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Review of Accounting Studies

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01-09-2020

Analyst teams

Authors: Bingxu Fang, Ole-Kristian Hope

Published in: Review of Accounting Studies | Issue 2/2021

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Abstract

This paper examines the impact of teamwork on sell-side analysts’ performance. Using a hand-collected sample of over 50,000 analyst research reports, we find that analyst teams issue more than 70% of annual earnings forecasts. In contrast, most research implicitly assumes that forecasts are issued by individual analysts. We document that analyst teams generate more accurate earnings forecasts than individual analysts and that the stock market reacts more strongly to forecast revisions issued by teams. Analyst teams also cover more firms, issue earnings forecasts more frequently, and issue less stale forecasts. Analysts working in teams are more likely to be voted as All-Star analysts in the future. Among analyst teams, we show that team size and team member ability are significantly associated with forecast accuracy. Moreover, using detailed analyst background information from LinkedIn, we find that forecast accuracy is positively associated with team diversity based on sell-side experience, educational background, and gender. Additional analyses suggest that analyst teams, especially more diverse ones, are more likely to issue cash-flow forecasts and use discounted cash-flow valuation models in their reports. These findings suggest that teamwork and team diversity play a crucial role in understanding sell-side analysts’ performance.

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We refer to the I/B/E/S analyst as the person whose name is associated with the forecast in I/B/E/S.

For instance, a lead analyst with the CFA designation can work with either CFA team members or non-CFA team members. We expect team member’s professional designations to impact team performance after controlling for the lead analyst’s professional designation.

As Investext does not cover all brokerages, we acknowledge that our sample may have a selection bias (similar to most other studies in this line of research).

P.Eng. refers to Professional Engineer, and P.Geo. refers to Professional Geologist.

The pattern in our data suggests that an analyst can work with different team members for different firms. Therefore we measure TEAM at the analyst-firm-year level.

Our inferences are similar using other measures. For example, we have considered the distinct number of educational major categories of each team or an HHI measure based on further splitting each major category into undergraduate, graduate, and doctoral levels (untabulated).

Drake, Joos, Pacelli, and Twedt (2019) show that bundled forecasts are less accurate, less bold, and less informative to investors than unbundled forecasts.

We winsorize all continuous variables at the top and bottom 1% tails. Inferences are robust to winsorizing at other cutoffs or no winsorizing.

The inferences remain similar if we use demeaned values of performance measures and analyst characteristics variables, following Bradley et al. (2017), or if we use unadjusted values of each variable and include firm-year fixed effects to control for systematic differences across firm-years.

Team size of one refers to individual forecasts.

Because we exclude firm-years that are only covered by individuals or teams, the 73% number should be interpreted as the lower bound for the prevalence of teamwork, considering that the percentage of teams is even higher in the excluded observations.

The percentage is lower than that of Drake et al. (2019), as we only consider one-year ahead earnings forecasts, whereas they also include forecasts with longer horizons.

The inferences remain unchanged using other clustering structures, such as at the firm or industry level.

Lag value of Accuracy is the analyst’s standardized forecast Accuracy for the same firm in the previous fiscal year.

For example, the predicted value of Accuracy in Column 1 is 0.594 for individuals and 0.631 for analyst teams, suggesting that teams are 0.631/0.594–1 = 6.23% more accurate than individuals. Specially, the predicted value for individuals is the intercept 0.594, and the predicted value 0.631 for analyst teams is 0.594 + 0.037 × 1 = 0.631.

We obtain similar results (untabulated) if using the unadjusted value of forecast accuracy and control variables. In untabulated analyses, we also find that our findings are robust to controlling for whether team members other than the I/B/E/S analyst have ever appeared in I/B/E/S.

To further validate our measure of TEAM, which aims to captures whether analysts work with other people or just by themselves, we manually check 100 random cases of changes in TEAM over time. In 81 cases, we can collect the full employment history of team members, and in 71 cases (88%) the changes in TEAM correspond to the changes in team members’ employment status. This percentage should be interpreted as the lower bound of the accuracy of TEAM, because there might be reasons other than changes in employment status that affect whether a research associate works with the lead analyst.

Analysts with less experience and worse forecasting performance may be more likely to need help from others and thus work in teams. We control for analyst experience and previous-year forecast accuracy in the first-stage regression. As shown in Panel C of Table 3, our results do not support this argument.

The IMR is based on the probit estimate and it is calculated as the ratio of the standard normal probability density function divided by its cumulative probability.

In untabulated tests, we use each analyst’s covered firms without variation in TEAM as the control group. This sample is more balanced with respect to the number of treated and control observations. The results are similar to the findings in Panel B of Table 3.

In untabulated tests, we find that the effect of professional designations mainly comes from CFA, not CPA, P.Eng., or P.Geo. These results add to those reported by De Franco and Zhou (2009).

2.3% is calculated as the estimated coefficient 0.014 divided by sample mean 0.62 (0.014/0.62 = 2.3%).

Length is defined as the number of characters. Flesch Reading Ease = 206.8 – (1.015 × words per sentence) – (84.6 × syllables per word). Flesch Kincaid Index = (11.8 × syllables per word) + (0.39 × words per sentence) – 15.59. Fog = (words per sentence + percent of complex words) × 0.4.

Our inferences are robust to scaling by stock price, following Christie (1987).

Our inferences are unchanged if we exclude forecast revisions that experience concurrent earnings announcement in a three-day window. And our inferences (untabulated) are robust to measuring market reaction using intraday return following Keskek, Tse, and Tucker (2014).

The inferences remain similar if we employ an OLS model.

Similarly, no inferences are affected if we include the squared root of Broker Size as an additional control variable.

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Title: Analyst teams
Authors: Bingxu Fang
Ole-Kristian Hope
Publication date: 01-09-2020
Publisher: Springer US
Published in: Review of Accounting Studies / Issue 2/2021
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI: https://doi.org/10.1007/s11142-020-09557-6

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