Advances in Business and Management Forecasting: Volume 10
Table of contents(16 chapters)
List of Contributors
This paper presents a decomposition forecast of stock prices using time series of weekly stock price data as implemented in Excel. The following decomposition components are presented, analyzed, and interpreted including a moving average, a trend, a periodic function, and two shock variables including a triangular shock variable and a level change. The results of the individual components are compared and a discussion of each component’s efficiency is provided. The trend component is statistically significant over the forecast time. The moving average component displays a bi-modal error distribution over varying spans of the moving average and forecast periods. The first mode coincides with random walk behavior with an optimal span and forecast period of one. The second mode is more interesting and applicable for investing beyond the short-term with an optimal spans and forecast periods beyond 75 weeks. The periodic sine function well captures the typical U.S. business cycle of 4–5 years and significantly improves model performance. Finally, the significant outliers remaining from the decomposition are diagnosed and modeled with a triangular shock variable for the bust and recovery associated with the 2008 financial crisis. The model presented does a good job of decomposing the analytical components in forecasting stock prices and provides a useful illustration of Excel methods.
This research examines the use of a number of time series model structures of a moderate allocation mutual fund, PRWCX. PRWCX was rated as the top fund in its category during the past five years. The fund invests at least 50% of its total assets that the fund manager believes that have above average potential for capital growth. The remaining assets are generally invested in convertible securities, corporate and government debt bank loans, and foreign securities. Forecasting the total NAV of such a moderate allocation mutual fund, composed of an extremely large number of investments, requires a method that produces accurate results. These models are exponentially smoothing (single, double, and Winter’s Method), trend models (linear, quadratic, and exponential) are Box-Jenkins models.
The purpose of this paper is to improve the information quality of bankruptcy prediction models proposed in the literature by building prediction intervals around the point estimates generated by these models and to determine if the use of the prediction intervals in conjunction with the point estimated yields an improvement in predictive accuracy over traditional models. The authors calculated the point estimates and prediction intervals for a sample of firms from 1991 to 2008. The point estimates and prediction intervals were used in concert to classify firms as bankrupt or non-bankrupt. The accuracy of the tested technique was compared to that of a traditional bankruptcy prediction model. The results indicate that the use of upper and lower bounds in concert with the point estimates yield an improvement in the predictive ability of bankruptcy prediction models. The improvements in overall prediction accuracy and non-bankrupt firm prediction accuracy are statistically significant at the 0.01 level. The authors present a technique that (1) provides a more complete picture of the firm’s status, (2) is derived from multiple forms of evidence, (3) uses a predictive interval technique that is easily repeated, (4) can be generated in a timely manner, (5) can be applied to other bankruptcy prediction models in the literature, and (6) is statistically significantly more accurate than traditional point estimate techniques. The current research is the first known study to use the combination of point estimates and prediction intervals to in bankruptcy prediction.
Thanks to the advancement of digital image technology, digital single-lens reflex cameras (DSLRs) have replaced film single-lens reflex cameras. This advancement is reflected in some core technologies of DSLRs such as digital image sensors and electronic shutter mechanisms, which have allowed taking photographs even under tough conditions. In a similar vein, mirrorless interchangeable-lens cameras (MILCs) are now threatening to disrupt the DSLR market. Disruptive technologies represent a major challenge in forecasting. This paper uses specifications of over a 100 DSLRs and MILCs from the six leading dominant brands: Canon, Nikon, Sony, Pentax, Panasonic, and Olympus, for the analysis.
The service sector is a major segment of the economy and contributes to the gross national product in a significant manner. It complements the manufacturing sector as organizations become global in nature. Sources of raw material may be quite dispersed from the manufacturing site. Further, not all manufacturing may take place in one particular location. Based on the availability of expertise and the required operations to produce the product, components, subassemblies, or assemblies could be produced in different geographical locations. This creates the necessity to transport raw material, components, or assemblies in a timely manner from one location to another based on the needs of the supply chain. All customers prefer not only an efficient delivery system but also one that is damage-free. In this chapter, we consider a model whereby service organizations offer a contract for damage protection based on product value. The objective is to determine the premium to be charged by the service organization so as to at least break even or accomplish a desired profit margin.
This chapter proposes a new technique based on the data envelopment analysis (DEA) method to evaluate the scale efficiency with considering the environmental influences. Using this method, we can get the pure scale efficiency which has eliminated the environmental factors and random errors that might influence the production process. Our approach extends the three-stage-DEA model by Fried, Lovell, Schmidt, and Yaisawarng (2002) to the five-stage DEA model. Afterward, in order to measure the scale efficiency of the China’s universities more accurately, this chapter gives an empirical study on the scale efficiency of the top universities in China by applying the five-stage DEA model. The results show that the efficiency levels of many universities are indeed affected by external environmental variables and random factors. According to the levels of pure technical efficiency and scale efficiency, we divide China’s universities into four types, and we also propose some suggestions for the inefficient universities to improve their scale efficiency.
A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.
Reducing Bias in Hierarchical Forecasting
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.
Wikis have emerged as an important Web 2.0 technology for facilitating collaboration in organizations. Although researchers have begun to analyze their use in organizations and develop metrics to study their usage, there has been limited statistical analysis of use data. In addition, much of the empirical analysis of wikis is based on Wikipedia. This paper mitigates those limitations in the literature by examining data generated from a large consulting organization, Accenture, to statistically analyze the relationships between different use variables.
Demand seasonality in the U.S. Imported Beer industry is common. The financial cycles of the past decade brought some extreme fluctuations to industry demand, which was trending upward. This research extends previous work in this area by comparing seasonal forecasting models for two time periods: 1999–2007 and 1999–2012. The previous study (Kros & Keller, 2010) examined the 1999–2007 time frame while this study extends their model using the new data. Models are developed within Excel and include a simple yearly model, a semi-annual model, a quarterly model, and a monthly model. The results of the models are compared and a discussion of each model’s efficacy is provided. While, the models did do a good job forecasting U.S. Import Beer sales from 1999 to 2007 the economic downturn starting in 2007 was deleterious to some models continued efficacy. When the data from the downturn is accounted for it is concluded that the seasonal models presented are doing an overall good job of forecasting U.S. Import Beer Sales and assisting managers in shorter time frame forecasting.
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- Advances in Business and Management Forecasting
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