This paper explores the effectiveness of various machine learning algorithms in predicting financial fraud and recidivism using a hand-collected dataset of cases prosecuted by UK financial regulators from 2010 to 2022. The study aims to identify key factors for predicting financial fraud and recidivism. Comparative analysis of machine learning algorithms, including Logistic Regression, Ridge Regression, Support Vector Machine, Decision Tree, Random Forest, Artificial Neural Network, and Adaptive Least Absolute Shrinkage and Selection Operator, reveals that the Random Forest model consistently outperforms others in AUC and precision rate for both financial fraud and recidivism. The study identifies crucial factors, including financial factors such as leverage, firm size, market value, and tangibility, and non-financial factors such as firm age, tone, gender ratio, and the number of directors, contributing significantly to financial fraud detection. The insights provide valuable guidance to accountants, independent directors and regulators for developing effective early warning systems for financial fraud.
This paper investigates the moderating role of the information environment in depicting the relation between peer leverage and corporate capital structure. In addition, we exploit the MiFID II regulatory shock in a quasi-experimental difference-in-differences design to test whether the peer-effect slope (i.e. firms' leverage sensitivity to peer leverage) changes after the reform. Past research suggests that firms align their capital structures with their industry peers. However, it remains silent on the role of analyst coverage in moderating this relationship. Using panel data from 2011 to 2024, we study firms in five major European economies: France, Germany, Italy, Spain, and Sweden. We find that peer effects are significant but vary across countries. Analyst coverage robustly attenuates peer alignment, promoting fundamentals-based decisions. Crucially, the MiFID II regulatory shock, which prompted a contraction in analyst coverage, amplified peer leverage comovement in the EU. These outcomes offer a deeper understanding of when and why firms imitate peers and underline actionable implications. Practitioners and policymakers should recognise analyst coverage as a governance tool that limits excessive imitation, highlighting the importance of research support to sustain corporate diversity and financial resilience.
The study examines the impact of corporate risk culture and board gender diversity on bank risk behaviour. Using the competing value framework, we assess the corporate risk culture of 120 US banks and bank holding companies for 2004-2023. Our empirical evidence shows that board gender diversity, when interacting with risk culture, reduces bank risk. It further confirms that the presence of female directors influences the risk culture when the number of women reaches a critical mass. These findings also demonstrate that greater board gender diversity has led to lower bank risk levels in the post-2009 period. The findings are robust to alternative proxies for bank risk, risk culture, and to sub-sample analysis. The results support the regulatory stance of 'tone from the top' as an important driver of corporate culture affecting bank risk.
In multiple asset classes, the pricing implications of salience theory on cross-sectional asset returns are clouded by informational overlaps with short-term past returns. Leveraging the delta-neutral nature of the delta-hedged option returns, this paper documents the first unclouded evidence of salience theory's impact in the options market. We introduce a new option-based salience theory (OST) value and find its strong negative predictive power for cross-sectional option returns: a long-short portfolio sorted by OST generates a monthly return spread of -0.57% and an annualized Sharpe ratio of 1.86. Our findings support the hypothesis that investors overweight salient past option returns, resulting in the overvaluation of high-OST options. Furthermore, the option salience effect cannot be explained by stock-based short-term reversal or past-month option returns, and is stronger when investor sentiment is high, limits to arbitrage are elevated, and volatility is heightened.
Using the implementation of China's green factory policy as the institutional setting, which allows firms to apply for government certification as green factories (GF), we examine how firms that are not GF-certified (focal firms) respond to their GF-certified peers within the same industry in the context of green innovation. Consistent with the crowding-out effect explanation, we find that the green innovation of focal firms is negatively associated with the number of green-certified peers. Our results remain robust across alternative measures of patent quality and a series of additional robustness checks. Channel analyses indicate that reduced access to government subsidies and procurement orders, along with greater financial constraints and higher cost of debt, help explain these findings. Overall, green-certified firms leverage government support and their superior resources to crowd out focal firms. Our findings provide a novel perspective on how GF-certified firms influence the green innovation of industry competitors, thereby highlighting the unintended negative externalities of the green manufacturing policy.
This research examines the impact of corporate culture on trade credit financing. Utilizing a Word2Vec model on MD&A texts from Chinese publicly listed firms, we uncover a positive and significant relationship between corporate culture and trade credit financing-validated by various endogeneity and robustness tests. Our findings indicate that corporate culture mitigates financial distress risk, information asymmetry, and supplier concentration, thereby enhancing trade credit financing. These positive effects are more pronounced in firms with shorter supplier relationships, greater geographic distance, reduced board diversity, state ownership, and in regions characterized by lower Confucian values, more market-driven economies, and stronger law enforcement. Moreover, five sub-dimensions of corporate culture-integrity, respect, teamwork, innovation, and quality-as well as cultural similarity, are positively associated with trade credit. This study provides novel evidence that corporate culture plays a crucial role in shaping firm-supplier relationships and facilitating trade credit financing.
