Knowledge that Transforms

Miners of proof-of-work networks like Bitcoin tend to gravitate towards regions with cheap energy. We analyze risks associated with this geographical centralization by exploiting a local electricity supply shock. Compared to a control group consisting of an energy-efficient proof-of-stake cryptocurrency, the blockchain’s capacity for processing transactions decreases while transaction fees increase substantially. The increased settlement latency on the blockchain also reduces secondary market quality as seen in higher exchange rate volatility, lower liquidity, and larger price differences between exchanges. Overall, our results suggest that geographical centralization poses short-lived but potentially severe system-wide risks to proof-of-work networks.

This study examines why firms’ access to finance differs across countries and assesses the extent to which democracy and financial liberalisation account for these variations. Using political economy and liberalisation theories as a foundation, we analyse a comprehensive dataset of over 110,000 firms across 112 economies between 2006 and 2021. Although previous research identifies firm-level and macroeconomic sources of financial frictions, evidence on the institutional drivers of cross-country financial access remains limited. Our findings show that democracy and financial liberalisation, when considered independently, are associated with reduced access to finance. However, when both conditions coexist, they ease financing barriers and enhance access to finance. These results remain consistent across multiple robustness tests, including alternative model specifications, endogeneity corrections, and various measures of institutional quality and financial openness. Overall, the study highlights that neither democracy nor liberalisation alone is sufficient to enhance access to credit. Instead, simultaneous institutional strengthening and financial market access are necessary to ease financing barriers. This has important policy implications, particularly for emerging and developing economies seeking to expand firm-level access to capital and stimulate economic growth.

Wealth Management Products (WMPs) have become a major source of bank funding over the past decade. Using a unique WMP transactions dataset from China covering 99,893 transactions during 2010-2020, this study examines whether greater reliance on WMPs as a type of funding innovation increases bank systemic risk. We find that higher WMP dependence significantly elevates systemic risk, with the effects concentrated among smaller banks. Exploiting the 2018 Asset Management Regulation as an exogenous shock, we establish causality using a difference-in-differences approach. Our channel analysis shows that maturity mismatch amplifies WMP-related systemic risk, while higher WMP yields further increase fragility through funding cost pressures. Overall, these results call for a regulatory approach that moves beyond aggregate balance-sheet metrics and instead targets funding composition, maturity structure, and pricing behaviour-dimensions in which WMPs materially increase systemic vulnerability.

• Home equity erosion causally reduces household mobility even without negative equity. • The Tax Reform Act 1986’s mortgage-only tax shield identifies higher mortgage demand. • Exclusive mortgage deduction inflated mortgage by $7,600, squeezing equity by 8.2%. • The equity erosion reduced homeowner mobility by 3.8%. • Renter mobility remained unchanged, validating the mortgage-specific variation.

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.

Machine learning (ML) could strengthen banks’ resilience through improved credit risk screening and ultimately benefit financial stability. Yet, ML adoption in banking remains limited, with simpler linear models still predominating. We argue that the emergence of highly flexible ML models has created a new challenge for forecasting tasks: ‘model multiplicity’—where equally accurate ML models at the aggregate level produce divergent individual-level predictions (‘predictive multiplicity’) or differ in their decision surfaces (‘procedural multiplicity’). These issues raise fundamental questions: Why should an individual or firm be subject to an adverse credit risk model outcome when there is an equally accurate model that treats them more favorably? Using the world’s largest loss database of corporate defaults, we examine these two phenomena in recovery rate ( RR ) modeling and propose heterogeneous ML ensembles as a natural solution. By combining predictions and decision surfaces from multiple well-performing ML models, ensembles mitigate risks associated with predictive multiplicity by ensuring that borrowers are not subject to the fluctuations of a single model, and reduce procedural multiplicity by providing a robust measure of features that ultimately improve out-of-sample RR predictions. By addressing the ‘multiplicity of good models’ problem, our study emphasizes the importance of model stability and provides new insights for the future development of ML models.

We use the staggered deregulation of interstate banking in the U.S. to show that CEOs who have gained career experience at multiple banks are more likely to pursue acquisitions when competition intensifies. Acquisitions completed by these CEOs perform better than those led by CEOs whose career experience is confined to a single institution. Analyzing the sources of performance gains, we find that CEOs with greater across-bank experience are more effective at identifying and integrating dissimilar targets. Our findings cannot be explained by other formative CEO experiences or a CEO general ability. The results highlight the importance of externally versus internally acquired experience in explaining how managers respond to a competitive shock.

Using an extensive matched hedge fund-prime broker panel dataset for the period 2001–2021, we document a strong positive relationship between hedge fund leverage and prime broker’s stock price crash risk after controlling for other crash risk drivers. Our results are not only statistically, but also economically significant, showing that a one-standard-deviation increase in hedge fund leverage is associated on average with an increase of around 5% of a standard deviation in the negative skewness or the down-to-up-volatility of bank stock returns. Moreover, they remain robust when accounting for endogeneity and conducting many robustness checks. We also document that some investment strategies, such as one focusing on fixed income, appear to decrease the slope of the risk metrics of prime brokers, and ultimately leading to lower stock price crash risk.