Diversifying Earnings Forecast Errors via Composites of Market-Based, Analyst and Time-Series Predictions

1999 ◽  
pp. 257-284
Author(s):  
Pieter T. Elgers ◽  
May H. Lo ◽  
Dennis Murray
Keyword(s):  
Time Series ◽  
Earnings Forecast ◽  
Forecast Errors

The Association between Unsystematic Security Returns and the Magnitude of Earnings Forecast Errors

1979 ◽  
Vol 17 (2) ◽  
pp. 316 ◽  
Author(s):  
William H. Beaver ◽  
Roger Clarke ◽  
William F. Wright
Keyword(s):  
Earnings Forecast ◽  
Forecast Errors ◽  
Security Returns

Evaluating Forecasting Procedures for Predicting Pacific Herring (Clupea harengus pallasi) Recruitment in British Columbia

1988 ◽  
Vol 45 (6) ◽  
pp. 928-935 ◽  
Author(s):  
M. Stocker ◽  
D. J. Noakes
Keyword(s):  
Time Series ◽  
Clupea Harengus ◽  
Environmental Data ◽  
Forecast Errors ◽  
Pacific Herring ◽  
Time Step ◽  
Absolute Deviation ◽  
Percent Error ◽  
One Step ◽  
Absolute Percent Error

The ability of four forecasting methods to generate one-step-ahead forecasts of Pacific herring (Clupea harengus pillasi) recruitment is considered in this paper. Recruitment time series for five coastal stocks and various environmental time series are employed in the analyses. Information up to and including time t is employed to estimate the parameters of each model used to forecast recruitment in year t + 1. Parameter estimates are then updated after each time step with a total of seven one-step-ahead forecasts being generated by each model for each stock. The forecast errors are compared using the five criteria: (1) root mean squared error, (2) mean absolute deviation, (3) mean absolute percent error, (4) median absolute deviation, and (5) median absolute percent error. The results of the study indicate that time series models may provide better forecasts of recruitment for the Strait of Georgia/Johnstone Strait stocks than the other competing procedures. A Ricker stock–recruitment model that takes into account environmental data appears to produce marginally better forecasts for the Central Coast and Queen Charlotte Island stocks, while all models produced equally good/bad forecasts for the Prince Rupert district stocks.

Modeling of Wind Power Production Forecast Errors for Wind Integration Studies

2010 ◽  
Author(s):  
Jason J. Kemper ◽  
Mark F. Bielecki ◽  
Thomas L. Acker
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Power Plants ◽  
Moving Average ◽  
Forecast Errors ◽  
Production Forecast ◽  
Wind Integration ◽  
Forecast Performance ◽  
Auto Regressive ◽  
Statistical Trends

In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating “synthetic” wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performance, and both synthetic forecast methods proposed are evaluated quantitatively and qualitatively. The forecast performance is then compared with a commercial forecast used for an operational wind power plant in the Northwestern United States evaluated using the same statistical performance measures. These quantitative evaluation parameters are monitored during specific months of the year, during rapid ramping events, and at all times. The best ARMA based models failed to replicate the auto-regressive decay of forecast errors associated with commercial forecasts. A modification to the Markov method, consisting of adding a dimension to the state transition array, allowed the forecast time series to depend on multiple inputs. This improvement lowered the artificial variability in the original time series. The overall performance of this method was better than for the ARMA based models, and provides a suitable technique for use in creating a synthetic wind forecast for a wind integration study.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

Regulation FD and the Financial Information Environment: Early Evidence

2003 ◽  
Vol 78 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Frank Heflin ◽  
K. R. Subramanyam ◽  
Yuan Zhang
Keyword(s):  
Stock Prices ◽  
Earnings Announcements ◽  
Relevant Information ◽  
Financial Information ◽  
Earnings Forecast ◽  
Forecast Errors ◽  
Informational Efficiency ◽  
The Public ◽  
Fair Disclosure ◽  
Regulation Fd

On October 23, 2000, the SEC implemented Regulation FD (Fair Disclosure), which prohibits firms from privately disclosing value-relevant information to select securities markets professionals without simultaneously disclosing the same information to the public. We examine whether Regulation FD's prohibition of selective disclosure impairs the flow of financial information to the capital markets prior to earnings announcements. After implementation of FD, we find (1) improved informational efficiency of stock prices prior to earnings announcements, as evidenced by smaller deviations between pre-and post-announcement stock prices; (2) no reliable evidence of change in analysts' earnings forecast errors or dispersion; and (3) a substantial increase in the volume of firms' voluntary, forward-looking, earnings-related disclosures. Overall, we find no evidence Regulation FD impaired the information available to investors prior to earnings announcements, and some of our evidence is consistent with improvement.

