Challenges of Stock Prediction

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The challenge of the stock price forecast is the most crucial component for companies and equity traders to predict future revenues. A successful and accurate prediction to the future stock prices ultimately results in profit maximisation. This chapter proposes the use of autoregressive integrated moving average (ARIMA) and the artificial neural networks (ANNs) models to predict the future prices of the stock. Using Walmart's stock index, the results show that both ARIMA and the ANNs models provide accurate forecasting performance. However, for short-term forecasting, the performance of ANNs outperformed ARIMA models.
Original languageEnglish
Title of host publicationValuation Challenges and Solutions in Contemporary Businesses
PublisherBusiness Science Reference
Number of pages19
ISBN (Print)9781799810865, 9781799810889
Publication statusPublished - 29 Nov 2019

Publication series

NameAdvances in Business Information Systems and Analytics


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