May I Have Your Attention, Please: The Market Microstructure of Investor Attention


Fink, Christopher ; Johann, Thomas



DOI: https://doi.org/10.2139/ssrn.2139313
Additional URL: http://ssrn.com/abstract=2139313
Document Type: Working paper
Year of publication: 2014
The title of a journal, publication series: SSRN Working Paper Series
Place of publication: Rochester, NY
Publication language: English
Institution: Außerfakultäre Einrichtungen > Graduate School of Economic and Social Sciences - CDSB (Business Studies)
Subject: 330 Economics
Abstract: This paper analyzes how a stock’s liquidity, turnover, volatility and returns are driven by short term fluctuations in investor attention. Attention-grabbing stocks are identified by their daily Google Search Volume. A newly developed download algorithm allows the creation of a search volume dataset with more frequent observations. In contrast to the existing literature, this paper provides a long term analysis of daily search volume. The dynamics of investor trading on high and low atten- tion days are examined in an attention-adjusted structural model based on Easley et al. (1996). The increase in trading on high attention days is due to more trading by both informed and uninformed investors; however, the probability of informed trading on high attention days is lower. Turnover and volatility of stocks significantly increase on high attention days whereas liquidity and short term returns are not significantly influenced. This relation is more pronounced for large stocks, stocks with a lower level of cross-sectional attention, and stocks with a higher proportion of retail trading and remains robust in several endogeneity checks. The results provide important insights into the trading dynamics on high attention days and show that these days are characterized by high volatility and uninformed trading.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Fink, Christopher ; Johann, Thomas (2014) May I Have Your Attention, Please: The Market Microstructure of Investor Attention. SSRN Working Paper Series Rochester, NY [Working paper]


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