Abstract This paper provides a timely detection method of an emerging bubble in the NASDAQ Composite Index using the Generalized Supremum Augmented Dickey-Fuller test. The detected bubbles are then analyzed with the help of a Dynamic Stochastic General Equilibrium–Asset Pricing (AP) model, which combines economic fundamentals and financial dynamics. Through a comprehensive analysis of booms and busts of the NASDAQ Composite Index over the past 50 years, particularly the in-depth analysis of the Dotcom Bubble around 2000, the Housing Bubble prior to the 2008–09 Global Financial Crisis, and the current bubble after 2020, we have formulated and confirmed the Connectedness Hypothesis (tight links between economic fundamentals and stock dynamics) and the Overshooting Hypothesis (excessive adjustments in share prices and returns). Three drivers of bubbles are identified: (i) unusual market sentiments (stock return shocks), (ii) unanticipated technological hype (technological progress shocks), and (iii) unexpected credit expansions (monetary policy shocks). The current bubble, which burst in 2022 but re-emerged after the AI boom in 2023, appears to be driven by all three factors, making the index easy to expand but also more vulnerable to downside risks or shocks.
Abstract Quantifying the risk of financial distress, which can lead to risk of default and the emergence of zombie firms, is of considerable interest to academics, investors, and regulators. This study examines and predicts financial distress likelihood among non-bank publicly listed firms in four Southeast Asian countries—Indonesia, Malaysia, the Philippines, and Thailand—over the period 2005–19 using panel logit models. We evaluate predictive performance through out-of-sample forecasts. Estimates from the 2005–19 sample are used to forecast financial distress in 2020, after which the estimation window is updated annually through 2022 to generate one-year-ahead forecasts. This recursive forecasting framework provides a built-in test of the robustness and stability of model estimates and predictions. To improve forecasting accuracy, we use Least Absolute Shrinkage and Selection Operator (LASSO) techniques. The results show that the panel logit models perform reasonably well in identifying distressed firms, while LASSO delivers the expected improvement in predictive accuracy. These findings provide useful insights for investors, financial institutions, and policymakers seeking to identify vulnerable firms and mitigate financial risks.
Abstract Previous research on disaster preparedness has largely examined whether individuals prepare for natural disasters, but less is known about how preparedness differs across socioeconomic groups and disaster contexts. Using a nationwide survey (N = 21,501), this study identifies four preparedness patterns—home stockpiles, emergency supply bags, both, or neither—and examines how demographic and socioeconomic factors shape them. Individuals with higher earthquake risk awareness are more likely to prepare, whereas renters are less likely to maintain home stockpiles, and high-income households are more likely to adopt both strategies. We further combine the survey with consumer point-of-sale data to track changes in purchasing behavior. Responses to the realized earthquake and the precautionary advisory differed in both the speed and geographic spread of emergency-related purchasing. These findings suggest that household preparedness is shaped not only by socioeconomic conditions and risk perception but also by the type and predictability of disasters.
