Application of advanced statistical methodologies in financial decision-making: evidence from Nigerian market economy
DOI:
https://doi.org/10.64497/jssci.215Keywords:
applied statistics, financial decision-making, stock market performance, macroeconomic policy, quantitative modelling, time-series analysisAbstract
This study explored the effectiveness of applied statistical methodologies in financial decision-making by examining the effects of key macroeconomic variables on stock market performance in Nigeria and assessing whether applied statistical models enhance decision outcomes. Adopting an ex-post facto research design with annual data from 2019 to 2024, the study integrated descriptive statistics, multiple regression, and Partial Least Squares Structural Equation Modelling (PLS-SEM). To ensure robustness and capture market dynamics, AutoRegressive Integrated Moving Average (ARIMA) and Vector Autoregression (VAR) models were estimated. Model adequacy is validated using standard econometric diagnostics (Hausman, Wooldridge, heteroskedasticity, multicollinearity, and PLS-SEM reliability and validity tests), evaluated at the 5 per cent significance level. The results indicated that inflation, monetary policy rate, and exchange rate depreciation exert statistically significant negative effects on stock market performance, while GDP growth contributes positively, leading to rejecting the null hypothesis that macroeconomic variables do not influence market performance. PLS-SEM findings confirmed that applied statistical models significantly improve financial decision-making outcomes by capturing both direct and indirect macro-financial linkages. ARIMA and VAR analyses further revealed persistent market shocks and dynamic feedback effects among macroeconomic variables, underscoring the importance of time-series and multivariate modelling in volatile environments. Overall, the study provides robust evidence that data-driven frameworks enhance portfolio decisions and risk management; thus, applied statistical modelling improves financial decision-making and offers valuable insights for investors, policymakers, and market regulators, particularly in emerging markets characterised by macroeconomic instability.
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