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The Impact of Media Attention on the Illiquidity of Stocks: Evidence from the Global FinTech Sector

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  2. Economics, Finance, Business & Management
  3. Credit and Capital Markets – Kredit und Kapital
  4. Vol. 54, Issue 4
  5. pp. 589–639

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Gaar, E., Moritz, V., Schiereck, D. The Impact of Media Attention on the Illiquidity of Stocks: Evidence from the Global FinTech Sector. Credit and Capital Markets – Kredit und Kapital, 54(4), 589-639. https://doi.org/10.3790/ccm.54.4.589
Gaar, Eduard; Moritz, Valentin and Schiereck, Dirk "The Impact of Media Attention on the Illiquidity of Stocks: Evidence from the Global FinTech Sector" Credit and Capital Markets – Kredit und Kapital 54.4, 2021, 589-639. https://doi.org/10.3790/ccm.54.4.589
Gaar, Eduard/Moritz, Valentin/Schiereck, Dirk (2021): The Impact of Media Attention on the Illiquidity of Stocks: Evidence from the Global FinTech Sector, in: Credit and Capital Markets – Kredit und Kapital, vol. 54, iss. 4, 589-639, [online] https://doi.org/10.3790/ccm.54.4.589
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The Impact of Media Attention on the Illiquidity of Stocks: Evidence from the Global FinTech Sector

Gaar, Eduard | Moritz, Valentin | Schiereck, Dirk

Credit and Capital Markets – Kredit und Kapital, Vol. 54 (2021), Iss. 4 : pp. 589–639

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Article Details

Publisher Name
Duncker & Humblot
DOI
https://doi.org/10.3790/ccm.54.4.589
Language
English
Pages
51
Keywords

Author Details

Eduard Gaar, Technical University of Darmstadt, Department of Business Administration, Economics and Law, 64289 Darmstadt, Germany.

Moritz Valentin, Technical University of Darmstadt, Department of Business Administration, Economics and Law, 64289 Darmstadt, Germany.

Dirk Schiereck, Technical University of Darmstadt, Department of Business Administration, Economics and Law, 64289 Darmstadt, Germany.

Cited By

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    Credit and Capital Markets – Kredit und Kapital, Vol. 56 (2023), Iss. 1 P.63

    https://doi.org/10.3790/ccm.56.1.63 [Citations: 0]

