Individual Investors and Local Bias
By Mark S. Seasholes, Ning Zhu. 2010
569 citations recorded by the source corpus.
Subfields: Accounting, Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/839770c090c638218b598aeef8fc4026fb4ba156
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
- Neeltje van Horen
- Luc Laeven
- Alexander A. Popov
- Christian Catalini
- Mariassunta Giannetti
- Gabriel Chodorow-reich
- Alexander Hillert
- Heiko Jacobs
In Economics and Econometrics
- Mariassunta Giannetti
- Neeltje van Horen
- Luc Laeven
- Heiko Jacobs
- Christian Catalini
- Alexander A. Popov
- Ajay Agrawal
- Sebastian Müller
In Finance
- Neeltje van Horen
- Alexander A. Popov
- Luc Laeven
- Mariassunta Giannetti
- Heiko Jacobs
- Gabriel Chodorow-reich
- Sebastian Müller
- Alp Simsek
Citations in the corpus
- Are Syndicates the Killer App of Equity Crowdfunding? (2015, background): “For example, widespread home bias in investing has been used to document the importance of asymmetric information in investment decisions.(28) Such home bias is especially prevalent for new ventures, suggesting a particularly important role for asymmetric information.”
- Is the crowd sensitive to distance?—how investment decisions differ by investor type (2017, unknown): “Related research on home bias related to public equity offerings include Coval and Moskowitz (1999, 2001), Ivković and Weisbenner (2005), Sulaeman (2014), and Seasholes and Zhu (2010).”
- Exporting Sovereign Stress: Evidence from Syndicated Bank Lending during the Euro Area Sovereign Debt Crisis (2015, background): “This can happen due to biases arising from informational advantages for domestic investors (Brennan and Cao, 1997; Kang and Stulz, 1997; Ahearne, Griever, and Warnock, 2004; Portes and Rey, 2005; Van Nieuwerburgh and Veldkamp, 2009; Andrade and Chhaochharia, 2010), from familiarity considerations (G”
- Stock Market Wealth and the Real Economy: A Local Labor Market Approach (2019, methodology): “6The data are a random sample of accounts at the brokerage and have been used extensively to study individual trading behavior (Barber and Odean, 2000, 2001; Graham and Kumar, 2006; Barber and Odean, 2007; Mitton and Vorkink, 2007; Kumar, 2009; Seasholes and Zhu, 2010; Kent et al., 2019).”
- Risk and Return in High-Frequency Trading (2018, unknown): “trader category most likely captures retail traders (e.g. Kaniel, Saar, and Titman, 2008; Seasholes and Zhu, 2010).”
- The Behavior of Individual Investors (2011, unknown): “Investors in the LDB dataset hold about 30% of their portfolio in stocks headquartered within a 240 mile radius of their home, while only 12% of firms are located within the same radius (Ivkovich and Weisbenner, 2005; Seasholes and Zhu, 2010).”
- Geographic Dispersion and Stock Returns (2012, unknown): “However, see Seasholes and Zhu (2010) for contradicting evidence.”