As countries grapple with the aftermath of climate-related disasters, the disruptions they inflict on domestic consumption ripple through the fabric of income and price level shocks. The income shock emanates from the adverse effects of such disasters on economic agents, leading to both wage and...

This study examines the impact of Empirical Mode Decomposition (EMD) and Recursive Feature Elimination (RFE) on the prediction of financial product performance employing several ensemble machine learning models, including Random Forest, XGBoost, LightGBM, AdaBoost, CatBoost, Bagging, and...

Developing countries face uncertainties driven by global macroeconomic variables over which they have little to no control. Key exogenous factors faced by most developing countries include interest rates in high-income countries, commodity prices, global demand for exports, and remit- tance...

Estimates of the area- and household idiosyncratic error distributions from household income and consumption regression models across 142 household surveys from 16 different countries, the type of models that underpin poverty maps, points to significant deviations from normality. Accounting for...

This study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a...

This paper compares the renowned GARCH model with a novel one, the Generalized Autoregressive Score (GAS) model in terms of forecasting performance. Considering the gap in the literature, this study focuses on the Turkish stock and FX markets. The analysis covers 25 years (1999-2023), of which...