References

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  4. Asur, S./Huberman, B. A. (2010): Predicting the Future with Social Media. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology – Workshops. WI-IAT 2010 Workshops: proceedings, 31 August–3 September 2010, Toronto, Ontario, Canada (pp. 492–499). [Los Alamitos, Calif.]: IEEE Computer Society.  Google Scholar
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  34. Pöppe, T./Schiereck, D./Zielinski, F. (2014): Das Google-Suchvolumen als Liquiditätsindikator des Aktienhandels: Evidenz aus dem weltweiten Agrarsektor. Credit and Capital Markets – Kredit und Kapital, 47(4), 611–640.  Google Scholar
  35. QYResearch (2019): FinTech Market Report: Global Market size, Market Forecast, Market Share, Industry outlook, Demand Analysis, Market Report 2019–2025: Valuates Reports. Retrieved February 13, 2020, from https://reports.valuates.com/market-reports/QYRE-Othe-2W194/fintech-market.  Google Scholar
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  39. Sabherwal, S./Sarkar, S. K./Zhang, Y. (2011): Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News. Journal of Business Finance & Accounting, 38(9–10), 1209–1237.  Google Scholar
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  43. Smales, L. A. (2014): News sentiment and the investor fear gauge. Finance Research Letters, 11(2), 122–130.  Google Scholar
  44. Smales, L. A. (2021): Investor attention and global market returns during the COVID-19 crisis. International Review of Financial Analysis, 73, 101616.  Google Scholar
  45. Smith, S./Tran, V./Perera, T. (2016): 2016 Top Markets Report Financial Technology: A Market Assessment Tool for U.S. Exporters, from https://legacy.trade.gov/topmarkets/financial-tech.asp.  Google Scholar
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  49. Tiberius, V./Rasche, C. (2017): FinTechs. Wiesbaden: Springer Fachmedien Wiesbaden.  Google Scholar
  50. Vozlyublennaia, N. (2014): Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17-35.  Google Scholar
  51. Wang, Q./Myers, M. D./Sundaram, D. (2013): Digital Natives und Digital Immigrants. Wirtschaftsinformatik, 55(6), 409–420.  Google Scholar
  52. Xu, S. X./Zhang, X. (2013): Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction. MIS Quarterly, 37(4), 1043–1068.  Google Scholar
  53. Bank, M./Larch, M./Peter, G. (2011): Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25(3), 31.  Google Scholar
  54. Barber, B. M./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.  Google Scholar
  55. Bijl, L./Kringhaug, G./Molnár, P./Sandvik, E. (2016): Google Searches and Stock Returns. International Review of Financial Analysis, 45, 150–156.  Google Scholar
  56. Blackman, P. (2019): Global fintech investment rockets to a record $111.8B in 2018, driven by mega deals: KPMG Pulse of Fintech – KPMG Global. Retrieved February 17, 2020, from https://home.kpmg/xx/en/home/media/press-releases/2019/02/global-fintech-investment-hits-record-in-2018.html.  Google Scholar
  57. Bollen, J./Mao, H./Zeng, X. (2011): Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.  Google Scholar
  58. Bordino, I./Battiston, S./Caldarelli, G./Cristelli, M./Ukkonen, A./Weber, I. (2012): Web Search Queries Can Predict Stock Market Volumes. PLOS ONE, 7(7), e40014, from https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0040014&type=printable.  Google Scholar
  59. Choi, C. Y./Ham, H. B. (2015): Theoretical Study on the Business Model of Fintech Enterprises. The e-Business Studies, (16), 85–100.  Google Scholar
  60. Da, Z. H. I./Engelberg, J./Gao, P. (2011): In Search of Attention. The Journal of Finance, 66(5), 1461–1499.  Google Scholar
  61. Dimpfl, T./Jank, S. (2016): Can internet search queries help to predict stock market volatility?, European Financial Management, 22(2), 171–192.  Google Scholar
  62. Duan, J./Liu, J./Chen, Q. (2020): Research on the relationship between FinTech attention and its sector returns. International Journal of Economics, Finance and Management Sciences, 8(1), 57.  Google Scholar
  63. Forns, M. R. (2020): Pageviews Analysis – Häufig gestellte Fragen. Retrieved January 10, 2020, from https://tools.wmflabs.org/pageviews/faq/.  Google Scholar
  64. Google (2020): FAQ about Google Trends data – Trends Help. Retrieved January 10, 2020, from https://support.google.com/trends/answer/4365533?hl=en.  Google Scholar
  65. Groß-Klußmann, A./Hautsch, N. (2011): When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions. Journal of Empirical Finance, 18(2), 321–340.  Google Scholar
  66. Gulden, J. (2019): Automatisierte Geldanlage. Wiesbaden: Springer Fachmedien Wiesbaden.  Google Scholar
  67. Heath, P. T./Schwartz, D./Sun, T. (2015): The Future of Finance: The Socialization of Finance. USA: Goldman, Sachs &. Co.  Google Scholar
  68. Hendrikse, R./Bassens, D./van Meeteren, M. (2018): The Appleization of finance: Charting incumbent finance’s embrace of FinTech. Finance and Society, 4(2), 159–180, from http://financeandsociety.ed.ac.uk/article/download/2870/3924.  Google Scholar
  69. Hinnosaar, M./Hinnosaar, T./Kummer, M. E./Slivko, O. (2015): Does Wikipedia Matter? The Effect of Wikipedia on Tourist Choices. SSRN Electronic Journal, 1–17.  Google Scholar
  70. Kennedy, P. (2009): A guide to econometrics (6th ed., [Nachdr.]). Malden, Mass.: Blackwell.  Google Scholar
  71. Lee, I./Shin, Y. J. (2018): Fintech: Ecosystem, business models, investment decisions, and challenges. Business Horizons, 61(1), 35–46.  Google Scholar
  72. LexisNexis Legal & Professional (2020): Legal. Retrieved January 09, 2020, from https://www.relx.com/our-business/market-segments/legal.  Google Scholar
