The rapid adoption of tokenization has redefined world trade and financial ecosystems, promoting decentralized, transparent, and frictionless value networks. However, this interconnectivity increases vulnerabilities in systems, making it easy to transmit shocks among each other. This paper explores the dynamics of spillovers and the interdependencies of portfolios in the tokenized trade ecosystem that includes supply chain, transportation, storage and payment tokens. Using the R^2 decomposed connectedness framework on daily data between November 2019 and August 2025 covering major global events, we assess both spillover and portfolio performance in the interconnected risk environment. The results indicate medium level connectedness (42.26%), with contemporaneous effects dominating the system. VeChain and Storj tokens emerges as the most dominant transmitters, whereas Mass vehicle ledger and Populous act as net shock receiver. Further, the results reveal significant heterogeneity among token in risk-return dynamics, asset allocation, and hedging efficiency. Notably, pair such as VET/DASH show relatively higher hedging effectiveness contributing to improves risk-adjusted returns and portfolio stability. Overall, the study provides evidence that while tokenized assets are increasingly interconnected, effective hedging remains asset-specific. These insights offer important implications for investors and portfolio managers seeking to design resilient crypto portfolios under conditions of extreme market uncertainty.
Hedged mutual funds (HMFs), employing hedge-fund-like strategies such as leverage, short-selling, and derivatives, have become a significant segment of the U.S. fund industry. These strategies generate returns tied to nontraditional or exotic risk factors, raising the question of whether investors can distinguish managerial skill from HMFS’ exotic risk exposures. Using U.S. data from 1994–2023, we examine fund flows with portfolio sorts, Fama–MacBeth regressions, and sentiment-based tests. We find that the apparent outperformance of positive-flow funds disappears once exotic risk factors are accounted for using the Fung–Hsieh (2001, 2004) model. Flows respond strongly to raw returns and Carhart alphas but not to Fung–Hsieh alphas, and they increase with exotic-risk exposures. These effects are concentrated in high-sentiment periods and among retail investors-tilted funds. Overall, our results show that HMF investors chase risk exposures rather than skill, amplifying misallocation risks in complex products.
This paper provides evidence on the link between public firms’ employee diversity and future firm performance. It documents that employee ethnic diversity predicts one-year ahead returns and accounting performance. Although diversity predicts greater future earnings, cash flows, and innovation, it is also linked with greater stock price volatility and lower employee productivity. The relationship between employee diversity and stock returns is concentrated among firms with low levels of diversity, suggesting limits to benefits from increasing diversity. Overall, this study provides initial evidence on costs and benefits of firm-wide diversity.
Jackson Pollock is one of the most influential Post-War American artists. The art critic Clement Greenberg claimed that Pollock’s drip paintings were the culmination of modern art. In this case study, we construct a novel dataset on the auctions of Pollock’s paintings from 1984 to 2023. We consider whether the artwork was sold or ‘bought in’ and explore the determinants of the auction hammer price correcting for sample selection bias. The robust method suggests that five variables explain 85.34% of the hammer price variation for the iconic artist’s paintings.
This study contributes to the growing literature on the determinants of Bitcoin volatility by examining its relationship with financial stress. Building on prior research linking Bitcoin volatility to broader economic and financial uncertainty, we employ a combination of regression analysis, a GARCH-MIDAS framework, and a Vector Autoregression (VAR) model to evaluate both the static and dynamic effects of financial uncertainty on Bitcoin. Preliminary regression results indicate that financial stress measures significantly and negatively predict Bitcoin volatility. The GARCH-MIDAS model confirms these results, showing a strong negative impact of financial stress on the long-term component of volatility. VAR analysis further reveals that Bitcoin volatility decreases in response to shocks in financial stress indicators. These findings highlight Bitcoin’s sensitivity to systemic financial conditions and carry important implications for risk management among cryptocurrency traders, institutional investors, and financial regulators.
