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A Prescriptive Stock Market Investment Strategy for the Restaurant Industry using an Artificial Neural Network Methodology
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Author(s): Gary R. Weckman (Ohio University, USA), Ronald W. Dravenstott (Ohio University, USA), William A. Young II (Ohio University, USA), Ehsan Ardjmand (Ohio University, USA), David F. Millie (Palm Island Enviro-Informatics LLC, USA)and Andy P. Snow (Ohio University, USA)
Copyright: 2020
Pages: 21
Source title:
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch014
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Abstract
Stock price forecasting is a classic problem facing analysts. Forecasting models have been developed for predicting individual stocks and stock indices around the world and in numerous industries. According to a literature review, these models have yet to be applied to the restaurant industry. Strategies for forecasting typically include fundamental and technical variables. In this research, fundamental and technical inputs were combined into an artificial neural network (ANN) stock prediction model for the restaurant industry. Models were designed to forecast 1 week, 4 weeks, and 13 weeks into the future. The model performed better than the benchmark methods, which included, an analyst prediction, multiple linear regression, trading, and Buy and Hold trading strategies. The prediction accuracy of the ANN methodology presented reached accuracy performance measures as high as 60%. The model also shown resiliency over the housing crisis in 2008.
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