  73. Li, J./Yu, J. (2012): Investor attention, psychological anchors, and stock return predictability. Journal of Financial Economics, 104(2), 401–419.  Google Scholar
  74. Lo, A./Wang, J: (2000). Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory. Cambridge, MA.  Google Scholar
  75. Loesch, S. (2018): A Guide to Financial Regulation for Fintech Entrepreneurs. Chichester, UK: John Wiley & Sons, Ltd.  Google Scholar
  76. Lütkepohl, H./Xu, F. (2012): The role of the log transformation in forecasting economic variables. Empirical Economics, 42(3), 619–638.  Google Scholar
  77. Menat, R. (2016): Why We’re so Excited About FinTech. In S. Chishti & J. Barberis (eds.), The FinTech Book (pp. 10–12). Chichester, UK: John Wiley & Sons, Ltd.  Google Scholar
  78. Moore, M./Tambini, D. (2018): Digital Dominance: The Power of Google, Amazon, Facebook, and Apple. Oxford: Oxford University Press Incorporated.  Google Scholar
  79. Newman, O. (2008): Online Business Sourcebook: De Gruyter.  Google Scholar
  80. Poley, C./Kuffer, J. (2020): DBIS. Retrieved January 09, 2020, from http://rzblx10.uni-regensburg.de/dbinfo/detail.php?bib_id=tuda&colors=&ocolors=&lett=fs&titel_id=1670#.  Google Scholar
  81. Pöppe, T./Kiesel, F./Kolaric, S./Schiereck, D. (2019): Information or Noise: How Twitter Facilitates Stock Market Information Aggregation. Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).  Google Scholar
  82. Pöppe, T./Schiereck, D./Zielinski, F. (2014): Das Google-Suchvolumen als Liquiditätsindikator des Aktienhandels: Evidenz aus dem weltweiten Agrarsektor. Credit and Capital Markets – Kredit und Kapital, 47(4), 611–640.  Google Scholar
  83. QYResearch (2019): FinTech Market Report: Global Market size, Market Forecast, Market Share, Industry outlook, Demand Analysis, Market Report 2019–2025: Valuates Reports. Retrieved February 13, 2020, from https://reports.valuates.com/market-reports/QYRE-Othe-2W194/fintech-market.  Google Scholar
  84. Redding, L. S. (1996): Noise Traders and Herding Behavior. IMF Working Papers: Working Paper No. 96/104. Washington, D.C: International Monetary Fund.  Google Scholar
  85. Report of the Board of IOSCO (2017): FR02/2017 IOSCO Research Report on Financial Technologies (Fintech), from https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf.  Google Scholar
  86. Rubin, A./Rubin, E. (2010): Informed Investors and the Internet. Journal of Business Finance & Accounting, 37(7–8), 841–865.  Google Scholar
  87. Sabherwal, S./Sarkar, S. K./Zhang, Y. (2011): Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News. Journal of Business Finance & Accounting, 38(9–10), 1209–1237.  Google Scholar
  88. Simon, H. A. (1955): A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99–118.  Google Scholar
  89. Skan, J./Dickerson, J./Masood, S. (2015): The Future of Fintech and Banking: Digitally disrupted or reimagined?, Accenture, from https://www.accenture.com/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/dualpub_11/accenture-future-fintech-banking.pdf.  Google Scholar
  90. Smales, L. A. (2012): Non-Scheduled News Arrival and High-Frequency Stock Market Dynamics: Evidence from the Australian Securities Exchange. SSRN Electronic Journal.  Google Scholar
  91. Smales, L. A. (2014): News sentiment and the investor fear gauge. Finance Research Letters, 11(2), 122–130.  Google Scholar
  92. Smales, L. A. (2021): Investor attention and global market returns during the COVID-19 crisis. International Review of Financial Analysis, 73, 101616.  Google Scholar
  93. Smith, S./Tran, V./Perera, T. (2016): 2016 Top Markets Report Financial Technology: A Market Assessment Tool for U.S. Exporters, from https://legacy.trade.gov/topmarkets/financial-tech.asp.  Google Scholar
  94. Smolinski, R./Gerdes, M./Siejka, M./Bodek, M. C. (2017): Innovationen und Innovationsmanagement in der Finanzbranche. Wiesbaden: Springer Fachmedien Wiesbaden.  Google Scholar
  95. Statista (2019): Search engine market share worldwide 2019 | Statista. Retrieved December 20, 2019, from https://www.statista.com/statistics/216573/worldwide-market-share-of-search-engines/.  Google Scholar
  96. Tetlock, P. C. (2007): Giving Content to Investor Sentiment: The Role of Media in the Stock Market. Journal of Finance, 62(3), 1139–1168.  Google Scholar
  97. Tiberius, V./Rasche, C. (2017): FinTechs. Wiesbaden: Springer Fachmedien Wiesbaden.  Google Scholar
  98. Vozlyublennaia, N. (2014): Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17-35.  Google Scholar
  99. Wang, Q./Myers, M. D./Sundaram, D. (2013): Digital Natives und Digital Immigrants. Wirtschaftsinformatik, 55(6), 409–420.  Google Scholar
  100. Xu, S. X./Zhang, X. (2013): Impact of Wikipedia on Market Information Environment: Evidence on Management Disclosure and Investor Reaction. MIS Quarterly, 37(4), 1043–1068.  Google Scholar
  101. Alt, R./Puschmann, T. (2016): Digitalisierung der Finanzindustrie. Berlin/Heidelberg: Springer Berlin Heidelberg.  Google Scholar
  102. Amihud, Y./Mendelson, H./Pedersen, L. H. (2002): Illiquidity and Stock Returns Cross-Section and Time-Series Effects, 110–136.  Google Scholar
  103. 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.  Google Scholar
  104. Asur, S./Huberman, B. A. (2010): Predicting the Future with Social Media. In 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology – Workshops. WI-IAT 2010 Workshops: proceedings, 31 August–3 September 2010, Toronto, Ontario, Canada (pp. 492–499). [Los Alamitos, Calif.]: IEEE Computer Society.  Google Scholar

