Reading the Tea Leaves: Why Serial Correlation Patterns in Analysts' Forecast Errors are not Evidence of Inefficient Information Processing

2010 ◽  
Author(s):  
Juhani T. Linnainmaa ◽  
Walter N. Torous
Keyword(s):  
Information Processing ◽  
Serial Correlation ◽  
Forecast Errors ◽  
Tea Leaves

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.

Revisiting Romer: Digging Deeper Into Influences on NFL Managerial Decisions

2018 ◽  
Vol 20 (5) ◽  
pp. 671-689
Author(s):  
Brian Goff ◽  
Stephen L. Locke
Keyword(s):  
Logistic Regression ◽  
Information Processing ◽  
National Football League ◽  
Risk Premia ◽  
Substantial Part ◽  
Forecast Errors ◽  
Managerial Decisions ◽  
Related Risk ◽  
Starting Point ◽  
Incorrect Information

Using Romer’s framework as the starting point, we examine the decisions by National Football League (NFL) coaches to “go for it” on fourth down with an expanded sample drawn from 12 years (2002-2013) of NFL play-by-play data. Our expanded results generally confirm Romer’s finding of divergence from expected point maximization or win maximization. However, unlike Romer, by estimating forecast errors for points and wins and related risk premia along with logistic regression estimates of the influences on fourth down decisions, we find strong indications that risk aversion rather than incorrect information processing likely accounts for a substantial part of the divergence.

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.

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

Reading the tea leaves: Model uncertainty, robust forecasts, and the autocorrelation of analysts’ forecast errors

2016 ◽  
Vol 122 (1) ◽  
pp. 42-64 ◽  
Author(s):  
Juhani T. Linnainmaa ◽  
Walter Torous ◽  
James Yae
Keyword(s):  
Model Uncertainty ◽  
Forecast Errors ◽  
Tea Leaves

Serial Correlation in Management Earnings Forecast Errors*

2009 ◽  
Author(s):  
Guojin Gong ◽  
Laura Yue Li ◽  
Jundong (Jeff) Wang
Keyword(s):  
Serial Correlation ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Management Earnings Forecast

Model Uncertainty and Serial Correlation in Forecast Errors

2009 ◽  
Author(s):  
Juhani T. Linnainmaa ◽  
Walter N. Torous
Keyword(s):  
Model Uncertainty ◽  
Serial Correlation ◽  
Forecast Errors

Serial Correlation in Management Earnings Forecast Errors

2011 ◽  
Vol 49 (3) ◽  
pp. 677-720 ◽  
Author(s):  
GUOJIN GONG ◽  
LAURA Y. LI ◽  
JEFF J. WANG
Keyword(s):  
Serial Correlation ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Management Earnings Forecast

Do management earnings forecasts fully reflect information in past earnings changes?

2019 ◽  
Vol 27 (3) ◽  
pp. 373-406
Author(s):  
Guojin Gong ◽  
Yue Li ◽  
Ling Zhou
Keyword(s):  
Information Processing ◽  
Voluntary Disclosure ◽  
Performance Indicator ◽  
Forecast Errors ◽  
Management Forecasts ◽  
Management Earnings Forecasts ◽  
Content Type ◽  
Processing Bias ◽  
Information Processing Bias ◽  
Earnings Change

Purpose It has been widely documented that investors and analysts underreact to information in past earnings changes, a fundamental performance indicator. The purpose of this paper is to examine whether managers’ voluntary disclosure efficiently incorporates information in past earnings changes, whether analysts recognize and fully anticipate the potential inefficiency in management forecasts and whether managers’ potential forecasting inefficiency entirely results from intentional disclosure strategies or at least partly reflects managers’ unintentional information processing biases. Design/methodology/approach Archival data were used to empirically test the relation between management earnings forecast errors and past earnings changes. Findings Results show that managers underreact to past earnings changes when projecting future earnings and analysts recognize, but fail to fully anticipate, the predictable bias associated with past earnings changes in management forecasts. Moreover, analysts appear to underreact more to past earnings changes when management forecasts exhibit greater underestimation of earnings change persistence. Further analyses suggest that the underestimation of earnings change persistence is at least partly attributable to managers’ unintentional information processing bias. Originality/value This study contributes to the voluntary disclosure literature by demonstrating the limitation in the informational value of management forecasts. The findings indicate that the effectiveness of voluntary disclosure in mitigating market mispricing is inherently limited by the inefficiency in management forecasts. This study can help market participants to better use management forecasts to form more accurate earnings expectations. Moreover, our evidence suggests a managerial information processing bias with respect to past earnings changes, which may affect managers' operational, investment or financing decisions.

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