New Evidence on Serial Correlation in Analyst Forecast Errors

1999 ◽  
Vol 28 (4) ◽  
pp. 106 ◽  
Author(s):  
Stacey R. Nutt ◽  
John C. Easterwood ◽  
Cintia M. Easterwood
Keyword(s):  
Serial Correlation ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
New Evidence

The Trend in Firm Profitability and the Cross-Section of Stock Returns

2017 ◽  
Vol 92 (5) ◽  
pp. 1-32 ◽  
Author(s):  
Ferhat Akbas ◽  
Chao Jiang ◽  
Paul D. Koch
Keyword(s):  
Risk Factors ◽  
Stock Returns ◽  
Cross Section ◽  
The Other ◽  
Analyst Forecast ◽  
Forecast Errors ◽  
Earnings Surprise ◽  
Firm Profitability ◽  
Earnings Momentum ◽  
Jel Classifications

ABSTRACT This study shows that the recent trajectory of a firm's profits predicts future profitability and stock returns. The predictive information contained in the trend of profitability is not subsumed by the level of profitability, earnings momentum, or other well-known determinants of stock returns. The profit trend also predicts the earnings surprise one quarter later, and analyst forecast errors over the following 12 months, suggesting that sophisticated investors underreact to the information in the profit trend. On the other hand, we find no evidence of investor overreaction, and our results cannot be explained by well-known risk factors. JEL Classifications: G12; G14.

The role of accounting fundamentals and other information in analyst forecast errors

2018 ◽  
Vol 21 (2) ◽  
pp. 175-194
Author(s):  
Danilo S. Monte-Mor ◽  
Fernando C. Galdi ◽  
Cristiano M. Costa
Keyword(s):  
Analyst Forecast ◽  
Forecast Errors ◽  
Other Information

scholarly journals Forecast Comparison Based on Random Walks

2016 ◽  
Vol 144 (2) ◽  
pp. 615-626 ◽  
Author(s):  
Timothy DelSole ◽  
Michael K. Tippett
Keyword(s):  
Random Walks ◽  
Serial Correlation ◽  
Model Development ◽  
Sign Test ◽  
Forecast Errors ◽  
Dynamical Models ◽  
Multimodel Ensemble ◽  
The North ◽  
Forecast Comparison ◽  
North American Multimodel Ensemble

Abstract This paper proposes a procedure based on random walks for testing and visualizing differences in forecast skill. The test is formally equivalent to the sign test and has numerous attractive statistical properties, including being independent of distributional assumptions about the forecast errors and being applicable to a wide class of measures of forecast quality. While the test is best suited for independent outcomes, it provides useful information even when serial correlation exists. The procedure is applied to deterministic ENSO forecasts from the North American Multimodel Ensemble and yields several revealing results, including 1) the Canadian models are the most skillful dynamical models, even when compared to the multimodel mean; 2) a regression model is significantly more skillful than all but one dynamical model (to which it is equally skillful); and 3) in some cases, there are significant differences in skill between ensemble members from the same model, potentially reflecting differences in initialization. The method requires only a few years of data to detect significant differences in the skill of models with known errors/biases, suggesting that the procedure may be useful for model development and monitoring of real-time forecasts.

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.

Nonlinear Adjustments of Volatility Expectations to Forecast Errors: Evidence from Markov-Regime Switches in Implied Volatility

2012 ◽  
Vol 15 (03) ◽  
pp. 1250007 ◽  
Author(s):  
Kazuhiko Nishina ◽  
Nabil Maghrebi ◽  
Mark J. Holmes
Keyword(s):  
Financial Crises ◽  
Regime Switching ◽  
Serial Correlation ◽  
Implied Volatility ◽  
Nonlinear Behavior ◽  
Heterogeneous Beliefs ◽  
Forecast Errors ◽  
Market Participants ◽  
Model Free ◽  
Switching Models

This paper tests for nonlinearities in the behavior of volatility expectations based on model-free implied volatility indices. Using Markov regime-switching models, the empirical evidence from the German, Japanese and U.S. markets suggests that there are indeed regime-specific levels of volatility expectations. Whereas the regimes seem to be governed by the degree of serial correlation and adjustment to forecast errors, there is no evidence of significant leverage effects. The frequency of regime shifts in volatility expectations is affected by the onset of financial crises, which have the effect of increasing the likelihood of regimes driven by lower autoregressive effects and faster speeds of adjustment. The evidence suggests that despite the heterogeneous beliefs of market participants, implied volatility indices provide a measure of consensus expectations that can be useful in understanding the nonlinear behavior of volatility expectations during periods of financial instability.

scholarly journals On the Rationality of Medium-Term Tax Revenue Forecasts: Evidence from Germany

2015 ◽  
Vol 235 (1) ◽  
pp. 22-40 ◽  
Author(s):  
Christian Breuer
Keyword(s):  
Serial Correlation ◽  
Institutional Setting ◽  
Tax Revenue ◽  
Forecast Errors ◽  
Structural Level ◽  
Medium Term ◽  
Short Term ◽  
Percentage Points ◽  
Rational Behaviour ◽  
Revenue Projections

Summary This paper examines tax revenue projections in Germany for the period 1968 to 2012 with a focus on forecasting rationality. It is shown that tax revenue forecasts for the medium-term are upward biased. Overoptimistic revenue projections are particularly pronounced after the German reunification and reflect upward-biased GDP projections in this period. The predicted tax-GDP-ratio appears to be upward biased, as well. The forecasts are likely to overestimate tax revenues if the predicted tax-GDP-ratio exceeds its structural level of approximately 22½ percentage points. The results also indicate that forecast errors of short-term projections for the current year exhibit serial correlation. It is conceivable that the non-rational behaviour can be traced back to the specific institutional setting of revenue forecasting and budgetary planning in Germany.

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.

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