Tax-Motivated Loss Shifting

2013 ◽  
Vol 88 (5) ◽  
pp. 1657-1682 ◽  
Author(s):  
Merle M. Erickson ◽  
Shane M. Heitzman ◽  
X. Frank Zhang
Keyword(s):  
Earnings Forecasts ◽  
Data Availability ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
Corporate Reporting ◽  
Earnings Surprises ◽  
Reporting Decisions ◽  
Tax Payments ◽  
Tax Loss ◽  
Do So

ABSTRACT: This paper examines the implications of tax loss carryback incentives for corporate reporting decisions and capital market behavior. During the 1981 through 2010 sample period, we find that firms increase losses in order to claim a cash refund of recent tax payments before the option to do so expires, and we estimate that firms with tax refund-based incentives accelerate about $64.7 billion in losses. Tax-motivated loss shifting is reflected in both recurring and nonrecurring items and is more evident for financially constrained firms. Analysts do not generally incorporate tax-motivated loss shifting into their earnings forecasts, resulting in more negative analyst forecast errors for firms with tax-based incentives than for firms without. Holding earnings surprises constant, however, investors react less negatively to losses reported by firms with tax loss carryback incentives. Data Availability: Data are available from sources identified in the paper.

scholarly journals The Role of Anchoring Bias in the Equity Market: Evidence from Analysts’ Earnings Forecasts and Stock Returns

2012 ◽  
Vol 48 (1) ◽  
pp. 47-76 ◽  
Author(s):  
Ling Cen ◽  
Gilles Hilary ◽  
K. C. John Wei
Keyword(s):  
Stock Returns ◽  
Earnings Forecasts ◽  
Equity Market ◽  
Stock Splits ◽  
Forecast Errors ◽  
Forecast Revisions ◽  
Earnings Per Share ◽  
Earnings Surprises ◽  
Anchoring Bias

AbstractWe test the implications of anchoring bias associated with forecast earnings per share (FEPS) for forecast errors, earnings surprises, stock returns, and stock splits. We find that analysts make optimistic (pessimistic) forecasts when a firm’s FEPS is lower (higher) than the industry median. Further, firms with FEPS greater (lower) than the industry median experience abnormally high (low) future stock returns, particularly around subsequent earnings announcement dates. These firms are also more likely to engage in stock splits. Finally, split firms experience more positive forecast revisions, more negative forecast errors, and more negative earnings surprises after stock splits.

Earnings Surprises that Motivate Analysts to Reduce Average Forecast Error

2008 ◽  
Vol 83 (2) ◽  
pp. 303-325 ◽  
Author(s):  
Orie E. Barron ◽  
Donal Byard ◽  
Yong Yu
Keyword(s):  
Private Information ◽  
Forecast Error ◽  
Earnings Forecasts ◽  
Forecast Errors ◽  
Future Earnings ◽  
Earnings Surprises ◽  
The Mean ◽  
Negative Earnings

Large earnings surprises and negative earnings surprises represent more egregious errors in analysts' earnings forecasts. We find evidence consistent with our expectation that egregious forecast errors motivate analysts to work harder to develop or acquire relatively more private information in an effort to avoid future forecasting failures. Specifically, we find that after large or negative earnings surprises there is a greater reduction in the error in individual analysts' forecasts of future earnings, and these individual forecasts are based more heavily on individual analysts' private information. This increased reliance on private information reduces the error in the mean forecast of upcoming earnings (even after controlling for the effect of reduced error in individual forecasts). As reliance on private information increases, more of each individual forecast error is idiosyncratic, and thus averaged out in the computation of the mean forecast.

Analysts, Taxes, and the Information Environment

2019 ◽  
Vol 42 (1) ◽  
pp. 103-131 ◽  
Author(s):  
Sangwan Kim ◽  
Andrew P. Schmidt ◽  
Kelly Wentland
Keyword(s):  
Accounting Information ◽  
Earnings Forecasts ◽  
Information Environment ◽  
Data Availability ◽  
Forecast Errors ◽  
Complete Understanding ◽  
The Public ◽  
Earnings Information ◽  
Earnings Components ◽  
Jel Classifications

ABSTRACT This paper investigates the extent to which analysts incorporate tax-based earnings information into their earnings forecasts relative to other earnings information. We find that analysts' misreaction to tax-based earnings information is distinct from their misreaction to other (nontax) accounting information, on average. We then show that analysts differ in their misestimation of tax and other (nontax) earnings components only when firms have weak information environments; when firms have strong information environments, analysts' forecasts fully incorporate tax-based earnings information and exhibit no difference incorporating tax-based earnings information relative to other accounting information. Our evidence suggests that, on average, forecasting tax-based earnings information is more difficult for analysts relative to forecasting other accounting information. However, access to appropriate information and resources enables analysts to better process tax information. Overall, we contribute to the literature by providing a more complete understanding of the source of analysts' tax-related forecast errors. JEL Classifications: H25; M41; D82; G14. Data Availability: Data are available from the public sources identified in the text.

