Michael Eames

Earnings Management to Avoid Losses and Earnings Decreases: Are Analysts Fooled?*

This paper explores whether analyst forecasts impound the earnings management to avoid losses and small earnings decreases documented in Burgstahler and Dichev 1997, whether analysts are able to identify which specific firms engage in such earnings management, and the implications for significant forecast error anomalies at zero earnings and zero forecast earnings. We use data from Zacks Investment Research 1999 and find that analysts anticipate earnings management to avoid small losses and small earnings decreases. Further, analysts are much more likely to forecast zero earnings than firms are to realize zero earnings, and analysts are unable to consistently identify the specific firms that engage in earnings management to avoid small losses. This latter inability contributes to significant forecast pessimism associated with zero reported earnings and significant forecast optimism associated with zero earnings forecasts.

The Association Between Trading Recommendations and Broker-Analysts' Earnings Forecasts

This study examines analyst forecast errors within the context of stock recommendations. We predict positive forecast error (i.e., optimism) for buy recommendations and negative forecast error (i.e., pessimism) for sell recommendations. We offer two explanations for this prediction: (1) the unconscious tendency to process information in a manner that supports one’s goal, which we refer to as the “objectivity illusion” hypothesis, and (2) the economic incentive to boost trade, which we refer to as the “trade boosting” hypothesis. The pattern of analyst forecast bias we predict (i.e., optimism for buys and pessimism for sells) is opposite in direction to that predicted by the management relations hypothesis—a commonly cited hypothesis for analyst forecast bias. We find broker‐analyst earnings forecast errors are significantly optimistic for buy recommendations and significantly pessimistic for sell recommendations, consistent with the objectivity illusion and trade boosting hypotheses. Our study indicates that the pattern of results reported in prior research (i.e., increasingly optimistic earnings forecasts as the stock recommendation becomes less favorable) is likely driven by a correlated omitted variable, actual earnings. Results of an analysis to distinguish between trade boosting and objectivity illusion appear more consistent with the objectivity illusion.

Analyst vs. Market Forecasts of Earnings Management to Avoid Small Losses

Burgstahler and Eames (2003) present evidence that analysts commonly anticipate earnings management to avoid small losses, but often incorrectly predict its occurrence. Here we consider whether the market’s behavior mimics that of analysts. Our results suggest that analysts exhibit more forecast optimism in their zero earnings forecasts than in their other small earnings forecast levels, and markets exhibit less relative optimism at this point. At the 271-360 day forecast horizon, we find a reduction in the earnings response coefficient at analysts’ zero earnings forecasts and interpret this as reflecting less optimism in market earnings forecasts than in analyst forecasts when analysts forecast zero earnings. This evidence is consistent with the market not following analysts in erroneously predicting earnings management to avoid small losses. We do not find similar evidence for shorter forecast horizons, suggesting that market and analyst forecasts converge towards the end of the year. Finding differences in market and analyst earnings forecasts in this loss avoidance environment raises the possibility of differences in a variety of earnings management and other environments, and sends a general note of caution in using analyst forecasts issued early in the year to proxy market expectations.

The Effect Firm Reporting Quality Has on the Issuance and Properties of Disaggregated Earnings Forecasts

This study documents that analysts are more likely to issue revenue forecasts to complement earnings-per-share estimates (EPS) when the quality of firm financial reporting is low. This is because compared to EPS forecasts accuracy, revenue forecast accuracy is less adversely affected by poor reporting quality, and as a result, investors rely more on revenue than EPS estimates in their investment decisions when the reporting quality is low. The result is robust to using five proxies for the quality of firm financial reporting: the variation in discretionary accruals, the absolute level of discretionary accruals, earnings persistence, absolute total accruals, and earnings volatility. Further, we document that better earnings forecasters are more likely to issue revenue estimates. This is because only better quality analysts would want their forecasts to be subject to higher market scrutiny, and because a combination of accurate revenue and EPS forecasts is a stronger signal of the analyst forecasting skill compared to an accurate stand-alone EPS estimate only.