Time Series Modeling and Forecasting of NEPSE Index: An ARIMA/SARIMA Approach with Market Efficiency Evidence

Forecasting stock market behavior remains an important issue for investors, policymakers, and financial researchers, particularly in emerging markets where market volatility and informational inefficiencies may influence investment decisions. Despite growing interest in forecasting the Nepal Stock Exchange (NEPSE) index, existing studies have primarily focused on conventional ARIMA-based approaches without sufficiently incorporating rolling-window out-of-sample evaluation and benchmark comparison within the context of weak-form market efficiency. Addressing this gap, the present study examines the forecasting performance of the NEPSE daily closing index using an ARIMA/SARIMA-based time series framework and compares the selected model with a naïve random walk benchmark. The study utilized 1,157 daily observations of the NEPSE index and applied descriptive statistics, Augmented Dickey–Fuller stationarity testing, autocorrelation analysis, SARIMA model selection procedures, residual diagnostic testing, and rolling-window one-step-ahead forecasting techniques. Based on model selection criteria, seasonal structure, and diagnostic adequacy, SARIMA (3,1,0) (2,0,0) [5] was selected as the final forecasting model. The findings revealed that the SARIMA model generated slightly lower forecasting errors than the naïve benchmark during the window evaluation period, although the forecasting gains remained economically modest. Consequently, the results are broadly consistent with weak-form market efficiency, suggesting that historical price information provides only a limited predictive advantage in forecasting the NEPSE index. Residual diagnostic analysis further indicated the presence of remaining volatility clustering, implying that future studies may benefit from integrating GARCH-type volatility models. Overall, the study contributes to the growing NEPSE forecasting literature by incorporating rolling-window out-of-sample forecasting and benchmark comparison within a seasonal time series framework. Keywords: NEPSE, SARIMA, Stock Market Forecasting, Time Series Analysis, Rolling-Window Forecasting, Weak-Form Market Efficiency.