How the Interplay between Financial and Nonfinancial Measures Affects Management Forecasting Behavior

2019 ◽  
Vol 31 (3) ◽  
pp. 41-63 ◽  
Author(s):  
Joseph F. Brazel ◽  
Bradley E. Lail
Keyword(s):  
Earnings Forecasts ◽  
Data Availability ◽  
Forecast Errors ◽  
Earnings Growth ◽  
Management Forecast ◽  
Nonfinancial Measures ◽  
External Stakeholders ◽  
Jel Classifications ◽  
Future Management

ABSTRACT This study examines how the interplay between financial and nonfinancial measures (NFMs) affects management forecasting behavior. Building on the knowledge that NFMs are typically aligned with actual earnings and are likely incorporated into earnings forecasts, we investigate if the level of divergence between changes in NFMs and contemporaneous changes in earnings influences management forecasting behavior. We hand collect company-specific NFMs disclosed in 10-K filings and describe how a greater divergence between NFMs and earnings (i.e., NFM changes substantially outpacing earnings growth, or vice versa) is associated with greater uncertainty about the underlying business. As such, in more divergent settings, we observe that management is less likely to issue guidance. Consistent with our theory, for managers that do provide guidance in more divergent settings, management forecast errors increase. Last, we provide evidence that external stakeholders can use the level of divergence to predict future management forecasting behavior. JEL Classifications: G14; M40; M41. Data Availability: The data used in this study are publicly available from the sources indicated in the text.

Identifying the News in Analysts’ Earnings Forecasts Revisions: An Alternative to the Random Walk Expectation

2021 ◽  
Author(s):  
Ray Pfeiffer ◽  
Karen Teitel ◽  
Susan Wahab ◽  
Mahmoud Wahab
Keyword(s):  
Random Walk ◽  
Stock Returns ◽  
Earnings Forecasts ◽  
Simple Random Walk ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
Earnings Expectations ◽  
Forecast Revisions ◽  
Conditioning Information ◽  
Using Data

Previous research indicates that analysts’ forecasts are superior to time series models as measures of investors’ earnings expectations. Nevertheless, research also documents predictable patterns in analysts’ forecasts and forecast errors. If investors are aware of these patterns, analysts’ forecast revisions measured using the random walk expectation are an incomplete representation of changes in investors’ earnings expectations. Investors can use knowledge of errors and biases in forecasts to improve upon the simple random walk expectation by incorporating conditioning information. Using data from 2005 to 2015, we compare associations between market-adjusted stock returns and alternative specifications of forecast revisions to determine which best represents changes in investors’ earnings expectations. We find forecast revisions measured using a ‘bandwagon expectations’ specification, which includes two prior analysts’ forecast signals and provides the most improvement over random-walk-based revision measures. Our findings demonstrate benefits to considering information beyond the previously issued analyst forecast when representing investors’ expectations of analysts’ forecasts.

I/B/E/S Reported Actual EPS and Analysts' Inferred Actual EPS

2012 ◽  
Vol 88 (3) ◽  
pp. 853-880 ◽  
Author(s):  
Lawrence D. Brown ◽  
Stephannie Larocque
Keyword(s):  
Majority Rule ◽  
Forecast Accuracy ◽  
Earnings Forecasts ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Sample Period ◽  
Forecast Dispersion ◽  
Forecast Revisions ◽  
Earnings Surprises ◽  
Analyst Earnings

ABSTRACT Users of I/B/E/S data generally act as if I/B/E/S reported actual earnings represent the earnings analysts were forecasting when they issued their earnings estimates. For example, when assessing analyst forecast accuracy, users of I/B/E/S data compare analysts' forecasts of EPS with I/B/E/S reported actual EPS. I/B/E/S states that it calculates actuals using a “majority rule,” indicating that its actuals often do not represent the earnings that all individual analysts were forecasting. We introduce a method for measuring analyst inferred actuals, and we assess how often I/B/E/S actuals do not represent analyst inferred actuals. We find that I/B/E/S reported Q1 actual EPS differs from analyst inferred actual Q1 EPS by at least one penny 39 percent of the time during our sample period, 36.5 percent of the time when only one analyst follows the firm (hence, this consensus forecast is based on the “majority rule”), and 50 percent of the time during the last three years of our sample period. We document two adverse consequences of this phenomenon. First, studies failing to recognize that I/B/E/S EPS actuals often differ from analyst inferred actuals are likely to obtain less accurate analyst earnings forecasts, smaller analyst earnings forecast revisions conditional on earnings surprises, greater analyst forecast dispersion, and smaller market reaction to earnings surprises than do studies adjusting for these differences. Second, studies failing to recognize that I/B/E/S EPS actuals often differ from analyst inferred actuals may make erroneous inferences.

