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Review of Quantitative Finance and Accounting

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12.03.2016 | Original Research

The effects of firms’ information environment on analysts’ herding behavior

verfasst von: Ryan D. Leece, Todd P. White

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 2/2017

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Abstract

We find financial analysts herd to a greater degree in firms with more opaque information environments as measured by the incidence of short-term institutional investors present. The S-statistic, a measure of forecast bias, and forecast timing and quality metrics are used to measure analyst herding behavior. The results are consistent with the notion that opaque information environments are more conducive to analysts engaging in reputational herding behavior where more capable analysts act first and less capable analysts follow. Additionally, analysts are more likely to issue forecast revisions subsequent to management earnings guidance in less opaque environments. Robustness tests indicate our operational measure of opacity is not subsumed by other measures of the information environment, namely information asymmetry.

Vorheriger Artikel Diversification benefits of risk portfolio models: a case of Taiwan’s stock market

Nächster Artikel Home bias in portfolio choices: social learning among partially informed agents

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We define opacity as the value of private information in forecasting earnings. Prior literature has documented a significantly negative effect of opacity on average forecast accuracy and a positive effect on forecast dispersion (e.g., Bhat et al. 2006; Hope 2003a, b).

Forecasts are identified as bold if they are above (below) the analysts’ prior forecast and the consensus immediately before issuance.

Institutional investors are typically disaggregated into two types based on their investment horizon. Dedicated institutional investors are defined as long-term investors in contrast to transient, or short-term, investors who have high portfolio turnover and employ earnings momentum strategies (e.g., Yan and Zhang 2009).

The calculation rests on two conditional probabilities: (1) conditional probability of one-half that a forecast exceeds actual earnings given it is greater than the consensus; (2) conditional probability of one-half that a forecast falls short of actual earnings given it is less than the consensus.

The information discovery stage is the 30-day window prior to a quarterly earnings announcement. The information analysis stage is the 5-day window on and after the quarterly earnings announcement.

The Securities and Exchange Commission enacted Regulation Fair Disclosure on October 23, 2000, in order to limit selective information disclosure.

This result differs from our primary test, to determine whether analysts are less likely to revise forecasts in environments where private information is valued more. However, our results support the inverse of our prediction. Analysts are more likely to revise their forecasts in environments where private information is valued less and public information is valued more.

For example, Han et al. (2001) provide empirical evidence that the market is only partially aware of optimistic analyst forecast bias.

This advantage is documented in Grinblatt and Titman (1989), Barber and Odean (2000), Wermers (2000), and Yan and Zhang (2009).

For example, Kao (2007); Lin and Manowan (2012) find transient institutional investors are associated with firms with higher and more persistent discretionary accruals.

A biased forecast is indicative of either herding or anti-herding behavior. Anti-herding indicates an analyst forecasts away from the consensus (rather than toward it) in the direction of his private information.

For example, assume the consensus annual EPS forecast for a company at time t is 1.25 and r days later (time t + r), the consensus rises to 1.35. Further, assume actual earnings per share are 1.30. Herding would be evidenced if the forecasts above the consensus (1.25) at time t were consistently below 1.30, and those below the consensus (1.35) at time t + r were consistently above 1.30. If this is the case, then the distribution of forecasts around actual earnings is not random, as analysts are adjusting their forecasts to be closer to the consensus.

In a theoretical study, Arya et al. (2005) predict Reg. FD could lead to increased herding behavior among analysts. In contrast, empirical studies (e.g., Mohanram and Sunder 2006; Mensah and Yang 2008) find very little evidence that Reg. FD influenced analyst herding tendencies.

The prevailing peers’ forecast is defined as the most recent analyst forecast. If other forecasts were issued on the same day as the most recent forecast then the prevailing peers’ forecast is the average of those forecasts.

A “greater than” forecast is a form of guidance where management gives an indication that earnings are expected to be “greater than” some amount, while a “less than” forecast is one where management explains that earnings are anticipated to be “less than” a given amount.

Transaction costs represent a threshold by which the marginal investor evaluates whether the incremental benefit from acting on new information exceeds the costs (Glosten and Milgrom 1985; Kyle 1985).

See Clement and Tse 2005; Bernhardt et al. 2006; Ramnath et al. 2008; Keskek et al. 2014 for a sample of the analyst herding literature.

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Titel: The effects of firms’ information environment on analysts’ herding behavior
verfasst von: Ryan D. Leece
Todd P. White
Publikationsdatum: 12.03.2016
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 2/2017
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-016-0559-z

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