Andrew C. Call

Inside the 'Black Box' of Sell-Side Financial Analysts

Our objective is to penetrate the “black box” of sell-side financial analysts by providing new insights into the inputs analysts use and the incentives they face. We survey 365 analysts and conduct 18 follow-up interviews covering a wide range of topics, including the inputs to analysts’ earnings forecasts and stock recommendations, the value of their industry knowledge, the determinants of their compensation, the career benefits of Institutional Investor All-Star status, and the factors they consider indicative of high-quality earnings. One important finding is that private communication with management is a more useful input to analysts’ earnings forecasts and stock recommendations than their own primary research, recent earnings performance, and recent 10-K and 10-Q reports. Another notable finding is that issuing earnings forecasts and stock recommendations that are well below the consensus often leads to an increase in analysts’ credibility with their investing clients. We conduct cross-sectional analyses that highlight the impact of analyst and brokerage characteristics on analysts’ inputs and incentives. Our findings are relevant to investors, managers, analysts, and academic researchers.

Short-Term Earnings Guidance and Earnings Management

We study the relation between short-term earnings guidance and earnings management. We find that firms issuing short-term earnings forecasts exhibit significantly lower absolute abnormal accruals, our proxy for earnings management, than do firms that do not issue earnings forecasts. Regular guiders also exhibit less earnings management than do less regular guiders. These findings are contrary to conventional wisdom but consistent with the implications of Dutta and Gigler (2002) and the expectations alignment role of earnings guidance (Ajinkya and Gift 1984). Our results continue to hold after we control for self-selection and potential reverse causality concerns, and in a setting where managers are documented to have strong incentives to manage earnings. Additional analysis reveals that guiding firms exhibit less income-increasing accrual management whether firms guide expectations upwards or downwards, and no evidence that guiding firms inflate earnings through real activities management. We also provide evidence to demonstrate that meeting-or-beating benchmarks is not an appropriate proxy for earnings management in our research setting.

Changes in the I/B/E/S Database and Their Effect on the Observed Properties of Analyst Forecasts

I/B/E/S is a common source of analyst earnings forecast data, and the reliability of these data is important for practice and academic research. Examining a common sample period, we compare annual earnings forecasts across two versions of the I/B/E/S detail file, one made available in 2009 and the other made available in 2015. We find substantial differences in the contents of these two versions of the detail file, as well as significant differences in the attributes of the earnings forecasts available in each version. Specifically, the earnings forecasts in the more recent version are more accurate and less biased, and they identify substantially different firms as meeting or just beating analysts’ expectations than those in the older version. To highlight the potential impact of these differences, we show that the economic magnitude of the effects of analyst experience and brokerage size on earnings forecast accuracy change by over 30 percent when we use the more recent version instead of the older version. Additional analyses suggest that the differences across versions of the detail file are ongoing. In contrast, we find that different versions of the summary file exhibit only minor differences over time. We also find significant differences in the properties of consensus earnings forecasts calculated from the individual earnings forecasts available in the detail file and consensus earnings estimates from the summary file. Finally, we provide guidance to researchers using I/B/E/S for analyst earnings forecast data.