Demand forecasting in the beauty industry using fuzzy inference systems

2020 ◽  
Vol 15 (4) ◽  
pp. 1389-1417
Author(s):  
Ricardo Felicio Souza ◽  
Peter Wanke ◽  
Henrique Correa
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Theory And Practice ◽  
Forecast Errors ◽  
Fuzzy Inference Systems ◽  
Inference System ◽  
Content Type ◽  
Forecasting Methods ◽  
Inference Systems

Purpose This study aims to analyze the performance of four different fuzzy inference system-based forecasting tools using a real case company. Design/methodology/approach The forecasting tools were tested using 27 products of the nail polish line of a multinational beauty company and the performance of said tools was compared to those of the company’s previous forecasting methods that were basically qualitative (informal and intuition-based). Findings The performance of the methods analyzed was compared by using mean absolute percentage error. It was possible to determine the characteristics and conditions that make each model the best for each situation. The main takeaways were that low kurtosis, negatively skewed demand time-series and longer horizon forecasts that favor the fuzzy inference system-based models. Besides, the results suggest that the fuzzy forecasting tools should be preferred for longer horizon forecasts over informal qualitative methods. Originality/value Notwithstanding the proposed hybrid modeling approach based on fuzzy inference systems, our research offers a relevant contribution to theory and practice by shedding light on the segmentation and selection of forecasting models, both in terms of time-series characteristics and forecasting horizon. The proposed fuzzy inference systems showed to be particularly useful not only when time-series distributions present no clear central tendency (that is, they are platykurtic or dispersed around a large plateau around the median, which is the characteristic of negative kurtosis), but also when mode values are greater than median values, which in turn are greater than mean values. This large tail to the left (negative skewness) is typical of successful products whose sales are ramping up in early stages of their life cycle. For these, fuzzy inference systems may help managers screen out forecast bias and, therefore, lower forecast errors. This behavior also occurs when managers deal with forecasts of longer horizons. The results suggest that further research on fuzzy inference systems hybrid approaches for forecasting should emphasize short-term forecasting by trying to better capture the “tribal” managerial knowledge instead of focusing on less dispersed and slower moving products, where the purely qualitative forecasting methods used by managers tend to perform better in terms of their accuracy.

scholarly journals A note on analysts’ earnings forecast errors distribution

2003 ◽  
Vol 36 (1-3) ◽  
pp. 147-164 ◽  
Author(s):  
Daniel A Cohen ◽  
Thomas Z Lys
Keyword(s):  
Earnings Forecast ◽  
Forecast Errors

scholarly journals Modelling moving feasts determined by the Islamic calendar

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Michel Grun Rehomme ◽  
Amani Ben Rejeb
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Information Criterion ◽  
Interval Length ◽  
Forecast Errors ◽  
Gregorian Calendar ◽  
Production And Consumption ◽  
Comparison Results ◽  
Final Adjustment ◽  
Macroeconomic Time Series

National and religious events always influence economic activity. Islamic events also influence production and consumption. Moreover, these Islamic feasts move over time, depending on the Hegirian calendar, which is based on the lunar cycles, even though, some Islamic countries use officially the Gregorian calendar. The lunar calendar is shorter than the Gregorian calendar, which is based on the cycles of the Earth revolution. Consequently, every year the dates of religious events change in the official calendar creating moving events. Tunisia offers a good example of this phenomenon. Twelve relevant series are analysed and five feasts are considered in our work. Modelling the effect of moving holidays improves the quality of the final adjustment. Removing Islamic feasts from time series is crucial to have better forecasting and comparison results. We adopt an approach initially developed by Bell and Hillmer (1983) to analyse the Easter effect. Since the effect is not the same, we consider three regressors for before, during, and after the holiday for each feast. For model selection and determining the number of regressors and their interval length, two methods are used: the F-adjusted Akaike's information criterion and a criterion based on forecast errors. The empirical results confirm our model selection for all the macroeconomic time series considered except for the exports and the broad money which are not affected by the religious feasts.

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