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Forecast Accuracy (forecast + accuracy)
Kinds of Forecast Accuracy Selected AbstractsIMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS,INTERNATIONAL ECONOMIC REVIEW, Issue 2 2009Todd E. Clark This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias,variance trade-off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width. [source] Disclosure Practices, Enforcement of Accounting Standards, and Analysts' Forecast Accuracy: An International StudyJOURNAL OF ACCOUNTING RESEARCH, Issue 2 2003Ole-Kristian Hope Using a sample from 22 countries, I investigate the relations between the accuracy of analysts' earnings forecasts and the level of annual report disclosure, and between forecast accuracy and the degree of enforcement of accounting standards. I document that firm-level disclosures are positively related to forecast accuracy, suggesting that such disclosures provide useful information to analysts. I construct a comprehensive measure of enforcement and find that strong enforcement is associated with higher forecast accuracy. This finding is consistent with the hypothesis that enforcement encourages managers to follow prescribed accounting rules, which, in turn, reduces analysts' uncertainty about future earnings. I also find evidence consistent with disclosures being more important when analyst following is low and with enforcement being more important when more choice among accounting methods is allowed. [source] Does the Capitalization of Development Costs Improve Analyst Forecast Accuracy?JOURNAL OF INTERNATIONAL FINANCIAL MANAGEMENT & ACCOUNTING, Issue 1 2010Evidence from the UK It has been documented that investments in Research and Development (R&D) are associated with increased errors and inaccuracy in earnings forecasts made by financial analysts. These deficiencies have been generally attributed to information complexity and the uncertainty of the future benefits of R&D. This paper examines whether the capitalization of development costs can reduce analyst uncertainty about the future economic outcome of R&D investments, provide outsiders with a better matching of future R&D-related revenues and costs, and therefore promote accuracy in analyst forecasts. UK data is used, because accounting rules in the United Kingdom permitted firms to conditionally capitalize development costs even before the introduction of the International Financial Reporting Standards. The choice to expense R&D rather than conditionally capitalize development costs is found to relate positively to signed analyst forecast errors. This finding is robust to controlling for the influence of other factors that may affect errors, as well as for the influence of R&D investments on forecast errors. The decision to capitalize versus expense is not observed to have a significant influence on analyst forecast revisions. The findings are interpreted as evidence that the choice to capitalize as opposed to expense may help to reduce deficiencies in analyst forecasts; hence, is informative for users of financial statements. Increased informativeness is expected to have repercussions for the effectiveness with which analysts produce earnings forecasts, and, as a result, market efficiency. [source] The Persistence and Forecast Accuracy of Earnings Components in the USA and JapanJOURNAL OF INTERNATIONAL FINANCIAL MANAGEMENT & ACCOUNTING, Issue 1 2000Don Herrmann Not all components of earnings are expected to provide similar information regarding future earnings. For example, basic financial statement analysis indicates that the persistence of ordinary income should be greater than the persistence of special, extraordinary, or discontinued operations. Because the market assigns higher multiples to earnings components that are more persistent, differentiating earnings components on the basis of relative persistence would appear to be useful. A focus on relative predictive value is consistent with research findings and user recommendations on separating earnings components that are persistent or permanent from those that are transitory or temporary. This paper examines the persistence and forecast accuracy of earnings components for retail and manufacturing companies listed in the world's two largest equity markets; the USA and Japan. We find the forecast accuracy of earnings in both the USA and Japan increases with greater disaggregation of earnings components. The results further indicate that the improvements in forecast accuracy due to earnings disaggregation are greater in the USA than in Japan. The greater emphasis and more detailed guidelines for reporting earnings components in the USA produce a better differentiation in the persistence of earnings components resulting in greater forecast improvements from earnings disaggregation. [source] Forecast accuracy and economic gains from Bayesian model averaging using time-varying weightsJOURNAL OF FORECASTING, Issue 1-2 2010Lennart Hoogerheide Abstract Several Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time-varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time series. The results indicate that the proposed time-varying model weight schemes outperform other combination schemes in terms of predictive and economic gains. In an empirical application using returns on the S&P 500 index, time-varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs. Another empirical example refers to forecasting US economic growth over the business cycle. It suggests that time-varying combination schemes may be very useful in business cycle analysis and forecasting, as these may provide an early indicator for recessions. Copyright © 2009 John Wiley & Sons, Ltd. [source] Meeting Real,Time Traffic Flow Forecasting Requirements with Imprecise ComputationsCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 3 2003Brian L. Smith This article explores the ability of imprecise computations to address real,time computational requirements in infrastructure control and management systems. The research in this area focuses on the development of nonparametric regression as a means to forecast traffic flow rates for transportation management systems. Nonparametric regression is a forecasting technique based on nearest neighbor searching, in which forecasts are derived from past observations that are similar to current conditions. A key concern regarding nonparametric regression is the significant time required to search for nearest neighbors in large databases. The results presented in this article indicate that approximate nearest neighbors, which are imprecise computations as applied to nonparametric regression, may be used to adequately speed the execution time of nonparametric regression, with acceptable degradations in forecast accuracy. The article concludes with a demonstration of the use of genetic algorithms as a design aid for real,time algorithms employing imprecise computations. [source] Auditor Quality and the Accuracy of Management Earnings Forecasts,CONTEMPORARY ACCOUNTING RESEARCH, Issue 4 2000PETER M. CLARKSON Abstract In this study, we appeal to insights and results from Davidson and Neu 1993 and McConomy 1998 to motivate empirical analyses designed to gain a better understanding of the relationship between auditor quality and forecast accuracy. We extend and refine Davidson and Neu's analysis of this relationship by introducing additional controls for business risk and by considering data from two distinct time periods: one in which the audit firm's responsibility respecting the earnings forecast was to provide review-level assurance, and one in which its responsibility was to provide audit-level assurance. Our sample data consist of Toronto Stock Exchange (TSE) initial public offerings (IPOs). The earnings forecast we consider is the one-year-ahead management earnings forecast included in the IPO offering prospectus. The results suggest that after the additional controls for business risk are introduced, the relationship between forecast accuracy and auditor quality for the review-level assurance period is no longer significant. The results also indicate that the shift in regimes alters the fundamental nature of the relationship. Using data from the audit-level assurance regime, we find a negative and significant relationship between forecast accuracy and auditor quality (i.e., we find Big 6 auditors to be associated with smaller absolute forecast errors than non-Big 6 auditors), and further, that the difference in the relationship between the two regimes is statistically significant. [source] Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative MethodsDECISION SCIENCES, Issue 2 2004Wei Zhang ABSTRACT In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate-linear, multivariate-linear, univariate-neural network, and multivariate-neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models. [source] Improved Estimates of Correlation Coefficients and their Impact on Optimum PortfoliosEUROPEAN FINANCIAL MANAGEMENT, Issue 3 2006Edwin J. Elton G11 Abstract To implement mean variance analysis one needs a technique for forecasting correlation coefficients. In this article we investigate the ability of several techniques to forecast correlation coefficients between securities. We find that separately forecasting the average level of pair-wise correlations and individual pair-wise differences from the average improves forecasting accuracy. Furthermore, forming homogenous groups of firms on the basis of industry membership or firm attributes (e.g. size) improves forecast accuracy. Accuracy is evaluated in two ways: First, in terms of the error in estimating future correlation coefficients. Second, in the characteristics of portfolios formed on the basis of each forecasting technique. The ranking of forecasting techniques is robust across both methods of evaluation and the better techniques outperform prior suggestions in the literature of financial economics. [source] An Examination of the Differential Impact of Regulation FD on Analysts' Forecast AccuracyFINANCIAL REVIEW, Issue 1 2006Scott Findlay G14; G18; G24; G38 Abstract Regulation fair disclosure (FD) requires companies to publicly disseminate information, effectively preventing the selective pre-earnings announcement guidance to analysts common in the past. We investigate the effects of Regulation FD's reducing information disparity across analysts on their forecast accuracy. Proxies for private information, including brokerage size and analyst company-specific experience, lose their explanatory power for analysts' relative accuracy after Regulation FD. Analyst forecast accuracy declines overall, but analysts that are relatively less accurate (more accurate) before Regulation FD improve (deteriorate) after implementation. Our findings are consistent with selective guidance partially explaining variation in the forecasting accuracy of analysts before Regulation FD. [source] Does corporate governance transparency affect the accuracy of analyst forecasts?ACCOUNTING & FINANCE, Issue 5 2006Gauri Bhat M4; O1 Abstract Using country-level proxies for corporate governance transparency, this paper investigates how differences in transparency across 21 countries affect the average forecast accuracy of analysts for the country's firms. The association between financial transparency and analyst forecast accuracy has been