This study explores the relationship between new female director appointments and managerial ability for a sample of 18,216 US firm-year observations representing 1,885 distinct publicly traded firms. Using a two-stage instrumental variable, we find that new female director appointments are associated with a decrease in managerial ability. This effect is robust to alternative measures, endogeneity corrections, and matched sample approaches. However, while the consequences of new female director appointments also include reductions in labor productivity and cash flows, firms do benefit from an increase in intangible assets. In addition, with time, experience, and support, firms can profit from the appointment of a new female director.
This study investigates whether adding macroeconomic variables and uncertainty indices improves the performance of ensemble bankruptcy prediction models for France's manufacturing and construction sectors. Starting from a baseline using only financial ratios, we estimate separate specifications, each adding one input: macroeconomic variables, the French Economic Policy Uncertainty index, the French Geopolitical Risk index, or a new French Google Trends-based uncertainty index. The results show that incorporating macroeconomic variables significantly improves out-of-sample predictive performance. For the uncertainty measures, each index delivers incremental improvements in accuracy relative to the ratios-only baseline. Notably, the Google Trends-based index yields gains comparable to those from the macroeconomic set, positioning this search engine-based measure as a promising predictor of bankruptcy risk. These insights offer practical value for corporate boards, financial analysts, lenders, and policymakers seeking to strengthen bankruptcy risk assessment during periods of elevated economic and geopolitical uncertainty.
This study investigates the extent to which credit quality and risk aversion influence open banking adoption and data-sharing behaviours among UK consumers. Contrary to credit signalling theory, we find that credit quality plays little role in adoption decisions within the UK context, providing no support for the view that consumers strategically reveal creditworthiness through open banking participation. Risk aversion emerges as the dominant behavioural barrier to open banking adoption and to consumers' willingness to share financial data, both with traditional financial service providers and financial technology firms. The effect of risk aversion on data-sharing intentions persists irrespective of consumers' open banking adoption status, and operates through heightened privacy and security concerns, including apprehensions about data usage, storage, financial loss, and identity theft. We find pronounced heterogeneity across institution types, indicating that consumers' data-sharing decisions are contingent on the perceived nature of the institutional counterparty. Risk aversion exerts a consistently negative influence across the open banking ecosystem. Additionally, we find that open banking adopters appear financially vulnerable, reporting lower financial satisfaction, reduced ability to cope with income shocks, and greater difficulty managing financial commitments, raising important questions about who open banking serves and carrying direct implications for consumer protection regulations.
This study evaluates the performance of global, regional, and hybrid asset pricing models across 67 countries, employing the most refined regional classification to date, which captures both geographic and developmental distinctions. The results indicate that hybrid and regional models consistently outperform global models, which generate the largest pricing errors. Sub-period analyses reveal no evidence of increasing dominance of global factors, showing that full financial integration has not yet been achieved. The best-performing individual model varies by region. Regional models are more effective in segmented markets, while hybrid models perform better in partially integrated ones, indicating that model effectiveness depends on the degree of regional integration. Factor-spanning tests indicate that the market factor is the most globalized, followed by momentum, while investment remains largely regional. A novel globalization measure highlights high integration in North America, Western Europe, and developed Asia-Pacific, contrasted with persistent segmentation in MENA, Latin America, and Eastern Europe.
Banks operate branch networks that span multiple geographic markets. Building on theories of economies of scale, this paper examines how overlap in banks' branch networks across markets affects cost efficiency. Using a panel of 866 U.S. bank holding companies from 1986 to 2024, we measure branch network overlap based on the similarity of banks' branch presence across metropolitan statistical areas (MSAs). Fixed-effects estimates show that banks with greater branch network overlap exhibit significantly higher cost efficiency. Complementary instrumental-variable and robustness analyses yield qualitatively similar results. Mechanism analyses suggest that overlapping branch networks enhance cost efficiency primarily through the exploitation of economies of scale facilitated by spatial proximity. The paper contributes to the literature by extending scale economy theories to the context of banking studies and by introducing a new multi-bank measure of branch network overlap. From a managerial and policy perspective, the results inform decisions on branch expansion, consolidation, and the assessment of geographic competition in banking markets.
Accurately identifying the determinants of oil spot prices remains a persistent challenge. This paper proposes a spillover-based approach to information-set selection, in which candidate system specifications are ranked based on their internal interconnectedness rather than via individual variable screening. Conceiving markets as dynamically evolving information networks, we implement the architecture of Total Spillover Index (TSI) to quantify the transmission of shocks across variables within candidate systems. We then construct, within a cointegration framework, alternative models representing Brent and WTI markets from both isolated and globally integrated perspectives. Spillover analysis shows that systems that incorporate global market indicators exhibit very strong interconnectedness and respond more sensitively to macroeconomic shocks, as reflected in their co-movement with the Global Economic Policy Uncertainty Index. Out-of-sample forecasts using both Fractional Cointegration Vector Autoregressive (FCVAR) models and Long Short-Term Memory networks show that a hybrid global specification consistently outperforms models that are restricted to isolated markets, particularly at medium and longer horizons. These results suggest that information coherence, capturing persistent cross-variable transmission within the system, provides a useful criterion for identifying forecasting-relevant information-sets in complex market environments such as global oil markets.
