Questions

1. Firms post financial tweets more often around more material accounting news events, irrespective of the direction of the news (positive or negative)
   • Testing theory: Hummel, Morgan, and Stocken (2018 Working)
2. Firms use hyperlinks more often around more material accounting news events, irrespective of the direction of the news (positive or negative)

Example firm financial tweets

Example firm non-financial tweets

Classifying tweets

Tweeting behavior intraday

Financial tweets and Financial events

Financial tweets and event direction

Financial tweets and url usage

Other splits examined

• Loss vs non-loss firms (based on the prior year)
  • Loss firms push positive news a bit more
  • Non-loss firms push negative news a bit more
• Intraday windows
  • Earnings announcements pushed in the 3 hours after
  • 10-K filings pushed beforehand
• Using an older tweet classification method
  • Doesn’t work as well, particularly for earnings announcements

Research Questions

1. What drives exec adoption of Twitter?
2. Do executives disclose discretionarily?
   • Or they may tweet for intrinsic utility…
3. Does the market react to exec tweets?
   • This may be subsumed by firms’ tweets…
4. Why is there reaction to exec tweets?
   • New information? Or trust in executives

Example exec tweets (Business)

Example exec tweets (Non-business)

Tweet content over time

Executive Adoption of Twitter

Adoption is higher when younger, female, extroverted, or facing higher litigation risk. SEC’s 2013 guidance lowers adoption rate at high litigation risk firms.

Executives’ Discretionary Tweeting

Executives tweet more around important firm events, particularly around planned internal events.

Executives Drive Stock Returns

Executives’ tweets attract 583% more stock movement per tweet, accounting for 51% of Twitter-led movement.

Market Reaction Mechanism: Trust

\[ \begin{array}{l c c} \hline \text{Variable} & \left|MMR_{(+1)}\right| & t\text{-value} \\ \hline \text{Exec fin tweets} & -0.015 & (-1.57) \\ \text{Similarity} \times \text{Exec fin tweets} & 0.038^{*} & (1.91) \\ \text{Controls} & \text{Yes} & \\ \text{Firm FE} & \text{Yes} & \\ \text{Exec FE} & \text{Yes} & \\ \text{Year FE} & \text{Yes} & \\ \text{Month FE} & \text{Yes} & \\ \hline \end{array} \]

Result is consistent with Trust driving investor reaction to exec financial tweets.

Methodology: Trust vs Information

We identify how content-wise similar each exec tweet is to corresponding firm tweets via Universal Sentence Encoder (USE).

USE is a neural network method that relies on word meaning and word order to determine sentence meanings. It does not rely on word choice.

USE example

USE abstracts away from word choice!

Timing of the data

Data by language

Data by location

Reopening vs other discussion

Conclusion

• From an accounting perspective…
  • Non-traditional information sources are commonly in use
  • Companies are using Twitter to great effect for informing investors
    • Symmetrically disclosing both good and bad news
  • Executives have even more impact than firms when they disclose financial information on Twitter
    • As investors trust the executive more than firms’ own Twitter accounts
• From a macro perspective
  • Sources of public information such as Twitter provide an interesting set of insights into the reaction of individuals
    • This can be used effectively to understand macro phenomena or to understand the environment micro-econ phenomena occur in

Packages used for these slides

• dplyr
• ggplot2
• gridExtra
• kableExtra
• knitr
• revealjs