Advances in Business and Management Forecasting
Published on 1. September 2021
Book
Hardback
184 pages
978-1-83982-091-5 (ISBN)
Description
Volume 14, Advances in Business and Management Forecasting is a blind refereed serial publication. It presents state-of-the-art studies in the application of forecasting methodologies in such areas as financial forecasting, market demand analysis, executive compensation forecasting, data analysis, forecasting improvement with interpolation and cluster analysis.
This is a key text for academics and researchers of financial forecasting, market demand forecasting and executive compensation forecasting.
This is a key text for academics and researchers of financial forecasting, market demand forecasting and executive compensation forecasting.
More details
Series
Language
English
Place of publication
Bingley
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 15 mm
Weight
426 gr
ISBN-13
978-1-83982-091-5 (9781839820915)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Kenneth D. Lawrence | Ronald K. Klimberg
Advances in Business and Management Forecasting
E-Book
09/2021
1st Edition
Emerald Publishing Limited
€92.49
Available for download
Persons
Kenneth D. Lawrence is a Professor Management Science and Business Analytics at the Tuchman School of Management at the New Jersey Institute of Technology.
Ronald K. Klimberg is a Professor in the Decision and System Sciences Department of the Haub School of Business at Saint Joseph's University.
Ronald K. Klimberg is a Professor in the Decision and System Sciences Department of the Haub School of Business at Saint Joseph's University.
Content
A. Forecasting Methods and Applications I;
Chapter 1. The Predictive Power of Investor Sentiment on U.S. Equity Market Performance; Matthew Steves, Son Nguyen, John Quinn, and Alan Olinsky;
Chapter 2. A Regression Model to Forecast the Compensation of Top Level Executives at MetLife; Kenneth D. Lawrence and Sheila M. Lawrence;
Chapter 3. Forecasting Seasonal Products: A Case of Ice Cream Sales; Divyanshi Trakran;
Chapter 4. Predicting the Length of Stay in Hospital Emergency Rooms in Rhode Island; Alicia Lamere, Son Nguyen, Gao Niu, Alan Olinsky, and John Quinn;
B. Forecasting Methods and Applications II;
Chapter 5. Effects of Resampling Techniques on Imbalanced Data Classification: A New Under-Resampling Method; Son Nguyen, Phyllis Schumacher, Alan Olinsky, and John Quinn;
Chapter 6. Development of a Method to Improve Statistical Forecasts using Interpolation and Cluster Analysis; Ronald K. Klimberg and Samuel Ratick;
Chapter 7. Performance Measures of Products with Multiple Characteristics Utilizing Unit Costs of Nonconformance; Amitava Mitra;
Chapter 8. Competing Forecasting Techniques Produces Different Optimal Solutions Based on the Product; John L. Stanton and Stephen L. Baglione;
C. Covid-19 Trend Analysis;
Chapter 9. Exploring the Trend of COVID-19 Treatment Efficiency in Chinese Provinces in 2020 by using DEA and Regression Analysis; Feng Yang, Zhen Bi, Fangqing Wei, and Zhimin Huang;
Chapter 10. The Impact of International Price on the Technological Industry in the USA and China During Times of Crisis: Commercial War and COVID-19; Lisset-Vanesa Apcho-Ccencho, Berdy-Briggitte Cuya-Velasquez, Diego Alvarado Rodriguez, Maria de las Mercedes Anderson-Seminario, Aldo Alvarez-Risco, Alfredo Estrada-Merino, and Sabina Mlodzianowska;
Chapter 11. Price Variation in Lower Goods as of Previous Economic Crisis and the Contrast of the Current Price Situation in the Context of COVID-19 in Peru; Maria-Alejandra Leiva-Martinez, Maria de las Mercedes Anderson-Seminario, Aldo Alvarez-Risco, Alfredo Estrada-Merino, and Sabina Mlodzianowska
Chapter 1. The Predictive Power of Investor Sentiment on U.S. Equity Market Performance; Matthew Steves, Son Nguyen, John Quinn, and Alan Olinsky;
Chapter 2. A Regression Model to Forecast the Compensation of Top Level Executives at MetLife; Kenneth D. Lawrence and Sheila M. Lawrence;
Chapter 3. Forecasting Seasonal Products: A Case of Ice Cream Sales; Divyanshi Trakran;
Chapter 4. Predicting the Length of Stay in Hospital Emergency Rooms in Rhode Island; Alicia Lamere, Son Nguyen, Gao Niu, Alan Olinsky, and John Quinn;
B. Forecasting Methods and Applications II;
Chapter 5. Effects of Resampling Techniques on Imbalanced Data Classification: A New Under-Resampling Method; Son Nguyen, Phyllis Schumacher, Alan Olinsky, and John Quinn;
Chapter 6. Development of a Method to Improve Statistical Forecasts using Interpolation and Cluster Analysis; Ronald K. Klimberg and Samuel Ratick;
Chapter 7. Performance Measures of Products with Multiple Characteristics Utilizing Unit Costs of Nonconformance; Amitava Mitra;
Chapter 8. Competing Forecasting Techniques Produces Different Optimal Solutions Based on the Product; John L. Stanton and Stephen L. Baglione;
C. Covid-19 Trend Analysis;
Chapter 9. Exploring the Trend of COVID-19 Treatment Efficiency in Chinese Provinces in 2020 by using DEA and Regression Analysis; Feng Yang, Zhen Bi, Fangqing Wei, and Zhimin Huang;
Chapter 10. The Impact of International Price on the Technological Industry in the USA and China During Times of Crisis: Commercial War and COVID-19; Lisset-Vanesa Apcho-Ccencho, Berdy-Briggitte Cuya-Velasquez, Diego Alvarado Rodriguez, Maria de las Mercedes Anderson-Seminario, Aldo Alvarez-Risco, Alfredo Estrada-Merino, and Sabina Mlodzianowska;
Chapter 11. Price Variation in Lower Goods as of Previous Economic Crisis and the Contrast of the Current Price Situation in the Context of COVID-19 in Peru; Maria-Alejandra Leiva-Martinez, Maria de las Mercedes Anderson-Seminario, Aldo Alvarez-Risco, Alfredo Estrada-Merino, and Sabina Mlodzianowska