This study investigates the effects of recent large U.S. regional bank failures on industrywide systemic risk as well as individual bank failure risk. We begin by using a logit model of individual bank failure risks to construct aggregate measures of systemic risk over time. Subsequently, mimicking bank supervisory practice, predicted systemic risks are estimated by mean reversion models. Lastly, using these estimates, we forecast the failure risk of individual banks. We find that, in response to increasing systemic risks, all bank experienced higher predicted failure risk. While regional banks were more affected than national banks, community banks were particularly sensitive to rising systemic risks. Future research on regulatory efforts to control systemic risk is recommended.
This study examines how research topics and abstract readability influence citation impact in finance journals. Using BERTopic modeling on over 7,000 abstracts from 13 top-tier journals, we identify four core themes—financial markets, banking & credit, corporate finance, and insurance & actuarial science—along with one interdisciplinary topic. Our results show that topic choice significantly affects citations: corporate finance attracts the highest average citations, whereas insurance & actuarial science receives the fewest. Interdisciplinary research has emerged as a major trend over the past decade. Abstract readability, measured by textual analysis, is also a key determinant: clearer abstracts are associated with higher citation counts. Page length, co-authorship, and journal impact factors further enhance citations. Overall, these findings underscore the importance of topic selection and highly readable abstracts in maximizing research visibility.
This study examines the impact of the government-business relations on urban greening investments in major Chinese cities. We find that the government-business index (GBI) is positively related to urban greening construction. However, the corruption sub-index within the GBI shows a negative relationship, suggesting the risk of vanity projects that prioritize officials’ visibility over genuine sustainability. Notably, the presence of hometown mayors weakens the positive effect of the GBI on urban greening construction. Moreover, while the GBI is associated with a reduction in green total factor productivity (GTFP), hometown mayors are shown to enhance GTFP, highlighting their significant roles in promoting sustainable urban development.
This study examines whether the linguistic complexity of corporate risk factor disclosures affects trading behavior. Using a sample of 8,297 firm-year observations from 1,489 unique firms spanning 2006-2024, the analysis examines the relationship between multiple readability measures and abnormal trading volume. The findings reveal that more complex disclosures are associated with reduced trading activity, consistent with theoretical predictions that complex information increases processing costs and reduces investor participation. The effect is economically meaningful, with a one-standard-deviation increase in complexity associated with a 42% reduction in abnormal trading volume for the median firm. The relationship is stronger for larger firms and has intensified over time, suggesting growing importance of disclosure clarity in modern markets.
Jackson Pollock is one of the most influential Post-War American artists. The art critic Clement Greenberg claimed that Pollock’s drip paintings were the culmination of modern art. In this case study, we construct a novel dataset on the auctions of Pollock’s paintings from 1984 to 2023. We consider whether the artwork was sold or ‘bought in’ and explore the determinants of the auction hammer price correcting for sample selection bias. The robust method suggests that five variables explain 85.34% of the hammer price variation for the iconic artist’s paintings.
Mandating the public reporting of Critical Audit Matters (CAMs) found during an audit changes when and how negative firm information is disclosed to market participants. These issues must be reported in the firm’s annual 10-K report starting in 2019. Relative to this regulatory change, we find that insiders at firms reporting CAMs purchase 50 million shares in the 60 days following a 10-K, doubling the amount purchased at these firms prior to the CAMs mandate. Consistent with our multivariate results, this implies that insiders change their trading patterns and purchase shares after the CAMs release date. There are also significant negative abnormal returns for purchases whose 90-day window overlaps with the CAMs release. This suggests that the market reacts negatively to CAMs information, and that insiders are utilizing their private knowledge of the forthcoming audit to shift their purchases to the period after CAMs are released to avoid significant negative returns.
This study examines the impact of stock split events on abnormal returns, identifying significant effects both short-term and long-term. We find that market sentiment plays a crucial role, with abnormal returns being more pronounced during high sentiment periods. Our research highlights two key contributions: it emphasizes the importance of market sentiment in stock price reactions to corporate events and supports the signaling hypothesis, suggesting that management uses stock splits to convey positive information, especially when sentiment is high. These findings are valuable for investors and corporate managers considering the implications of stock splits.
