Challenges of Stock Prediction

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Author(s): Walid A. Mohammed (The University of Salford, UK)
Copyright: 2020
Pages: 19
Source title: Valuation Challenges and Solutions in Contemporary Businesses
Source Author(s)/Editor(s): Sinem Derindere Köseoğlu (Independent Researcher, Turkey)
DOI: 10.4018/978-1-7998-1086-5.ch013

Keywords: Business & Management / Business and Organizational Research / Business Science Reference / Risk Management & Analysis

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Abstract

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.

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