Abstract
In financial markets, investment options are relatively fixed but may occasionally change as new securities (tickers) are introduced to the markets. Theory on exploratory behavior suggests that when new tickers are introduced, the text completion decision aid (autocomplete) can facilitate diversion of market participants from their initially intended ticker to the newly introduced ticker. We analyze whether such diversions have an economic impact in terms of transaction loss. Consistent with our hypothesis, we provide evidence that upon ticker introduction, turnover of securities that are syntactically similar to a newly introduced security is significantly reduced by 3%–5% around the starting day of trade of the new security. Autocomplete appears to have a significant economic effect on market transactions.
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Notes
The appendix includes examples from Safeway.com, Trivago.com, Yahoo! Finance, and CNBC Finance.
The introduction of a new ticker is tantamount to the introduction of a new security. A new security is introduced into the financial markets for two major reasons: the firm becomes public (it goes through an initial public offering known as IPO) or the firm has undergone a major restructuring (such as a merger between two firms) that led to a change in its ticker (its symbol on the exchange).
There is substantial evidence that stocks are underpriced around IPO (Ritter and Welch, 2002). Thus, an investor who is subject to exploratory behavior and diverts to a newly introduced security may behave rationally.
Fatigue and boredom are closely related to vigilance and attention management (Cummings et al., 2016). While these can further foster exploratory behavior, as noted, this behavior is innate and expected regardless of whether one is fatigued.
Autocomplete became fully operable on Google in 2008 (Gomez, 2013), but was available approximately a year prior in the Google Toolbar, and was quickly implemented afterwards by other websites and portals (Baifore et al., 2010; Gomez, 2013). The first finance mobile apps which emerged in 2009 already included autocomplete (Schroeder, 2009). We later show that our analysis is robust to the consideration of the incorporation of autocomplete in systems at any point between 2007–2009.
It should be noted that other events occurring around that period are immaterial to our analysis. In other words, an event such as the financial crisis of 2008 cannot provide an explanation for effects related to syntactic similarity.
Shipman et al. (2017) urge to use the most important variables when matching observations “because the estimated score depends upon the variables included in X… theory should motivate the variables included”. If many X variables are included in the procedure, the matching suitability is reduced because differences in variables that are unrelated to the dependent (i.e., change in turnover) are minimized, but differences in the key variables that may be related to the dependent may actually increase.
The t-statistics in column (1) of Table 3 and the rightmost column in Table 2 are different because of changes in the degrees of freedom and procedure. The t-statistics in Table 2 consider that we have two samples of 793 turnovers each, while those in Table 3 consider that we have 793 differences altogether.
Huberman et al. (1998) found that consumers have a lower threshold for uncertainty at the beginning of a navigation session, when they are more likely to click on hyperlinks that deviate from their navigational path.
For example, many products from the same company begin with the same prefix, e.g., Samsung Galaxy Note, Galaxy X, Galaxy Tab, etc. and Apple MacBook MacAir, Mac Mini, etc. Obviously, the similar product name choices are a result of many different considerations. This research suggests a possible ramification stemming from such similar name choices by companies.
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Acknowledgements
The authors are grateful for the valuable comments and suggestions of the editor and the three anonymous referees. The authors also thank Fredrik Beuk, Gal Oestreicher-Singer, and Debmalya Mukherjee for their comments and suggestions on the paper. The authors are also grateful for the comments raised in discussions on preliminary versions of this study presented at the University of Akron, Simon Fraser University, and the International Conference of Information Systems (Rubin & Rubin, 2019).
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Appendix
Appendix
Autocomplete in Trivago.com
Autocomplete in Safeway.com
Autocomplete in cnbc.com
Autocomplete in Yahoo! Finance
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Rubin, E., Rubin, A. On the economic effects of the text completion interface: empirical analysis of financial markets. Electron Markets 31, 717–735 (2021). https://doi.org/10.1007/s12525-021-00485-0
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DOI: https://doi.org/10.1007/s12525-021-00485-0