Relative accuracy of analysts’ earnings forecasts over time: a Markov chain analysis

Hsu, D. A.; Chiao, Cheng-Huei

doi:10.1007/s11156-010-0214-z

Cited by 14 publications

(4 citation statements)

References 31 publications

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“…For one, the use of mean analysts' forecast could lead to a biased (usually overly optimistic) estimate of the expected future earnings, which may not capture non‐accounting information effectively. The relevance and reliability of accounting forecasts in valuation have been shown to differ drastically for different firms and at different points in time (Hsu & Chiao, 2011). As cautioned by Cheng (2005), overreliance on analysts' forecasts in valuation can lead to noisy, “black box” estimates and biased results.…”

Section: Resultsmentioning

confidence: 99%

Temporal changes in the value relevance of analysts' forecasts

Johnston

Guidry

Trimble

2021

J Corp Accounting Finance

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We examine the temporal change in the value relevance of analysts' forecasts relative to accounting information over a 42‐year period. The temporal change in value relevance is measured using the explained variation (R2) from the cross‐sectional regressions of security prices and returns on accounting metrics, specifically earnings and book value, alone and jointly with other information proxied by analysts' forecasts using a sample of 102,875 U.S. firm‐year observations. Prior research suggests that the value relevance of accounting information has declined over time. If investors are relying on metrics other than financial metrics, we would expect the reliance on analysts' forecasts to increase. Interestingly, the joint relevance of accounting and analysts' forecasts also declines over our sample period and the incremental relevance of analysts' forecasts remains constant over our sample period, suggesting that investors are not substituting analysts' forecasts for accounting information.

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Section: Resultsmentioning

confidence: 99%

Temporal changes in the value relevance of analysts' forecasts

Johnston

Guidry

Trimble

2021

J Corp Accounting Finance

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“…Another factor that can lead to weekend information release by analysts is workload. Researchers (Hsu and Chiao, 2011; Valentine, 2010) reveal that heavy workload can cause analysts to underperform the benchmark. A job workload survey reported by Salary Explorer (2016) reveals that the workload index for financial analysts is worse than the average workload indexes of 51 occupations surveyed.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Analysts issuing forecasts on weekends

Liu

2017

IJAIM

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Purpose This study aims to investigate the motivation of financial analysts issuing forecasts on weekends and the impact of such behavior on forecast accuracy and analysts’ careers. Design/methodology/approach Logistic regression and ordinary least squares models with Huber–White standard errors were used in this study. Findings This paper first documented the emerging trends of the weekend forecasts after 2000. Longitudinal data from 2002 to 2011 validated that analysts’ conscientious timing of information release in line with their workload and confidence level gives more accurate forecasts. Further, given the same accuracy, analysts exhibiting diffident behaviors (analysts who are predicted to work on weekdays but in fact work on weekends) are not fired or demoted by brokerage houses, but those exhibiting inactive behaviors (analysts who are predicted to work on weekends but did not do so) are more likely to be dismissed or demoted by brokerage houses, indicating that brokerage houses are aware of the negative effect of both behaviors, but treat them differently. Research limitations/implications Weekend versus weekday proxies for an analyst’s timing of information release consider only one of many timing options. Other timing proxies, the nature and the composition of the information release of analysts are not examined in this study. Practical implications For practitioners, the results indicate that depending on the alignment, capital market can predict analysts’ future forecast accuracy, and hence, respond accordingly. For example, in addition to analyst forecast level or change, investors could pay attention to when the information is released to the market and possible reasons behind the choice of timing. Investors can thus better assess the forecast accuracy of one specific forecast and respond with the right action. Furthermore, analysts can better project their own forecast accuracy and career potential by assessing to what extent their forecasts are released conscientiously. Social implications This study examines analysts’ forecast behavior, but generate some insights on linking the analysts and investors in the capital market. Originality/value This study is the author’s original work.

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“…Let us see some specific applications. Hsu and Chiao (2012) started from predictions based on Markov chain for firms' earnings and they evaluated the evolution in time of the relative accuracy established by experts. Sakamoto (2012) used the Markov chain method to predict indicators for the Japanese industry, showing the equivalence of the method with a VAR(1) method.…”

Section: Predictions Based On Markov Chainsmentioning

confidence: 99%

exactitud de las predicciones para la estructura de cesta del consumo: un análisis comparativo entre la zona euro y Rumanía

Bratu

2016

RMCE

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En este estudio se aplica el método de las cadenas de Markov para predecir la estructura de la cesta de consumo para la zona euro y para Rumanía, un país que trata de cumplir las condiciones de entrada en la zona euro. En ambos casos, se sigue la misma metodología para la determinación del índice armonizado de precios al consumo (IPCA). La evaluación ex-post de las previsiones para el período 2010-2012 pone de manifiesto la mejora de la precisión de las previsiones para la zona euro al usar este método. El mayor grado de precisión en cada unidad territorial se ha registrado para los pesos de los servicios, de acuerdo con el estadístico U de Theil, aunque los indicadores absolutos de precisión son más bajos para otras predicciones de pesos. Se considera que las predicciones para el año 2013 por el método de las cadenas de Markov serán más precisas para cada cesta de consumo en las previsiones de los pesos para los alimentos para la zona euro y para las de los pesos de los servicios para Rumanía.

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Relative accuracy of analysts’ earnings forecasts over time: a Markov chain analysis

Cited by 14 publications

References 31 publications

Temporal changes in the value relevance of analysts' forecasts

Temporal changes in the value relevance of analysts' forecasts

Analysts issuing forecasts on weekends

exactitud de las predicciones para la estructura de cesta del consumo: un análisis comparativo entre la zona euro y Rumanía

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