Abstract

As a result of technological innovations in data processing, the exploitation of Internet usage data in relation to search engines or social networks is becoming increasingly intriguing for understanding and anticipating stock market movements. We analyze the impact of three alternative investor attention variables, i.?e. Google search volume, Wikipedia page views, and stock market-relevant news on the rapidly growing FinTech sector. The result of the simultaneous correlation analysis reveals a highly significant correlation between the trading activities of the FinTech sector and the three investor attention variables. The time-delayed regression analysis complements the results by identifying substantial changes of the effects within one week considering the order of magnitude and sign. Furthermore, multivariate regression analysis highlights that the explanatory power for future stock trading activities and illiquidity primarily depends on Google search volume and stock market-relevant news volume, while the simultaneous correlations are best explained by the number of visits to the corresponding Wikipedia page.

Table of Contents

Section Title Page Action Price
Eduard Gaar / Valentin Moritz / Dirk Schiereck: The Impact of Media Attention on the Illiquidity of Stocks: Evidence from the Global FinTech Sector 1
Abstract 1
I. Introduction 1
II. Previous Research 3
III. Method 7
1. Data Sources 7
a) Google 7
b) Wikipedia 8
c) LexisNexis 8
2. FinTech Sector 9
3. The Selection and Inquiry of Financial Key Figures and Internet Search Activity 1
IV. Correlation Analysis of Liquidity and Search Activity 1
1. Partitioned Correlation Analysis of Search Activity and Liquidity 2
V. Multivariate Regression Analysis 2
1. Model 2
2. Results 2
a) Google Search Volume 2
b) Wikipedia Search Volume 3
c) News Volume 3
d) Comparison of the Results 3
VI. Conclusion 3
References 3
Appendix 3

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