In this paper, we describe genetic algorithms (GA’s) for forecasting long-term quarterly sales of products in the telecommunications technology sector using widely available economic indicators such as Disposable Personal Income and New Housing Starts as independent variables. Individual chromosomes indicated inclusion or disinclusion of specific economic variables, as well as operational rules...

This paper compares alternative time-series models to forecast state tax revenues. Forecast accuracy is compared to a benchmark random walk forecast. Quarterly data for California is used to forecast total tax revenue along with its three largest components, sales, income, and corporate tax revenue. For oneand four-quarter-ahead forecasts from 2004 to 2009, Bayesian vector autoregressions gener...

F sales forecasts for retailers can be improved if we incorporate cost of goods sold, inventory, and gross margin (defined by us as the ratio of sales to cost of goods sold) as three endogenous variables. We construct a simultaneous equations model, estimated using public financial and nonfinancial data, to provide joint forecasts of annual cost of goods sold, inventory, and gross margin for re...

Recently, the number of Convenience Store (CVS) increases dramatically and CVS’s business operation is facing various competitive environments in Taiwan. Sales forecasting of daily fresh foods of CVS is highly complex due to the influence of internal and external environments. However, reliable sales forecasting can improve the quality of decision making and increase its competitiveness. The pu...

Efficient and accurate sales forecasting is a vital part of creating an efficient supply chain in enterprises. Times series methods are a popular choice for forecasting demand sales. A major challenge is to develop a relatively inexpensive and automated forecasting engine that guarantees a desired forecasting accuracy. Times series decomposition and Forecast combination have been two classes of...

Zara introduces 10,000 new designs every year and distributes 5.2 million clothing articles per week to a network of over 1925 stores in more than 86 countries. Their high product mix and vast global network makes demand forecasting for Zara a challenging endeavor. This thesis sets out to incorporate the effects from seasonality, product lifecycle, and cannibalization into a long term aggregate...

Purchase intentions are routinely used to forecast sales of existing products and services. While past studies have shown that intentions are predictive of sales, they have only examined the absolute accuracy of intentions, not their accuracy relative to other forecasting methods. For example, no research has been able to demonstrate that intentions-based forecasts can improve upon a simple ext...

Title of Dissertation: USING ONLINE SEARCH DATA TO FORECAST NEW PRODUCT SALES Gauri M. Kulkarni, Doctor of Philosophy, 2010 Dissertation directed by: Professor P.K. Kannan and Professor Wendy W. Moe Department of Marketing This dissertation focuses on online search as a measure of consumer interest. Internet use is at an all-time high in the United States, and according to the Pew Internet & Am...

In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodol...

We document that market reactions to insider purchases and sales are different in terms of price and volume. With an extensive data set that covers the period from 1991 to 2006, we systematically segregate the asymmetric effects of these two types of insider transactions. We follow the design of previous studies (e.g., Basu, 1997; Ball and Shivakumar, 2005, Leone et al. 2006) to understand the ...