Predictable Behavior, Profits, and Attention

By Mark S. Seasholes, Guojun Wu. 2007

356 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c3a86af731ba47995e853a7443efc2b4fc5f0f87

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Shiyang Huang
2. Yulin Huang
3. F. Teulon
4. R. Ding
5. Amal Aouadi
6. Dong Lou
7. Wenxuan Hou
8. Dehua Shen

In Finance

1. Shiyang Huang
2. Yulin Huang
3. F. Teulon
4. R. Ding
5. Amal Aouadi
6. Dong Lou
7. Wenxuan Hou
8. Dehua Shen

In Management Science and Operations Research

1. R. Ding
2. Amal Aouadi
3. Dong Lou
4. Wenxuan Hou
5. Dehua Shen
6. Shiyang Huang
7. Yulin Huang
8. Tse-Chun Lin

Citations in the corpus

• Attention allocation and return co-movement: Evidence from repeated natural experiments (2019, methodology): “19 Schmidt (2013) uses Google search for different sports as a proxy for attention shocks and find that 4 countries show significantly negative changes in synchronicity and 13 countries have positive changes in synchronicity.”
• The Cross-Section of Speculator Skill: Evidence from Day Trading (2014, neutral): “Seasholes and Wu (2007) examine the trades of ten extremely active traders on the Shanghai Stock Exchange.”
• Investor attention and stock market activity: Evidence from France (2013, background): “, 2004; Chemmanur and Yan, 2009) and price limits (Seasholes and Wu, 2007).”
• In Search of Attention (2009, neutral): “Recent studies provide a theoretical framework in which limited attention can a¤ect asset pricing statics as well as dynamics.1 When testing theories of attention, empiricists face a substantial problem: we don t have direct measures of investor attention.”
• Retail Investor Attention and Stock Liquidity (2015, result): “This is in line with the finding of Seasholes and Wu (2007) that stocks with higher returns or higher risk receive more news coverage and therefore attract more attention among investors.”
• Driven to Distraction: Extraneous Events and Underreaction to Earnings News (2009, neutral): “…the forecast error (FE) as a measure of the earnings surprise, we calculate the difference between announced earnings as reported by I/B/E/S (eiq) and the consensus earnings forecast (Fiq), defined as the median of the most recent forecasts from individual analysts using the I/B/E/S detail tape.”
• Open source information, investor attention, and asset pricing (2013, background): “…(Chemmanur and Yan, 2009; Grullon et al., 2004), media coverage (Chan, 2003; Fang and Peress, 2009), excessive trading volume and stock return (Barber and Odean, 2008) and price limit event (Seasholes and Wu, 2007), are applied in study the dynamics of investor attention and asset pricing.”
• Does It Pay to Pay Attention (2018, neutral): “…researchers to resort to attention proxies such as trading volume 6 ((Gervais, Kaniel, and Mingelgrin, 2001)), price limits (Li and Yu (2012) and Seasholes and Wu (2007)), and news (Yuan (2015) and Barber and Odean (2007)), and making the implicit assumption that investors are likely to pay…”
• Attracting investor attention through advertising (2014, background): “9See, for example, Gervais, Kaniel, and Mingelgrin (2001); Chen, Noronha, and Singal (2004); Seasholes and Wu (2007); Barber and Odean (2008); Lehavy and Sloan (2008); Fang and Peress (2009); Kaniel, Li, and Starks (2010).”