Tests , 1973, Journal of Political Economy.

[13]  E. Bodt,et al.  Event studies with a contaminated estimation period , 2007 .

[14]  J. Edward Russo,et al.  A Pyramid of Decision Approaches , 1993 .

[15]  E. Brynjolfsson,et al.  Beyond Computation: Information Technology, Organizational Transformation and Business Performance , 2000 .

[16]  M. Kathryn Brohman,et al.  The business intelligence value chain: data-driven decision support in a data warehouse environment: an exploratory study , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[17]  Efraim Turban,et al.  Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures , 2013 .

[18]  Erik Brynjolfsson,et al.  The productivity paradox of information technology , 1993, CACM.

[19]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[20]  M. Lettau,et al.  Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk , 2000 .

[21]  Erik Dane,et al.  Exploring Intuition and its Role in Managerial Decision Making , 2007 .

[22]  Vernon J. Richardson,et al.  The Value Relevance of Announcements of Transformational Information Technology Investments , 2003, MIS Q..

[23]  Matteo Golfarelli,et al.  Beyond data warehousing: what's next in business intelligence? , 2004, DOLAP '04.

[24]  E. Brynjolfsson,et al.  Information Technology As A Factor Of Production: The Role Of Differences Among Firms , 1995 .

[25]  Nils Rasmussen,et al.  Financial business intelligence : trends, technology, software selection, and implementation/ Nils Rasmussen, Paul S. Goldy and Per O. Solli , 2002 .

[26]  P. Perrewé,et al.  Managerial decision-making under crisis: The role of emotion in an intuitive decision process , 2004 .

[27]  Grant R. Mcqueen,et al.  Whence GARCH? A Preference-Based Explanation for Conditional Volatility , 2003 .