The increasing longevity of individuals, coupled with rising financial uncertainty, underscores the critical need for adequate household savings. Despite the importance of financial preparedness, nearly 50% of U.S. households nearing retirement (ages 55-64) lack sufficient savings (U.S. Federal Reserve, 2023). This study examines the relationship between life cycle variables, including expected longevity, financial knowledge, and health-related factors in shaping household savings. Using data from the 2022 Survey of Consumer Finances, we employ binary logistic regression to examine factors associated with household saving behavior. The Life Cycle Hypothesis (Ando & Modigliani, 1963) provides the theoretical foundation, extended to incorporate expected lifespan, financial knowledge and health related factors as key predictors. Findings reveal that households with high subjective financial knowledge have 71% higher odds of saving, while smokers have 30% lower odds of saving. Additionally, socioeconomic disparities were found to be significant, with single females and Hispanic households exhibiting lower savings rates compared to their counterparts. These findings underscore the need for targeted programs and policies that enhance financial literacy and health, promoting long-term saving habits and healthy lifestyles, particularly among vulnerable demographic groups. The study contributes to personal finance by integrating cognitive, health, and demographic influences into household saving decisions.
This paper estimates the term premium and equilibrium rates im- plied in the Brazilian yield curve, using a term structure model that incorporates data from survey expectations. Nominal long-term yields in Brazil are explained mostly by fluctuations in the equilibrium real rate.
The main purpose of this study is to empirically investigate the relationship between ETF flows and the volatility of NAV returns in Chinese ETF markets. Our empirical findings show that there is a positive relationship between ETF flows and the volatility of NAV returns. Additional analysis using flows–interaction terms shows that ETF demand and arbitrage flows are the main drivers of the volatility of NAV returns, compared to unexpected flows. From the analysis of IRFs, demand flow shock emerges as the most influential factor in long-term volatility compared to the other two shocks. Understanding the dynamics of flow-volatility can aid in designing regulatory frameworks that ensure market stability while promoting the advantages of ETF investments to market participants in order to reduce information asymmetry and maintain market efficiency.
This paper shows that climate uncertainty can help predict the size and direction of intraday jumps in green assets, both in and out-of-sample. Using tick data to capture the size and intensity of intraday jumps, we find that news that relate to transition climate uncertainty including international summits and climate policy, particularly those that could be interpreted as bad news for brown industries, are the most dominant predictors of jumps in green assets compared to proxies of physical climate risks. Our findings provide a novel perspective to the role of climate uncertainty as a driver of idiosyncratic tail risk and jump innovations in green assets and imply that pricing models that incorporate jump risk as a risk factor can be improved by exploiting the predictive power of climate uncertainty over jump dynamics.
At the start of the COVID-19 pandemic the increased market volatility and risk aversion led to a deterioration of U.S. Dollar funding conditions in the Euro Area. The swap line interventions by the ECB and Federal Reserve on March 15, 2020 aimed to alleviate the mispricing of EUR/USD FX swaps. We find that these swap line interventions were effective since they alleviated part of the mispricing. The announcement effect of the interventions is however limited; the impact of the swap line interventions is larger and more significant closer to the implementation date. This study provides insight into the effectiveness of central bank interventions in the FX swap market during turbulent periods.
We examine the information content of oil volatility-of-volatility (VOV), constructed from the past 1-month OVX (implied volatility in crude oil market), on the expected tail risk of commodities. Specifically, we find oil VOV predicts 1-step-ahead tail risks of Energy, Precious Metals, Agriculture, Livestock sectors and the Aggregate Commodity sector (GSCI) for both in-sample and out-of-sample. Our results indicate the important role of crude oil in overall commodity markets by incorporating forward-looking information of OVX. Our findings are robust and complement the strand of literature about the leading role of crude oil in commodity markets.
Since there are persistent concerns about the viability of euro area banks, we analyse their profit recovery in the post-crisis period, applying the concepts of β and σ convergence as well as the Phillips and Sul clustering algorithm. The results are consistent with ROE convergence, but to different levels across bank groups. The clustering analysis reveals the existence of banks with solid performance, but also a group of persistent underperformers. We find that non-interest income and operational efficiency emerge as crucial discriminating factors to explain the banks’ relative post-crisis ROE dynamics. Supervisors and bank managers are advised to monitor and reinforce bank business model viability.
