Knowledge that Transforms

This paper examines whether climate risk affects the dynamics of banks’ regulatory capital adjustments, based on a large panel of European banks over the 2006–2021 period. Using a dynamic partial adjustment model, we find that climate-exposed banks hold higher capital adequacy ratios and adjust faster toward their optimal capital structure, particularly when exposed to transition risk and post-COP21. Climate risk also induces asymmetric adjustment behaviours. Deleveraging occurs through risk-weighted asset reallocation toward safer exposures, without asset liquidation or lending cuts. While leveraging operates through risk-weighted asset expansion, without reducing equity growth. However, pre-COP21, deleveraging is primarily achieved through lending contraction, whereas leveraging relies mainly on asset expansion. Our findings highlight the policy relevance of climate risk for prudential supervision and bank capital regulation.

This paper applies the Temporal Fusion Transformer (TFT) model to learn dynamic time-series patterns in mutual fund performance and to assess whether these patterns predict future alpha. I summarize the model’s cross-sectional ranking power using diagnostic portfolio spreads: a top-minus-bottom decile exhibits an annualized Carhart four-factor alpha spread of 2.8%, with dispersion persisting for up to four years. In panel regressions controlling for standard predictors and fund and time fixed effects, TFT forecasts improve explanatory power by more than 25% in adjusted R 2 . Leveraging TFT’s interpretable outputs, I show that historical fund returns receive the largest weight (about 29%), their importance displays earnings-cycle seasonality, and attention to past observations rises by 46% during crisis periods. Using fund-by-month variable-importance weights, I define fund-specific informativeness states and construct conditional skill measures that predict and persist precisely when the same signal becomes informative again, beyond coarse macro conditioning. Together, these results provide an alternative explanation for why unconditional performance persistence appears weak: skill is episodic and becomes visible when a manager’s key signals regain relevance. • TFT deep learning model predicts mutual fund alpha from time-series patterns. • Top-minus-bottom decile spread yields 2.8% annualized four-factor alpha. • Model attention to past observations rises 46% during crisis periods. • Conditional skill measures persist when key signals regain informativeness. • Episodic skill explains why unconditional persistence appears weak.

We examine how ETF adoption affects retail investor performance using a comprehensive panel of 524,181 Finnish investors tracked from 2007 to 2022. ETF adopters tend to be older, predominantly male, and more active, holding smaller but better-diversified portfolios. Importantly, first-time use of ETFs yields statistically significant improvements in risk-adjusted returns. ETF users also demonstrate greater portfolio resilience during financial crises. Our findings confirm that ETFs serve as effective tools for enhancing performance and managing risk for retail investors.