Inferring investor behavior: Evidence from TORQ data

By Charles M. C. Lee, B. Radhakrishna. 2000

490 citations recorded by the source corpus.

Subfields: Accounting, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2348221cf742fc887845d9cab3980fa92dcc427f

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Citations in the corpus

Are Small Investors Naive About Incentives (2007, unknown): “tor classification is based on NYSE data (Lee and Radhakrishna, 2000; Odders-White, 2000).”
Political contributions and analyst behavior (2015, unknown): “In particular, Lee and Radhakrishna (2000) show that the $5,000 tradesize cutoff can effectively identify trades initiated by retail investors.”
The Information Content of International Portfolio Flows (2001, unknown): “The analysis in Lee and Radhakrishna [2000] evaluates the effectiveness of the Lee and Ready matching algorithm.”
Managerial and Investor Responses to Disclosure Regulation: The Case of Reg Fd and Conference Calls (2004, unknown): “Following prior research (Lee 1992; Lee and Radhakrishna 2000), we define a “small” trade using a dollar-value based proxy.”
Evidence on the Speed of Convergence to Market Efficiency, forthcoming: Journal of Financial Economics (2001, unknown): “While Ellis, Michael, and O’Hara (2000) show that the algorithm is imperfect for Nasdaq stocks, the results of Lee and Radhakrishna (2000), Finucane (2000), and Odders-White (2000) indicate that the Lee/Ready algorithm is accurate enough for NYSE stocks as to not pose systematic problems in our larg”
Do Liquidity Measures Measure Liquidity (2009, unknown): “Second, there is no confusion in the Rule 605 data about buys vs. sells or about marketable orders vs. non-marketable orders whereas Lee and Radhakrishna (2000) report that the Lee and Ready (1991) method commonly used with TAQ data incorrectly classifies 24% of inside-the-spread trades that have a…”
Small Trades and the Cross-Section of Stock Returns (2008, unknown): “Moreover, using the torq data, Lee and Radhakrishna (2000) provide support for the use of trade size as a proxy for individual versus institutional trading. They also find that the classification accuracy can be enhanced by conditioning the cut-off point on firm size. Hvidkjaer (2005) suggests sorti”
Who Trades on Pro Forma Earnings Information (2004, background): “An investor can't know what's been left out, why it's left out, or how it compares with other companies' earnings” (Levinsohn 2002).”
An Empirical Analysis of Stock and Bond Market Liquidity (2005, unknown): “Yet, as shown in Lee and Radhakrishna (2000), and Odders-White (2000), the Lee/Ready algorithm is accurate enough as to not pose serious problems in our large sample study.”
Retail investor attention and stock price crash risk: Evidence from China (2019, methodology): “Instead, following Lee and Radhakrishna (2000), Hvidkjaer (2008), and Wang et al. (2011), we classify all trades by dollar-based trade size and apply small-size trades (each of which is less than RMB 100,000) to distinguish trades by retail investors.11 Furthermore, adopting Grinblatt and Keloharju…”
Stock Market Declines and Liquidity (2010, unknown): “We examine this possibility using the percentage of small trades, defined as trades below $5000, as a proxy for uninformed, behaviorally biased trades by individuals (see Lee (1992), Lee and Radhakrishna (2000), Barber, Odean and Zhu (2008)).”