Statistical Modeling of High Frequency Datasets Using the ARIMA-ANN Hybrid

Etaf Alshawarbeh, Alanazi Talal Abdulrahman and Eslam Hussam ()
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Etaf Alshawarbeh: Department of Mathematics, College of Science, University of Ha’il, Ha’il P.O. Box 55476, Saudi Arabia
Alanazi Talal Abdulrahman: Department of Mathematics, College of Science, University of Ha’il, Ha’il P.O. Box 55476, Saudi Arabia
Eslam Hussam: Department of Accounting, College of Business Administration in Hawtat bani Tamim, Prince Sattam bin Abdulaziz University, Hawtat bani Tamim, Saudi Arabia

Mathematics, 2023, vol. 11, issue 22, 1-17

Abstract: The core objective of this work is to predict stock market indices’ using autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and their combination in the form of ARIMA-ANN. Financial data are, in fact, trendy, noisy and highly volatile. To tackle their chaotic nature and forecast the three considered stock markets, namely Nasdaq stock exchange, United States, Nikkei stock exchange, Japan, and France stock exchange data (CAC 40 index), we use novel approaches. The data are taken from the Yahoo Finance website for the period from 4 January 2010 to 20 August 2021. To assess the relative predictive effectiveness of the selected tools, the dataset was divided into two distinct subsets: 75% of the data was allocated for training purposes, while the remaining 25% was reserved for testing. The empirical results suggest that ARIMA-ANN produces more accurate forecasts than the separate components of all stock markets. In light of this, it may be inferred that the combining tool is more effective in analyzing financial data and provides a more accurate comparative prediction.

Keywords: stock markets; machine learning; hybridization; forecasting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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