An Exploration of Approaches to Forecasting Company Earnings using the Neural Network Paradigm

 

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  • Product Description
 

This study addresses the fundamental management problem of decision-making in a climate where future values of important variables are unknown and can at best be estimated. Traditionally various techniques have existed to help managers forecast earnings; most of these techniques are founded in statistics, thus requiring some limiting assumptions. The use of neural networks has been described as a promising non-parametric approach, negating the need for statistical assumptions. This thesis explores the application of the neural network paradigm to the area of earnings forecasting. A novel radial basis function approach is developed in several configurations and these are explored for their abilities to forecast earnings for non-financial companies included in Hong Kong''s Hang Seng 100 index. The resultant model was tested for forecast accuracy as well as its generalisability across various industries included in the index. Several incremental models are presented and tested showing that the inclusion of variables external to company information increase the accuracy of forecasts, and thus information quality.

Product Specifications
SKU :COC22735
AuthorRobert Biscontri
LanguageEnglish
BindingPaperback
Number of Pages232
Publishing Year3/17/2010
ISBN978-3838351650
Edition1 st
Book TypeBusiness & management
Country of ManufactureIndia
Product BrandLAP LAMBERT Academic Publishing
Product Packaging InfoBox
In The Box1 Piece
Product First Available On ClickOnCare.com2015-07-28 00:00:00
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