ASSESSMENT OF NIGERIA STOCK EXCHANGE (NSE) USING TIME SERIES ANALYSIS APPROACH

The stock market remains one of the most volatile and unpredictable sectors of the economy, creating challenges for investors and policymakers who rely on accurate forecasts for decision-making. In Nigeria, the Nigerian Stock Exchange (NSE) plays a central role in economic growth and capital formation, yet its fluctuations highlight the need for reliable forecasting models. This study focused on applying time series analysis to forecast Dangote stock prices as a case study for understanding stock market dynamics. The objectives were to fit a trend model to historical data, forecast stock prices using the ARIMA technique, and provide insights into the predictability of stock movements. Data spanning 2020–2023 were sourced from the National Bureau of Statistics and financial databases. Statistical tools such as autocorrelation, partial autocorrelation, and differencing were employed to ensure data stationarity before applying ARIMA. Model evaluation relied on metrics including R-squared and Mean Absolute Percentage Error (MAPE). Findings revealed that the ARIMA(1,2,1) model was the most appropriate, achieving an R-squared of 86% and MAPE of 7.36%, indicating strong predictive performance. The forecast suggested a steady upward trend in Dangote stock prices from November 2023 to May 2024. The study concludes that time series models, particularly ARIMA, are effective for stock market forecasting and recommends their adoption by investors, analysts, and regulators for informed investment and policy decisions.