well documented in previous published literature; however, the association between governance transparency and analyst forecast accuracy remains unexplored. Using the two distinct country-level factors isolated by Bushman et al. (2004), governance transparency and financial transparency, we investigate whether corporate governance information impacts on the accuracy of earnings forecasts over and above financial information. We document that governance transparency is positively associated with analyst forecast accuracy after controlling for financial transparency and other variables. Furthermore, our results suggest that governance-related disclosure plays a bigger role in improving the information environment when financial disclosures are less transparent. Our empirical evidence also suggests that the significance of governance transparency on analyst forecast accuracy is higher when legal enforcement is weak. [source] Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selectionHYDROLOGICAL PROCESSES, Issue 8 2001Paulin Coulibaly Abstract The issue of selecting appropriate model input parameters is addressed using a peak and low flow criterion (PLC). The optimal artificial neural network (ANN) models selected using the PLC significantly outperform those identified with the classical root-mean-square error (RMSE) or the conventional Nash,Sutcliffe coefficient (NSC) statistics. The comparative forecast results indicate that the PLC can help to design an appropriate ANN model to improve extreme hydrologic events (peak and low flow) forecast accuracy. Copyright © 2001 John Wiley & Sons, Ltd. [source] IMPROVING FORECAST ACCURACY BY COMBINING RECURSIVE AND ROLLING FORECASTS,INTERNATIONAL ECONOMIC REVIEW, Issue 2 2009Todd E. Clark This article presents analytical, Monte Carlo, and empirical evidence on combining recursive and rolling forecasts when linear predictive models are subject to structural change. Using a characterization of the bias,variance trade-off faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two, we derive optimal observation windows and combining weights designed to minimize mean square forecast error. Monte Carlo experiments and several empirical examples indicate that combination can often provide improvements in forecast accuracy relative to forecasts made using the recursive scheme or the rolling scheme with a fixed window width. [source] Disclosure Practices, Enforcement of Accounting Standards, and Analysts' Forecast Accuracy: An International StudyJOURNAL OF ACCOUNTING RESEARCH, Issue 2 2003Ole-Kristian Hope Using a sample from 22 countries, I investigate the relations between the accuracy of analysts' earnings forecasts and the level of annual report disclosure, and between forecast accuracy and the degree of enforcement of accounting standards. I document that firm-level disclosures are positively related to forecast accuracy, suggesting that such disclosures provide useful information to analysts. I construct a comprehensive measure of enforcement and find that strong enforcement is associated with higher forecast accuracy. This finding is consistent with the hypothesis that enforcement encourages managers to follow prescribed accounting rules, which, in turn, reduces analysts' uncertainty about future earnings. I also find evidence consistent with disclosures being more important when analyst following is low and with enforcement being more important when more choice among accounting methods is allowed. [source] Domestic Accounting Standards, International Accounting Standards, and the Predictability of EarningsJOURNAL OF ACCOUNTING RESEARCH, Issue 3 2001Hollis Ashbaugh We investigate (1) whether the variation in accounting standards across national boundaries relative to International Accounting Standards (IAS) has an impact on the ability of financial analysts to forecast non-U.S. firms' earnings accurately, and (2) whether analyst forecast accuracy changes after firms adopt IAS. IAS are a set of financial reporting policies that typically require increased disclosure and restrict management's choices of measurement methods relative to the accounting standards of our sample firms' countries of domicile. We develop indexes of differences in countries' accounting disclosure and measurement policies relative to IAS, and document that greater differences in accounting standards relative to IAS are significantly and positively associated with the absolute value of analyst earnings forecast errors. Further, we show that analyst forecast accuracy improves after firms adopt IAS. More specifically, after controlling for changes in the market value of equity, changes in analyst following, and changes in the number of news reports, we find that the convergence in firms' accounting policies brought about by adopting IAS is positively associated with the reduction in analyst forecast errors. [source] A Temporal Analysis of Earnings Surprises: Profits versus LossesJOURNAL OF ACCOUNTING RESEARCH, Issue 2 2001Lawrence D. Brown I show that median earnings surprise has shifted rightward from small negative (miss analyst estimates by a small amount) to zero (meet analyst estimates exactly) to small positive (beat analyst estimates by a small amount) during the 16 years, 1984 to 1999. I show that a rightward temporal shift in median surprise from negative to positive describes earnings, but neither profits nor losses. Median profit surprise shifts within the positive quadrant, from zero to one cent per share. Median loss surprise shifts within the negative quadrant from extreme negative (about -33 cents per share) to zero. I show that the median surprise for profits exceeds that for losses in every year. I