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This study examines the effect of high interest rates on economic growth in Nigeria, focusing on the Central Bank of Nigeria's (CBN) monetary policy framework. It specifically evaluates the short-run and long-run relationships among the Monetary Policy Rate (MPR), Gross Domestic Product (GDP), inflation rate, exchange rate, and broad money supply. The study adopts an ex-post facto research design and employs annual time-series data covering 2005–2024. The study analyzes data using the Autoregressive Distributed Lag (ARDL) model to examine short-run and long-run dynamics among variables with different orders of integration. The model assesses the effects of MPR, inflation rate, exchange rate, and broad money supply on economic growth. The findings indicate that high interest rates, proxied by MPR, have a statistically significant negative effect on GDP, suggesting that tight monetary policy constrains economic growth by increasing borrowing costs and discouraging private investment. Conversely, broad money supply has a positive and statistically significant effect on GDP, demonstrating the importance of adequate liquidity in stimulating investment and productive economic activities. The findings further indicate that although inflation control remains essential, excessively high interest rates may undermine output expansion and private-sector investment. The study concludes that the CBN should pursue a balanced monetary policy that controls Inflation without imposing excessively restrictive interest rates. Policymakers should improve access to affordable credit for businesses and households and strengthen monetary-fiscal policy coordination. Such measures would support investment, sustainable economic growth, price stability, and improved macroeconomic performance in Nigeria.
This study evaluates the performance of the Runge–Kutta Dual Attention (RUN-DA) optimization framework for hyperparameter tuning in a dual-attention Long Short-Term Memory (LSTM) model for financial time-series forecasting. The experiment was conducted using historical stock price data of MRS Oil Plc covering the period 2012–2024, representing a Nigerian financial market dataset. The proposed optimizer was compared with the Genetic Algorithm (GA) and Brown Bear Optimization Algorithm (BBOA) under consistent experimental conditions. Model performance was assessed using validation Mean Squared Error (MSE) and computational efficiency. Results show that RUN-DA achieved the lowest mean fitness value of 0.1369, compared with 0.4054 for GA and 0.1924 for BBOA. Sensitivity analysis indicated that moderate learning rates and time-step values produced more stable generalization performance. The evaluation under different market regimes further showed that RUN-DA maintained relatively lower fitness values across volatility conditions, decreasing from 0.2343 in low-volatility periods to 0.0473 in high-volatility periods, while GA and BBOA recorded higher corresponding values. In terms of computational efficiency, RUN-DA converged in 1015.23 seconds, slightly faster than GA (1073.25 seconds) and substantially faster than BBOA (2147.05 seconds). These results suggest that RUN-DA provides an effective optimization approach for improving LSTM-based financial forecasting models, although further validation on additional financial assets and evaluation metrics is recommended.
Using panel data from 46 Sub-Saharan Countries, this study employed Descriptives statistics, Correlation, Bound test for cointegration, Panel ARDL analysis to analysed the data by examining how health outcomes affect economic performance in Sub-Saharan. The study was motivated by the growing recognition that health is an important component of human capital development and a critical determinant of productivity, labor efficiency, and sustainable economic growth. Despite various healthcare reforms and increased investments in the health sector across the region, many countries in Sub-Saharan Africa continue to experience poor health outcomes, including low life expectancy and high under-five mortality rates, which may hinder economic performance. Against this background, the study investigated the extent to which selected health outcome indicators influence economic growth in the region. The findings indicates that life expectancy, under-five mortality, health expenditure and Population have significant impact on economic growth. The study recommends among others that Authorities in Sub-Saharan Africa should place more emphasis on measurable improvements in life expectancy and reductions in under-five mortality by adopting targeted, high-impact health interventions.
Portfolio optimization remains a central challenge in finance, demanding accurate stock forecasting and effective risk-return trade-offs. This study introduces a novel hybrid model, the Improved Arithmetic Optimization Algorithm Long Short-Term Memory (IAOA-LSTM), tailored for portfolio selection in the biotechnology and oil gas sectors. Leveraging a decade-long dataset comprising daily prices of 50 representative stocks, the proposed model integrates the temporal modeling strength of LSTM with the global search capabilities of an enhanced Arithmetic Optimization Algorithm. Comparative analyses against standard Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Grid Search LSTM(GRID-LSTM), Random Search LSTM (RANDOM-LSTM), Genetic Algorithm LSTM (GA-LSTM) models demonstrate IAOA-LSTM’s superior predictive performance, achieving the lowest errors. Stocks were ranked using a reconstruction error-based metric, and the most predictable were selected to construct optimized portfolios. Furthermore, the efficient frontier, derived via Monte Carlo simulations, identified the portfolio with the highest Sharpe ratio at volatility-return coordinates, offering the most favorable risk-adjusted return. Rigorous statistical testing confirms the model’s significant improvement over benchmarks. These findings underscore the potential of IAOA-LSTM in enhancing investment strategies through deep learning (DL) and bio-inspired optimization. By aligning sector-specific dynamics with advanced modeling, this research offers a robust decision-support tool for investors aiming to maximize returns under uncertainty.
