Cognitive biases influence investor decision-making and how media outlets frame and report news. This study investigates whether cognitive biases embedded in news data can serve as early warning indicators of shifts in market sentiment and systemic risk, and whether emotion adds analytical value. News data from three South African outlets were analyzed for the period from January 2025 to May 2026, alongside major events that materially affected systemic risk. The study establishes the relationship between cognitive biases and the economic cycle, defines key cognitive bias proxy measures covering herding, availability, overconfidence, and loss aversion, and connects these measures to sentiment, emotion, and systemic risk. Large language models are used to extract sentiment and emotion from the news data. A systemic risk index is derived from South African equity, bond, foreign exchange, and credit variables. It captures financial stress and is more responsive to shocks than macroeconomic-based indices. The study shows that cognitive bias variables help explain changes in sentiment and emotional intensity, which often precede structural breaks in market variables. Spillover risk analysis shows that exogenous shocks coincide with more negatively toned news articles and a sharp increase in systemic risk.
The nonlinear ARDL (NARDL) model by Shin et al. tests for asymmetric adjustment by decomposing a forcing variable into cumulated positive and negative changes. We study this linear-in-parameters partial-sum design when the forcing variable is stationary, as is commonly assumed for composite pressure indices. The two partial sums then share one stochastic trend, carry opposite deterministic trends, and are cointegrated by construction; the long-run symmetry test concerns the coefficient on cumulative absolute variation. If the dependent variable’s drift is not explained by the other level regressors, the estimated asymmetry converges to the unexplained drift divided by half the mean absolute change in the forcing variable, and the test rejects with a probability approaching one. Without a levels relationship, the Wald statistic has a nonstandard limit. Monte Carlo experiments confirm both results. Where a valid levels relationship exists, a linear trend removes the gross distortion and a restricted bootstrap with the trend restores size. For the Global Supply Chain Pressure Index and U.S. long-distance less-than-truckload freight prices over 1998M1–2026M7, the conventional asymmetry estimate (0.0111) is close to its drift benchmark (0.0121), and symmetry is rejected at every lag order without a trend and at none with one. Sign-split innovation models do not support rockets-and-feathers asymmetry, although their estimates are imprecise.
Installed renewable capacity is the leading indicator of energy-transition efforts, yet its conversion into realized generation is rarely estimated directly. This study estimates that rate in a panel of 118 developing and emerging economies over 2001–2023, treating proportional conversion, a unit coefficient, as a testable benchmark. In the preferred two-way fixed-effects specification, non-hydro renewable generation per person rises with one-year-lagged capacity per person, both in log(1 + x) form, at a rate of 1.042 (standard error, SE = 0.029), statistically indistinguishable from proportional conversion (test of unit coefficient, p = 0.148); the pure log–log elasticity among positive observations is lower, 0.845 (SE = 0.039). The estimate is stable under country-specific trends (0.924, SE = 0.046) and fixed technology shares (1.025, SE = 0.029), and a distributed lag in exact first differences shows that generation responds mainly to the current and previous year’s capacity changes, with a cumulative response of 1.070 (SE = 0.044). Higher lagged capacity is associated with a 3.630 percentage-point rise in the renewable output share (SE = 0.813) and a 3.258 percentage-point fall in the fossil share (SE = 0.771). Governance interactions are small and fragile under multiple-testing correction, and Poisson and pure log–log checks address the units-sensitivity of log(1 + x) estimates. Conditional on lag structure and functional form, deployment statistics are informative about realized generation and fossil substitution, whereas broader institutional and development gains remain unconfirmed.
This study examines the time-varying and industry-specific threshold effect of “National Team” shareholding on firms’ capital structure. We develop a conditional threshold panel smooth transition regression (CoT-PSTR) model with an industry-year quantile threshold, in which the threshold is determined by firms’ relative leverage position within the industry-year distribution. Using a panel dataset of 1353 Chinese A-share listed firms from 2015 to 2024, we find empirical evidence of a time-varying and industry-specific threshold effect. The effect of “National Team” shareholding on future leverage changes smoothly and gradually from negative to positive as firms’ relative leverage positions increase. Overall, this study introduces a new approach to identifying time-varying and industry-specific thresholds, thereby contributing to a better understanding of the nonlinear relationship between state-backed institutional investors and firms’ leverage.
This study investigates the interdependence among exchange rates, equity indices, precious metals, and energy prices across emerging markets, utilising daily data from 8 August 2005 to 30 August 2025. The study utilised a Vine Copula-based ARFIMA-SGARCH model to capture nonlinear, asymmetric, and extreme tail dependence across asset classes and countries without imposing directional assumptions. The results reveal persistent volatility, heavy tails and pronounced crisis-period intensification of dependence across markets. Moreover, platinum and palladium exhibit strong symmetric tail dependence across Brazil, Russia, India, South Africa, and Norway, indicating substantial joint downside and reduced diversification benefits. We uncover country-specific dependence patterns, notably in Russia, where the equity index and Brent oil pose significant downside risk exposure, thereby undermining diversification. In the C-Vine structure, platinum serves as a central node in the estimated dependence network connecting assets across these markets. Portfolio managers should be cautious about Russia-specific exposures and assess downside risk exposure associated with Brent oil and Russian equities, while also monitoring contagion risk in metal pairs. Overall, the findings highlight how global uncertainty and portfolio rebalancing generate heterogeneous, nonlinear dependence structures, underscoring the fragility of diversification under extreme market conditions.