document significant positive temporal trends in both meet and beat analyst estimates for both profits and losses, but I find a greater frequency of profits that either meet or beat analyst estimates in every year. I find a significant positive temporal trend in positive profits that are "a little bit of good news," and a significant negative temporal trend in managers who report losses that are an "extreme amount of bad news." My results are robust to the four internal validity threats I consider,namely temporal changes in: (1) analyst forecast accuracy, (2) the mix of earnings of one sign preceded by earnings of another sign four quarters ago, (3) the timeliness of the most recent analyst forecast, and (4) the I/B/E/S definition of actual earnings. I find that managers of growth firms are relatively more likely than managers of value firms to report good news profits. I show that when they do report positive profit surprises, managers of growth firms are more likely to report "a little bit of good news" in every year. [source] The relative influence of advice from human experts and statistical methods on forecast adjustmentsJOURNAL OF BEHAVIORAL DECISION MAKING, Issue 4 2009Dilek Önkal Abstract Decision makers and forecasters often receive advice from different sources including human experts and statistical methods. This research examines, in the context of stock price forecasting, how the apparent source of the advice affects the attention that is paid to it when the mode of delivery of the advice is identical for both sources. In Study 1, two groups of participants were given the same advised point and interval forecasts. One group was told that these were the advice of a human expert and the other that they were generated by a statistical forecasting method. The participants were then asked to adjust forecasts they had previously made in light of this advice. While in both cases the advice led to improved point forecast accuracy and better calibration of the prediction intervals, the advice which apparently emanated from a statistical method was discounted much more severely. In Study 2, participants were provided with advice from two sources. When the participants were told that both sources were either human experts or both were statistical methods, the apparent statistical-based advice had the same influence on the adjusted estimates as the advice that appeared to come from a human expert. However when the apparent sources of advice were different, much greater attention was paid to the advice that apparently came from a human expert. Theories of advice utilization are used to identify why the advice of a human expert is likely to be preferred to advice from a statistical method. Copyright © 2009 John Wiley & Sons, Ltd. [source] A Re,examination of the Effectiveness of the Bankruptcy ProcessJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 9-10 2002Harlan D. Platt As an increasing number of companies go bankrupt, society grows concerned with the process's efficacy. In contrast to previous research, we find that relatively healthy companies emerge from bankruptcy as evidenced by their operating and equity performance post bankruptcy. While we find a substantial degree of variation in the forecast accuracy of sales, EBIT and net income, we find that forecast errors are not statistically significant and are smaller than had been thought. We provide evidence to support the argument that the economy's health affects operating and equity outcomes post bankruptcy. [source] The Gilt-Equity Yield Ratio and the Predictability of UK and US Equity ReturnsJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 3-4 2000Richard D.F. Harris A number of financial variables have been shown to be effective in explaining the time-series of aggregate equity returns in both the UK and the US. These include, inter alia, the equity dividend yield, the spread between the yields on long and short government bonds, and the lagged equity return. Recently, however, the ratio between the long government bond yield and the equity dividend yield , the gilt-equity yield ratio , has emerged as a variable that has considerable explanatory power for UK equity returns. This paper compares the predictive ability of the gilt-equity yield ratio with these other variables for UK and US equity returns, providing evidence on both in-sample and out-of-sample performance. For UK monthly returns, it is shown that while the dividend yield has substantial in-sample explanatory power, this is not matched by out-of sample forecast accuracy. The gilt-equity yield ratio, in contrast, performs well both in-sample and out-of-sample. Although the predictability of US monthly equity returns is much lower than for the UK, a similar result emerges, with the gilt-equity yield ratio dominating the other variables in terms of both in-sample explanatory power and out-of-sample forecast performance. The gilt-equity yield ratio is also shown to have substantial predictive ability for long horizon returns. [source] Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipstersJOURNAL OF FORECASTING, Issue 1 2009Martin Spann Abstract This article compares the forecast accuracy of different methods, namely prediction markets, tipsters and betting odds, and assesses the ability of prediction markets and tipsters to generate profits systematically in a betting market. We present the results of an empirical study that uses data from 678,837 games of three seasons of the German premier soccer league. Prediction markets and betting odds perform equally well in terms of forecasting accuracy, but both methods strongly outperform tipsters. A weighting-based combination of the forecasts of