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Book part
Publication date: 17 November 2010

Joanne S. Utley and J. Gaylord May

This study examines the use of forecast combination to improve the accuracy of forecasts of cumulative demand. A forecast combination methodology based on least absolute…

Abstract

This study examines the use of forecast combination to improve the accuracy of forecasts of cumulative demand. A forecast combination methodology based on least absolute value (LAV) regression analysis is developed and is applied to partially accumulated demand data from an actual manufacturing operation. The accuracy of the proposed model is compared with the accuracy of common alternative approaches that use partial demand data. Results indicate that the proposed methodology outperforms the alternative approaches.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

Book part
Publication date: 17 November 2010

Kenneth D. Lawrence, Dinesh R. Pai and Sheila M. Lawrence

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an…

Abstract

This chapter proposes a fuzzy approach to forecasting using a financial data set. The methodology used is multiple objective linear programming (MOLP). Selecting an individual forecast based on a single objective may not make the best use of available information for a variety of reasons. Combined forecasts may provide a better fit with respect to a single objective than any individual forecast. We incorporate soft constraints and preemptive additive weights into a mathematical programming approach to improve our forecasting accuracy. We compare the results of our approach with the preemptive MOLP approach. A financial example is used to illustrate the efficacy of the proposed forecasting methodology.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

Book part
Publication date: 30 April 2008

J. Gaylord May and Joanne M. Sulek

This chapter will present a goal programming model which simultaneously generates forecasts for the aggregate level and for lower echelons in a multilevel forecasting

Abstract

This chapter will present a goal programming model which simultaneously generates forecasts for the aggregate level and for lower echelons in a multilevel forecasting context. Data from an actual service firm will be used to illustrate and test the proposed model against a standard forecast technique based on the bottom-up/top-down approach.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-787-2

Book part
Publication date: 12 November 2014

Joanne Utley

This paper presents a mathematical programming model to reduce bias for both aggregate demand forecasts and lower echelon forecasts comprising a hierarchical forecasting

Abstract

This paper presents a mathematical programming model to reduce bias for both aggregate demand forecasts and lower echelon forecasts comprising a hierarchical forecasting system. Demand data from an actual service operation are used to illustrate the model and compare its accuracy with a standard approach for hierarchical forecasting. Results show that the proposed methodology outperforms the standard approach.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78441-209-8

Keywords

Book part
Publication date: 18 July 2016

John F. Kros and William J. Rowe

Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many…

Abstract

Business schools are tasked with matching curriculum to techniques that industry practitioners rely on for profitability. Forecasting is a significant part of what many firms use to try to predict budgets and to provide guidance as to the direction the business is headed. This chapter focuses on forecasting and how well business schools match the requirements of industry professionals. Considering its importance to achieving successful business outcomes, forecasting is increasingly becoming a more complex endeavor. Firms must be able to forecast accurately to gain an understanding of the direction the business is taking and to prevent potential setbacks before they occur. Our results suggest that, although techniques vary, in large part business schools are introducing students to the forecasting tools that graduates will need to be successful in an industry setting. The balance of our chapter explores the forecasting tools used by business schools and firms, and the challenge of aligning the software learning curve between business school curriculum and industry expectations.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78635-534-8

Keywords

Book part
Publication date: 13 March 2013

Joanne Utley

Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in…

Abstract

Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in the component forecasts can reduce the effectiveness of combination. This study proposes a methodology for combining demand forecasts that are biased. Data from an actual manufacturing shop are used to develop the methodology and compare its accuracy with the accuracy of the standard approach of correcting for bias prior to combination. Results indicate that the proposed methodology outperforms the standard approach.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

Keywords

Book part
Publication date: 30 April 2008

Stephen DeLurgio

This is a study of forecasting models that aggregate monthly times series into bimonthly and quarterly models using the 1,428 seasonal monthly series of the M3 competition…

Abstract

This is a study of forecasting models that aggregate monthly times series into bimonthly and quarterly models using the 1,428 seasonal monthly series of the M3 competition of Makridakis and Hibon (2000). These aggregating models are used to answer the question of whether aggregation models of monthly time series significantly improve forecast accuracy. Through aggregation, the forecast mean absolute deviations (MADs) and mean absolute percent errors (MAPEs) were found to be statistically significantly lower at a 0.001 level of significance. In addition, the ratio of the forecast MAD to the best forecast model MAD was reduced from 1.066 to 1.0584. While those appear to be modest improvements, a reduction in the MAD affects a forecasting horizon of 18 months for 1,428 time series, thus the absolute deviations of 25,704 forecasts (i.e., 18*1,428 series) were reduced. Similar improvements were found for the symmetric MAPE.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-787-2

Book part
Publication date: 17 November 2010

Ronald K. Klimberg, George P. Sillup, Kevin J. Boyle and Vinay Tavva

Producing good forecast is a vital aspect of a business. The accuracy of these forecasts could have a critical impact on the organization. We introduce a new, practical…

Abstract

Producing good forecast is a vital aspect of a business. The accuracy of these forecasts could have a critical impact on the organization. We introduce a new, practical, and meaningful forecast performance measure called percentage forecast error (PFE). The results of comparing and evaluating this new measure to traditional forecasting performance measures under several different simulation scenarios are presented in this chapter.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

Book part
Publication date: 14 November 2011

Ronald K. Klimberg, George P. Sillup and Kevin Boyle

The accuracy of forecasts has a critical impact on an organization. A new, practical, and meaningful forecast performance measure, percentage forecasting error (PFE), was…

Abstract

The accuracy of forecasts has a critical impact on an organization. A new, practical, and meaningful forecast performance measure, percentage forecasting error (PFE), was introduced by the authors in an earlier publication. In this chapter, we examined the accuracy of the PFE under several different scenarios and found the results to indicate that PFE offers forecasters an accurate and practical alternative to assess forecast accuracy.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-959-3

Open Access
Article
Publication date: 3 August 2022

Guqiang Luo, Kun Tracy Wang and Yue Wu

Using a sample of 9,898 firm-year observations from 1,821 unique Chinese listed firms over the period from 2004 to 2019, this study aims to investigate whether the market…

Abstract

Purpose

Using a sample of 9,898 firm-year observations from 1,821 unique Chinese listed firms over the period from 2004 to 2019, this study aims to investigate whether the market rewards meeting or beating analyst earnings expectations (MBE).

Design/methodology/approach

The authors use an event study methodology to capture market reactions to MBE.

Findings

The authors document a stock return premium for beating analyst forecasts by a wide margin. However, there is no stock return premium for firms that meet or just beat analyst forecasts, suggesting that the market is skeptical of earnings management by these firms. This market underreaction is more pronounced for firms with weak external monitoring. Further analysis shows that meeting or just beating analyst forecasts is indicative of superior future financial performance. The authors do not find firms using earnings management to meet or just beat analyst forecasts.

Research limitations/implications

The authors provide evidence of market underreaction to meeting or just beating analyst forecasts, with the market's over-skepticism of earnings management being a plausible mechanism for this phenomenon.

Practical implications

The findings of this study are informative to researchers, market participants and regulators concerned about the impact of analysts and earnings management and interested in detecting and constraining managers' earnings management.

Originality/value

The authors provide new insights into how the market reacts to MBE by showing that the market appears to focus on using meeting or just beating analyst forecasts as an indicator of earnings management, while it does not detect managed MBE. Meeting or just beating analyst forecasts is commonly used as a proxy for earnings management in the literature. However, the findings suggest that it is a noisy proxy for earnings management.

Details

China Accounting and Finance Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1029-807X

Keywords

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