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This study aims to examine several hypotheses, in conjunction with fundamental accounting concepts, to explain variations in the explanatory power of earnings for returns.

The authors explore three factors for their impact on the explanatory power of earnings. First, the accounting period preceding the earnings report is characterized by distinct intratemporal subperiod behavior. Recognizing this intratemporal nonstationarity is hypothesized to increase the explanatory power of earnings. Second, disaggregation of earnings into operating components is hypothesized to increase the explanatory power of earnings. Moreover, joint consideration of these first two factors is investigated. Third, the authors hypothesize that recognizing fundamental accounting concepts such as timeliness, predictive value, objectivity and verifiability offer key insights into the explanatory power of earnings.

The authors explore a sample of firms with management forecasts, which yields natural intratemporal subperiods – preforecast, forecast and realization periods – to generate hypotheses rooted in fundamental accounting concepts. The empirical evidence shows that recognition of nonstationary intratemporal behavior and earnings disaggregation yields a significant increase in the explanatory power of earning for returns. These findings are linked to fundamental concepts of accounting information.

This study is unique as it examines the joint role of nonstationarity and disaggregation in assessing the information conveyed in earnings. Importantly, results on these factors are linked to fundamental accounting concepts of timeliness, predictive value, objectivity and verifiability, along with their inherent trade-offs.

The purpose of this paper is to investigate the sources of the apparent episodic stationarity of the P/E ratio.

The Stock–Watson procedure is used to decompose a VAR/VMA model into changes in structure and changes volatility. In theory, if the P/E ratio is properly anticipated and shocks are random, according to Samuelson's proof, it should exhibit the characteristics of a pure martingale and therefore it should not be possible to statistically reject trend nonstationary.

Using a rolling window, the P/E ratio is shown to have episodic periods when trend nonstationarity could be rejected and that the P/E ratio was not properly anticipated. However, if there were changes in the structure of the underlying P/E ratio model or changes in the volatility of the underlying model, it suggests that the shocks impacting the P/E ratio would not be random and it might be possible to reject nonstationarity. This is investigated further with the objective of determining whether there was underlying structural change or volatility changes that are associated with these periods when trend nonstationarity in the P/E ratio could be rejected. The results are tested and found to be robust to a number of different specifications examined, including different data periods and frequencies.

Results findings should be tested in other countries and in other periods.

The paper developed a methodology whereby it is possible to detect periods there the P/E ratio is not properly anticipated.

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.

The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.

The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.

The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

Accurate forecast of tourist arrivals is crucial for Thailand since the tourism industry is a major economic factor of the country. However, a nonstationarity, normally consisted in nonlinear tourism time series can seriously ruin the forecasting computation. The purpose of this paper is to propose a hybrid forecasting method, namely discrete wavelet decomposition (DWD)-NARX, which combines DWD and the nonlinear autoregressive neural network with exogenous input (NARX) to cope with such nonstationarity, as a consequence, improve the effectiveness of the demand-side management activities.

According to DWD-NARX, wavelet decomposition is executed for efficiently extracting the hidden significant, temporal features contained in the nonstationary time series. Then, each extracted feature set at a particular resolution level along with a relative price as an exogenous input factor are fed into NARX for further forecasting. Finally, the forecasting results are reconstructed. Forecasting performance measures rely on mean absolute percentage error, mean absolute error as well as mean square error. Model overfitting avoidance is also considered.

The results indicate the superiority of the DWD-NARX over other efficient related neural forecasters in the cases of high forecasting performance rate as well as competently coping with model overfitting.

The scope of this study is confined to Thailand tourist arrivals forecast based on short-term projection. To resolve such limitations, future research should aim to apply the generalization capability of DWD-NARX on other domains of managerial time series forecast under long-term projection environment. However, the exogenous input factor is to be empirically revised on domain-by-domain basis.

Few works have been implemented either to handle the nonstationarity, consisted in nonlinear, unpredictable time series, or to achieve great success on finding an appropriate and effective exogenous forecasting input. This study applies DWD to attain efficient feature extraction; then, utilizes the competent forecaster, NARX. This would comprehensively and specifically deal with the nonstationarity difficulties at once. In addition, this study finds the effectiveness of simply using a relative price, generated based on six top-ranked original tourist countries as an exogenous forecasting input.