these methods leads to a slightly higher forecast accuracy, whereas a rule-based combination improves forecast accuracy substantially. However, none of the forecasts leads to systematic monetary gains in betting markets because of the high fees (25%) charged by the state-owned bookmaker in Germany. Lower fees (e.g., approximately 12% or 0%) would provide systematic profits if punters exploited the information from prediction markets and bet only on a selected number of games. Copyright © 2008 John Wiley & Sons, Ltd. [source] The predictive value of temporally disaggregated volatility: evidence from index futures marketsJOURNAL OF FORECASTING, Issue 8 2008Nicholas Taylor Abstract This paper examines the benefits to forecasters of decomposing close-to-close return volatility into close-to-open (nighttime) and open-to-close (daytime) return volatility. Specifically, we consider whether close-to-close volatility forecasts based on the former type of (temporally aggregated) data are less accurate than corresponding forecasts based on the latter (temporally disaggregated) data. Results obtained from seven different US index futures markets reveal that significant increases in forecast accuracy are possible when using temporally disaggregated volatility data. This result is primarily driven by the fact that forecasts based on such data can be updated as more information becomes available (e.g., information flow from the preceding close-to-open/nighttime trading session). Finally, we demonstrate that the main findings of this paper are robust to the index futures market considered, the way in which return volatility is constructed, and the method used to assess forecast accuracy. Copyright © 2008 John Wiley & Sons, Ltd. [source] Power transformation models and volatility forecastingJOURNAL OF FORECASTING, Issue 7 2008Perry Sadorsky Abstract This paper considers the forecast accuracy of a wide range of volatility models, with particular emphasis on the use of power transformations. Where one-period-ahead forecasts are considered, the power autoregressive models are ranked first by a range of error metrics. Over longer forecast horizons, however, generalized autoregressive conditional heteroscedasticity models are preferred. A value-at-risk-based forecast assessment indicates that, while the forecast errors are independent, they are not independent and identically distributed, although this latter result is sensitive to the choice of forecast horizon. Our results are robust across a number of different asset markets. Copyright © 2008 John Wiley & Sons, Ltd. [source] Forecasting the price of crude oil via convenience yield predictionsJOURNAL OF FORECASTING, Issue 7 2007Thomas A. KnetschArticle first published online: 14 NOV 200 Abstract The paper develops an oil price forecasting technique which is based on the present value model of rational commodity pricing. The approach suggests shifting the forecasting problem to the marginal convenience yield, which can be derived from the cost-of-carry relationship. In a recursive out-of-sample analysis, forecast accuracy at horizons within one year is checked by the root mean squared error as well as the mean error and the frequency of a correct direction-of-change prediction. For all criteria employed, the proposed forecasting tool outperforms the approach of using futures prices as direct predictors of future spot prices. Vis-ŕ-vis the random-walk model, it does not significantly improve forecast accuracy but provides valuable statements on the direction of change. Copyright © 2007 John Wiley & Sons, Ltd. [source] Building neural network models for time series: a statistical approachJOURNAL OF FORECASTING, Issue 1 2006Marcelo C. Medeiros Abstract This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using simple existing techniques. The problem of selecting the number of hidden units is solved by sequentially applying Lagrange multiplier type tests, with the aim of avoiding the estimation of unidentified models. Misspecification tests are derived for evaluating an estimated neural network model. All the tests are entirely based on auxiliary regressions and are easily implemented. A small-sample simulation experiment is carried out to show how the proposed modelling strategy works and how the misspecification tests behave in small samples. Two applications to real time series, one univariate and the other multivariate, are considered as well. Sets of one-step-ahead forecasts are constructed and forecast accuracy is compared with that of other nonlinear models applied to the same series. Copyright © 2006 John Wiley & Sons, Ltd. [source] Factor forecasts for the UKJOURNAL OF FORECASTING, Issue 4 2005Michael J. Artis Abstract Data are now readily available for a very large number of macroeconomic variables that are potentially useful when forecasting. We argue that recent developments in the theory of dynamic factor models enable such large data sets to be summarized by relatively few estimated factors, which can then be used to improve forecast accuracy. In this paper we construct a large macroeconomic data set for the UK, with about 80 variables, model it using a dynamic factor model, and compare the resulting forecasts with those from a set of standard time-series models. We find that just six factors are sufficient to explain 50% of the variability of all the variables in the data set. These factors, which can be shown to be related to key variables in the economy, and their use leads to considerable improvements upon standard time-series benchmarks in terms of forecasting performance. Copyright © 2005 John Wiley & Sons, Ltd. [source] Forecasting new product trial in a controlled test