Volume 1_Number 2

Purpose – This study investigates how ESG-related informational uncertainty is associated with the financial performance of listed real estate firms in Vietnam. It examines whether this relationship is nonlinear and heterogeneous while evaluating the usefulness of explainable machine learning in an emerging market.

Design/methodology/approach – A balanced monthly panel of 28 listed real estate firms covering 2013-2024 is analyzed. Eight machine-learning algorithms are compared, with Particle Swarm Optimization used for hyperparameter tuning. Model interpretability is achieved using SHAP, complemented by partial dependence plots and accumulated local effects to identify nonlinear and heterogeneous associations between ESG uncertainty and return on equity (ROE).

Findings – XGBoost delivers the strongest out-of-sample predictive performance, indicating that the ESG uncertainty-performance relationship is inherently nonlinear. Explainable AI reveals a state-dependent pattern: ESG uncertainty is negatively associated with ROE at low levels, becomes broadly neutral as firms adapt, and is associated with more favorable outcomes only among firms with stronger adaptive capacity under high uncertainty. Large firms are more sensitive to ESG uncertainty, medium-sized firms display more stable adaptive responses, and small firms exhibit weaker responses. These findings represent model-implied associations rather than causal effects.

Originality/value – This study shifts the ESG-performance literature from static ESG levels to ESG-related informational uncertainty and integrates explainable machine learning with SHAP to uncover nonlinear and heterogeneous effects, providing new evidence from Vietnam's listed real estate sector.

Purpose: This paper examines whether stacking (Wolpert, 1992) improves short-term forecasts of global oil consumption and electricity demand during periods of major shocks, including COVID-19, geopolitical conflicts, and trade disruptions. Given diverging trends between OECD and non-OECD economies, we evaluate forecast performance separately across these regions.

Design/methodology/approach: We combine XGBoost, random forest, elastic net, multi-layer perceptron, and a unit fixed-effects model within a stacking framework using a non-negative OLS meta-learner. We implement both a top-down approach, which models aggregate demand directly, and a bottom-up approach, which models disaggregated components and aggregates predictions, to assess the value of decomposition.

Findings: We find that stacking provides a robust benchmark, consistently matching or outperforming the best individual learner across most specifications. In the top-down setting, stacking generally delivers the strongest performance, particularly for global and non-OECD oil demand and global electricity demand. Bottom-up stacking improves accuracy for most oil sub-aggregates and performs comparably to the best base learner for electricity demand by source. Comparing strategies, bottom-up stacking performs better for electricity demand, while both approaches perform similarly for oil demand. Results by demand size suggest that retransformation bias from the log scale (Duan, 1983) and cross-country heterogeneity are particularly important for large economies. Overall, while no single model dominates across all settings, stacking provides a simple and reliable approach to improving forecast accuracy.

Originality/value: Our contribution is threefold. First, we provide a systematic comparison of stacking methods for forecasting global energy demand across OECD and non-OECD economies. Second, we evaluate the relative performance of top-down and bottom-up strategies within a common framework. Third, we examine how forecast performance varies with the scale of energy demand, highlighting the role of retransformation bias (Duan, 1983) and cross-country heterogeneity.

Purpose – Presenting a simplest tutorial for new comers to the field of chaotic dynamics in economics and finance.

Design/methodology/approach – To attract economists, the paper is “written in a way that doesn’t kill it”.

Findings – Elaborating in simplest terms the complex notion of chaotic dynamics.

Originality/value – Aiming at attracting economists to chaotic dynam- ical approach to economics and finance.