We propose a mean quadratic variation (MQV) framework for portfolio selection as an alternative to the classical Markowitz mean-variance (MV) paradigm. Instead of measuring risk by terminal return variance, the MQV framework employs quadratic variation, a pathwise and time-additive measure of return fluctuations. This mod-ification addresses two longstanding limitations of the MV framework: the sensi-tivity of covariance-based optimization in high-dimensional settings and the time-inconsistency of multi-period portfolio choice. The proposed model is straightforward to calibrate, as quadratic variation and quadratic covariation between assets can be directly estimated from realized return paths. Moreover, the additive structure of quadratic variation yields time-consistent optimal portfolio strategies in a discrete-time multi-period setting. We derive closed-form optimal portfolio weights, characterize the corresponding efficient frontier, and develop a quadratic variation-based capital asset pricing model. Extensive empirical backtests and simulation experiments show that MQV portfolios achieve competitive or improved out-of-sample performance relative to their MV counterparts across several asset universes. Overall, the results suggest that pathwise risk measurement provides a tractable and economically meaningful alternative to variance-based portfolio optimization.
This study investigates the impact of climate risks on the financial stability of banks using a comprehensive panel dataset of European banks covering the period from 2006 to 2021. We find that greater exposure to climate risk significantly undermines bank stability, with transition risk exerting a particularly strong destabilizing effect. This impact is especially pronounced among larger banks and those with stakeholder-oriented governance models, and it intensifies following the adoption of the Paris Agreement. Our findings reveal a non-linear relationship whereby higher levels of climate risk exposure lead to disproportionately greater financial instability. Further analysis shows that these effects are driven by heterogeneity in bank-specific characteristics and country-level factors. We also find that stricter climate-policy regimes, particularly during advanced phases of the EU Emissions Trading System, strengthen the adverse effect of transition risk on financial stability. This study underscores the importance of integrating climate risks into prudential regulation to enhance the resilience of the banking industry to climate-related shocks.
This study investigates the causal impact of hedge fund activism (HFA) on market liquidity. The empirical results show that HFA leads to a deterioration in stock liquidity, with the effect being more pronounced in firms characterized by greater information asymmetry and financial constraints. The decline in liquidity is also more evident in cases of high-intensity campaigns, led by funds with weaker market reputation, and that engage more frequently in activist interventions. Additional analyses reveal that price efficiency, corporate information flow, and operating complexity contribute to liquidity decline. This evidence holds using several liquidity metrics and sensitivity tests, and we rule out any potential endogeneity concern using an exogenous setting in our Difference-in-Differences regression analysis. Overall, this study underscores the disruptive influence of HFA on corporate dynamics and its wider market repercussions.
The advancement in digital and information technology (DIT) has profound effects on individuals, corporations, and society. The benefits of DIT, such as enhanced efficiency, easily access to information, and increased connectivity, are clear. However, scholars have raised various concerns regarding the plethora of information. For example, how DIT affects investors' information acquisition behavior is unclear. Competing theories offer conflicting predictions in investor information acquisition. These predictions have different implications regarding whether investors acquire more information about local firms (local attention bias), versus about non-local firms, when information becomes easily accessible. Our empirical results show that as DIT develops, investors pay more attention to local firms, amplifying local attention bias. Economic development and a better developed institutional environment amplify rather than attenuate local attention bias. Mediation analysis further shows that DIT development increases attention co-movement and stock return correlation not only directly but also indirectly through local attention bias as a mediator. Our novel evidence suggests that when information is more easily accessible associated with DIT development, information asymmetry can be amplified when agents can choose what to learn, increasing polarization of information acquisition and selective exposure to information.
This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.
We examine how climate-related (transition and physical) risks impact European bond markets and inflation expectations, and identify their effects across distinct volatility regimes using a Markov-switching vector autoregression model. Our central finding is that the transmission of climate-related risk shocks is highly state-dependent and primarily affects short-term inflation expectations. Transition risks have a limited, disinflationary effect on short-term expectations, but only during low volatility periods. In sharp contrast, physical risks exert a destabilising, inflationary impact during high volatility periods, depressing bond returns and amplifying market stress. Additionally, we observe two more patterns: first, that long-term inflation expectations tend to remain largely anchored. Second, financial linkages and contagion tend to intensify in the high volatility state. Our findings matter for asset pricing and for monetary authorities. They support integrating climate-related risks into stability frameworks, as these shocks presumably intensify and complicate the trade-off between inflation-target credibility and financial stability.
We examine the association between cryptocurrency environmental attention and cryptocurrency bubbles. Our results indicate that environmental attention is positively associated with the probability of a cryptocurrency bubble and ranks as the second most important explanatory factor. The positive association is more pronounced for smaller, less-mature, and proof-of-work (PoW) cryptocurrencies, indicating that cryptocurrency characteristics are important determining factors of bubble formation.