market environmentJOURNAL OF FORECASTING, Issue 5 2003Peter S. Fader Abstract A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e. shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model ,components' on forecasting performance. Furthermore, we also explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data.,Copyright © 2003 John Wiley & Sons, Ltd. [source] The homogeneity restriction and forecasting performance of VAR-type demand systems: an empirical examination of US meat consumptionJOURNAL OF FORECASTING, Issue 3 2002Zijun Wang Abstract This paper compares the forecast performance of vector-autoregression-type (VAR) demand systems with and without imposing the homogeneity restriction in the cointegration space. US meat consumption (beef, poultry and pork) data are studied. One up to four-steps-ahead forecasts are generated from both the theoretically restricted and unrestricted models. A modified Diebold,Mariano test of the equality of mean squared forecast errors (MSFE) and a forecast encompassing test are applied in forecast evaluation. Our findings suggest that the imposition of the homogeneity restriction tends to improve the forecast accuracy when the restriction is not rejected. The evidence is mixed when the restriction is rejected. Copyright © 2002 John Wiley & Sons, Ltd. [source] The Persistence and Forecast Accuracy of Earnings Components in the USA and JapanJOURNAL OF INTERNATIONAL FINANCIAL MANAGEMENT & ACCOUNTING, Issue 1 2000Don Herrmann Not all components of earnings are expected to provide similar information regarding future earnings. For example, basic financial statement analysis indicates that the persistence of ordinary income should be greater than the persistence of special, extraordinary, or discontinued operations. Because the market assigns higher multiples to earnings components that are more persistent, differentiating earnings components on the basis of relative persistence would appear to be useful. A focus on relative predictive value is consistent with research findings and user recommendations on separating earnings components that are persistent or permanent from those that are transitory or temporary. This paper examines the persistence and forecast accuracy of earnings components for retail and manufacturing companies listed in the world's two largest equity markets; the USA and Japan. We find the forecast accuracy of earnings in both the USA and Japan increases with greater disaggregation of earnings components. The results further indicate that the improvements in forecast accuracy due to earnings disaggregation are greater in the USA than in Japan. The greater emphasis and more detailed guidelines for reporting earnings components in the USA produce a better differentiation in the persistence of earnings components resulting in greater forecast improvements from earnings disaggregation. [source] Models to improve winter minimum surface temperature forecasts, Delhi, IndiaMETEOROLOGICAL APPLICATIONS, Issue 2 2004A. P. Dimri Accurate forecasts of minimum surface temperature during winter help in the prediction of cold-wave conditions over northwest India. Statistical models for forecasting the minimum surface temperature at Delhi during winter (December, January and February) are developed by using the classical method and the perfect prognostic method (PPM), and the results are compared. Surface and upper air data are used for the classical method, whereas for PPM additional reanalysis data from the National Center of Environmental Prediction (NCEP) US are incorporated in the model development. Minimum surface temperature forecast models are developed by using data for the winter period 1985,89. The models are validated using an independent dataset (winter 1994,96). It is seen that by applying PPM, rather than the classical method, the model's forecast accuracy is improved by about 10% (correct to within ± 2 °C). Copyright © 2004 Royal Meteorological Society. [source] An exploratory Investigation of new product forecasting practicesTHE JOURNAL OF PRODUCT INNOVATION MANAGEMENT, Issue 2 2002Kenneth B. Kahn To guide new product forecasting efforts, the following study offers preliminary data on new product forecasting practices during the commercialization stage (prelaunch and launch stage). Data on department responsibility for and involvement in the new product forecasting process, technique usage, forecast accuracy, and forecast time horizon across different types of new products are reported. Comparisons of new product forecasting practices for consumer firms versus industrial firms are also reported. Overall, study results show that the marketing department is predominantly responsible for the new product forecasting effort, there is a preference to employ judgmental forecasting techniques, forecast accuracy is 58% on average across the different types of new products, and two to four forecasting techniques are typically employed during the new product forecasting effort. Compared to consumer firms, industrial firms appear to have longer forecast time horizons and rely more on the sales force for new product forecasting. Additional analyses show that there does not appear to be a general relationship between a particular department's involvement and higher forecast accuracy or greater satisfaction, nor does it appear that use of a particular technique relates to higher forecast accuracy and greater satisfaction. Countering previous research findings, the number of forecasting techniques employed also does not appear to correlate to higher forecasting accuracy or greater satisfaction. Managerial and research implications are discussed. [source] | ||||||||||||||||||||||||||||