Structural changes and the forecasting of quarterly accounting earnings in the utility industry

Lee, Chi-Wen Jevons; Chen, Chung

doi:10.1016/0165-4101(90)90026-z

Cited by 15 publications

(4 citation statements)

References 37 publications

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“…The superiority of analyst forecasts is largely attributed to the fact that, unlike univariate ARIMA models, analysts employ a variety of inputs and can cope comfortably with structural change. Lee & Chen (1990), however, proposed a technique for dealing with structural change in ARIMA models of quarterly EPS and found that the application of their technique reduced the advantage of analyst forecasts over time series models to an insignificant level.…”

Section: Quarterly Earnings Studiesmentioning

confidence: 96%

The Univariate Time Series Modelling of Earnings: A Review

O’Hanlon

1995

The British Accounting Review

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Section: Quarterly Earnings Studiesmentioning

confidence: 96%

The Univariate Time Series Modelling of Earnings: A Review

O’Hanlon

1995

The British Accounting Review

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“…Mechanical models, although easy to implement, have unfortunately never been able to consistently beat analysts' forecasts, of which Value Line (VL) is an example. However, Lee and Chen (1990) show that both Value Line and ARIMA models have marginal earnings forecasting power. Thus, a combination of them might prove better than each individual forecast.…”

Section: Introductionmentioning

confidence: 95%

“…Evidence has been presented in favor of all three models. Their forecasting accuracy, though not as good as Value Line's, remained unbeaten until Lee and Chen (1990) introduced models incorporating structural change information. However, the Lee and Chen models were not able to make better forecasts than Value Line.…”

Section: Quarterly Earnings Forecasting and Forecast-combining Researchmentioning

confidence: 99%

“…Mechanical models include ARIMA models introduced by Foster (1977), Watts (1975)-Griffin (1977, Brown and Rozeff (1979) and individually identified models. Lee and Chen (1990) introduce models that account for structural change in a firm's quarterly earnings series. Mechanical models, although easy to implement, have unfortunately never been able to consistently beat analysts' forecasts, of which Value Line (VL) is an example.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming

Rodríguez

Trigueros

2020

BAS-UCHILE

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In this study we examine different methodologies to estimate earnings. More specifically, we evaluate the viability of Genetic Programming as both a forecasting model estimator and a forecast-combining methodology. When we compare the performance of traditional mechanical forecasting (ARIMA) models and models developed using Genetic Programming we observe that Genetic Programming can be used to create time-series models for quarterly earnings as accurate as the traditional linear models. Genetic Programming can also effectively combine forecasts. However, Genetic Programming's forecast combinations are sometimes unable to improve on Value Line. Moreover, simple averaging of forecasts results in better predictive accuracy than Genetic Programming-combining of forecasts. Hence, as implemented in this study, Genetic Programming is not superior to traditional methodologies in either forecasting or forecast combining of quarterly earnings.

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A nonlinear dynamic approach to cash flow forecasting

Yang

Shi

et al. 2022

Rev Quant Finan Acc

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We propose a novel grey-box model to capture the nonlinearity and the dynamics of cash flow model parameters. The grey-box model retains a simple white-box model structure, while their parameters are modelled as a black-box with a Padé approximant as a functional form. The growth rate of sales and firm age are used as exogenous variables because they are considered to have explanatory power for the parameter process. Panel data estimation methods are applied to investigate whether they outperform the pooled regression, which is widely used in the extant literature. We use the U.S. dataset to evaluate the performance of various models in predicting cash flow. Two performance measures are selected to compare the out-of-sample predictive power of the models. The results suggest that the proposed grey-box model can offer superior performance, especially in multi-period-ahead predictions.

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Structural changes and the forecasting of quarterly accounting earnings in the utility industry

Cited by 15 publications

References 37 publications

The Univariate Time Series Modelling of Earnings: A Review

The Univariate Time Series Modelling of Earnings: A Review

Forecasting and forecast-combining of quarterly earnings-per-share via genetic programming

A nonlinear dynamic approach to cash flow forecasting

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