Do Investors Fully Unravel Persistent Pessimism in Analysts' Earnings Forecasts?

2017 ◽  
Vol 93 (3) ◽  
pp. 349-377 ◽  
Author(s):  
David Veenman ◽  
Patrick Verwijmeren
Keyword(s):  
Stock Returns ◽  
Earnings Forecasts ◽  
Analyst Forecast ◽  
Short Term ◽  
Market Prices ◽  
Earnings Expectations ◽  
Earnings Surprises ◽  
Announcement Returns ◽  
Low Probability ◽  
Jel Classifications

ABSTRACT This study presents evidence suggesting that investors do not fully unravel predictable pessimism in sell-side analysts' earnings forecasts. We show that measures of prior consensus and individual analyst forecast pessimism are predictive of both the sign of firms' earnings surprises and the stock returns around earnings announcements. That is, we find that firms with a relatively high probability of forecast pessimism experience significantly higher announcement returns than those with a low probability. Importantly, we show that these findings are driven by predictable pessimism in analysts' short-term forecasts, as opposed to optimism in their longer-term forecasts. We further find that this mispricing is related to the difficulty investors have in identifying differences in expected forecast pessimism. Overall, we conclude that market prices do not fully reflect the conditional probability that a firm meets or beats earnings expectations as a result of analysts' pessimistically biased short-term forecasts. JEL Classifications: G12; G14; G20.

Audit Quality and Analyst Forecast Accuracy: The Impact of Forecast Horizon and Other Modeling Choices

2015 ◽  
Vol 35 (2) ◽  
pp. 167-185 ◽  
Author(s):  
Yi (Ava) Wu ◽  
Mark Wilson
Keyword(s):  
Audit Quality ◽  
Forecast Accuracy ◽  
Earnings Forecasts ◽  
Analyst Forecasts ◽  
Data Availability ◽  
Analyst Forecast ◽  
Forecast Horizon ◽  
Financial Reports ◽  
Decision Usefulness ◽  
The Impact

SUMMARY The accuracy and other properties of analyst earnings forecasts represent potentially useful proxies for the impact of audit quality on client financial reports. Extant research in the auditing literature, however, is characterized by diametrically opposite predictions and inconsistent findings regarding the relationship between audit quality and analyst forecast accuracy. We argue that a potential reason for the inconsistency in the literature reflects these studies' focus on end-of-year forecast accuracy, which is subject to competing effects of audit quality. High-quality auditors may simultaneously improve forecast accuracy through their impact on the decision usefulness of clients' prior period reports, and reduce forecast accuracy by constraining client attempts to manage earnings in the direction of the consensus forecast. We argue and present evidence in support of the conjecture that analysts' beginning-of-year forecasts are a superior metric for identifying the impact of audit quality on the properties of analyst forecasts because the decision usefulness effect of audit quality should be dominant with respect to those forecasts. Data Availability: Data are available from sources identified in the article.

The Interactive Role of Difficulty and Incentives in Explaining the Annual Earnings Forecast Walkdown

2016 ◽  
Vol 91 (4) ◽  
pp. 995-1021 ◽  
Author(s):  
Mark T. Bradshaw ◽  
Lian Fen Lee ◽  
Kyle Peterson
Keyword(s):  
Interactive Effect ◽  
Earnings Forecasts ◽  
Data Availability ◽  
Analyst Forecast ◽  
Earnings Forecast ◽  
Regression Analyses ◽  
Strategic Incentives ◽  
Psychology Literature ◽  
Annual Earnings

ABSTRACT The within-year walkdown of analysts' earnings forecasts has largely been attributed to analysts' incentives to curry favor with managers. We appeal to cognitive psychology literature on motivated reasoning and propose that forecasting difficulty interacts with such incentives to yield the observed walkdown. Higher forecasting difficulty generates a wider range of outcomes from which analysts can justify optimistically biased forecasts. In regression analyses, we find that the interaction between analysts' incentives for optimism and difficulty exhibits the strongest effect on earnings walkdowns. We also examine revenue forecasts as a benchmark of lower forecasting difficulty and find that revenue walkdowns are relatively diminutive. However, when analysts forecast losses, revenue forecasts are more critical and exhibit markedly steeper walkdowns. Our results suggest that analyst forecast walkdowns are better characterized by an interactive effect between analysts' strategic incentives for optimism and forecasting difficulty. JEL Classifications: G17; M41. Data Availability: Data are available from public sources identified in the text.

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