Application of ARMA Model in Forecasting Nigerian Stock Price Index (NSPI)

This was designed to study and compare the performance of the autoregressive (AR), Moving average (MA) and Autoregressive moving average (ARMA) models in forecasting the Nigeria Stocks Price Index (NSPI). The data used for this study are secondarily sourced from ‘All share index’ of the Nigeria stock exchange rate between 2009 and 2024. A test for stationarity of the original data was carried out using the augmented Dickey-fuller (ADF) test and the test showed the non-stationarity of the observed data, hence a test for stationarity for differenced data set was carried out to achieved stationarity. The results from the analysis showed that the ARMA (1,0,1) model outperforms the AR (1) and MA (1) models with a lower Akaike information criterion (AIC), Mean square error (MSE), Mean absolute error (MAE) and Root mean Square Error (RMSE). The study therefore recommends that investors and financial institutions use the ARMA (1, 0, 1) model to forecast the Nigeria Stock price index for precision of results.

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Publication Details

Journal
International Journal of Development Mathematics (IJDM)
Published
2025-06-29
DOI
https://doi.org/10.62054/ijdm/0202.20
Primary Topic
Stock Market Forecasting Methods
Type
article
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article

Application of ARMA Model in Forecasting Nigerian Stock Price Index (NSPI)

Kennedy Irikefe Ekerikevwe, Tayo Kamoru Oyeleke
International Journal of Development Mathematics (IJDM)
Stock Market Forecasting Methods
article

Application of ARMA Model in Forecasting Nigerian Stock Price Index (NSPI)

Kennedy Irikefe Ekerikevwe, Tayo Kamoru Oyeleke
article en

Abstract

This was designed to study and compare the performance of the autoregressive (AR), Moving average (MA) and Autoregressive moving average (ARMA) models in forecasting the Nigeria Stocks Price Index (NSPI). The data used for this study are secondarily sourced from ‘All share index’ of the Nigeria stock exchange rate between 2009 and 2024. A test for stationarity of the original data was carried out using the augmented Dickey-fuller (ADF) test and the test showed the non-stationarity of the observed data, hence a test for stationarity for differenced data set was carried out to achieved stationarity. The results from the analysis showed that the ARMA (1,0,1) model outperforms the AR (1) and MA (1) models with a lower Akaike information criterion (AIC), Mean square error (MSE), Mean absolute error (MAE) and Root mean Square Error (RMSE). The study therefore recommends that investors and financial institutions use the ARMA (1, 0, 1) model to forecast the Nigeria Stock price index for precision of results.

International Journal of Development Mathematics (IJDM)Vol. 2(2)
Delta State Polytechnic Ogwashi-Uku (NG)
Openalex Percentile: Top 82%
Stock Market Forecasting Methods
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Application of ARMA Model in Forecasting Nigerian Stock Price Index (NSPI) — Kennedy Irikefe Ekerikevwe, Tayo Kamoru Oyeleke · International Journal of Development Mathematics (IJDM) (2025) | TGRS Research Map | TGRS