An accurate long-term multi-step forecast provides crucial basic information for planning and reinforcing managerial decision-support. However, nonstationarity and nonlinearity, normally consisted of several types of managerial data can seriously ruin the forecasting computation. This paper aims to propose an effective long-term multi-step forecasting conjunction model, namely, wavelet–nonlinear autoregressive neural network (WNAR) conjunction model. The WNAR combines discrete wavelet transform (DWT) and nonlinear autoregressive neural network (NAR) to cope with such nonstationarity and nonlinearity within the managerial data; as a consequence, provides insight information that enhances accuracy and reliability of long-term multi-step perspective, leading to effective management decision-making.

Based on WNAR conjunction model, wavelet decomposition is executed for efficiently extracting hidden significant, temporal features contained in each of six benchmark nonstationary data sets from different managerial domains. Then, each extracted feature set at a particular resolution level is fed into NAR for the further forecast. Finally, NAR forecasting results are reconstructed. Forecasting performance measures throughout 1 to 30-time lags rely on mean absolute percentage error (MAPE), root mean square error (RMSE), Nash-Sutcliffe efficiency index or the coefficient of efficiency (E_f) and Diebold–Mariano (DM) test. An effect of data characteristic in terms of autocorrelation on forecasting performances of each data set are observed.

Long-term multi-step forecasting results show the best accuracy and high-reliability performance of the proposed WNAR conjunction model over some other efficient forecasting models including a single NAR model. This is confirmed by DM test, especially for the short-forecasting horizon. In addition, rather steady, effective long-term multi-step forecasting performances are yielded with slight effect from time lag changes especially for the data sets having particular high autocorrelation, relative against 95 per cent degree of confidence normal distribution bounds.

The WNAR, which combines DWT with NAR can be accounted as a bridge for the gap between machine learning, engineering signal processing and management decision-support systems. Thus, WNAR is referred to as a forecasting tool that provides insight long-term information for managerial practices. However, in practice, suitable exogenous input forecast factors are required on the managerial domain-by-domain basis to correctly foresee and effectively prepare necessary reasonable management activities.

Few works have been implemented to handle the nonstationarity, consisted of nonlinear managerial data to attain high-accurate long-term multi-step forecast. Combining DWT and NAR capabilities would comprehensively and specifically deal with the nonstationarity and nonlinearity difficulties at once. In addition, it is found that the proposed WNAR yields rather steady, effective long-term multi-step forecasting performance throughout specific long time lags regarding the data, having certainly high autocorrelation levels across such long time lags.

The use of modeling and statistics for the design and development of pricing strategy is prevalent in academia as well as the industry. One of the more commonly used tools by researchers and managers alike for the estimation of linear demand models is the ordinary least squares (OLS) regression. Unfortunately, a majority of data sets to which such models are applied suffer from nonstationarity ‐ that is, the dependence of a variable on its prior values ‐ thereby violating the assumptions of a basic (naïve) regression model. Estimates of variables under these conditions are known commonly to be inflated and inaccurate. While this problem is well‐known and can be corrected for among statisticians and econometricians, a simple and effective tool has not yet been designed for managers ‐ the actual users of such models. Studies some of the problems encountered when using a naïve model and proposes a simple method to check for nonstationarity and redesign the model to account for the same. Using scanner data on soup, shows that the redesigned model predicts better, fits better and offers more meaningful results. Finally, looks at the implications of estimating such models for pricing strategies and issues. Surface response analysis shows how a manager can use such models for conducting insightful studies on price sensitivity.

The purpose of this paper is to suggest a superior method for assessing mean stationarity of asset pricing effects.

The authors suggest the use of an F‐test to examine mean stationarity of asset pricing effects across subperiods. The superiority of this test is demonstrated through examination of the Halloween Effect using simulated data and the Morgan Stanley Capital International (MSCI) data for 18 developed economies.

It is found that the suggested F‐test provides results superior to a simple examination of the magnitude and statistical significance of estimated regression coefficients across subperiods when attempting to determine mean stationarity.

This paper sheds light on an analytical oversight in the asset pricing anomalies literature and suggests an appropriate test to address this oversight.

This paper considers a class of parametric models with nonparametric autoregressive errors. A new test is established and studied to deal with the parametric specification of the nonparametric autoregressive errors with either stationarity or nonstationarity. Such a test procedure can initially avoid misspecification through the need to parametrically specify the form of the errors. In other words, we estimate the form of the errors and test for stationarity or nonstationarity simultaneously. We establish asymptotic distributions of the proposed test. Both the setting and the results differ from earlier work on testing for unit roots in parametric time series regression. We provide both simulated and real-data examples to show that the proposed nonparametric unit root test works in practice.