José Antonio Muñiz, PhD

Understanding the use of unconventional monetary policy for portfolio decarbonisation in Europe

Muñiz, J. A., Corbet, S. and Larkin, C.

Journal of International Money and Finance · 2025 · Vol. 50

This study examines the European Central Bank's (ECB) use of unconventional monetary policy to decarbonise its balance sheet, focusing specifically on the inherent trade-offs between monetary stability and environmental sustainability. We investigate how the ECB's green asset purchases, as part of its Corporate Sector Purchase Programme (CSPP), affect the liquidity of its portfolio compared to similar assets not held by the ECB. While these assets align with green objectives, they exhibit significantly lower liquidity than their non-green counterparts. This discrepancy highlights a fundamental tension within the ECB's policy framework: the commitment to environmental sustainability may undermine liquidity and, by extension, the overall effectiveness of monetary policy. Our findings suggest that the ECB's strategy to integrate environmental considerations into its operations, though successful in supporting greener initiatives, poses challenges for maintaining strong liquidity levels. The study underscores the need for innovative financial instruments and strategies to reconcile the dual objectives of promoting environmental sustainability and ensuring monetary stability.

Systemic Risk Transmission to Energy Futures: Weekend Information Gaps and the Breakdown of Pricing Efficiency

Conlon, T., Corbet, S., Larkin, C. and Muñiz, J. A.

Journal of Future Markets · 2026

This study examines the efficiency of systemic risk transmission to international oil futures markets by analysing the dynamic connectedness between three distinct Common Volatility (COVOL) measures: Energy, Asset, and Country, and compares such with five major oil benchmarks. Utilising a framework that combines TVP-VAR, EGARCH, and wavelet coherence analyses, we investigate whether non-trading weekend breaks create a structural barrier to the pricing of systemic risk. Our findings identify a significant Monday effect, characterised by a pronounced decoupling between systemic risk signals and oil futures prices. The effect is highly state-dependent: during the COVID-19 pandemic, the disconnect dissipated for Energy and Asset COVOL but intensified for Country COVOL, while geopolitical conflicts extended the breakdown of the signal into Tuesday. These results indicate a hierarchy of influence in which country-level systemic risks exert the strongest effect on oil markets.

The Influence of Employee Health and Safety Policies on the Value of the Firm

Muñiz, J. A., Corbet, S. and Larkin, C.

Journal of Safety Research · 2026 · Vol. 97

This study investigates the impact of Health and Safety (H&S) policies on the valuation of over 2,000 United States firms, where results indicate the existence of a significant positive value elevation as a result of successful H&S Employee Policies implementation, with evidence of moderate variation partly attributed to the influence of the Patient Protection and Affordable Care Act (ACA). Crucially, the study establishes that the observed impact of H&S policies on firm value is not a result of an implicit reaction to policy implementation due to the COVID-19 pandemic, but a consistent trend over a prolonged period, underscoring the strategic importance of H&S policies in corporate valuation. The study presents a series of tests that demonstrate the particular benefit of H&S Policy in enhancing the value of firms.

Market Perceptions of ESG Reputational Risk in the US Pharmaceutical Industry

Erdinc, A., Corbet, S., Muñiz, J. A. and Scrimgeour, F.

Corporate Social Responsibility and Environmental Management · 2026 · Vol. 33

Negative ESG-related reputational events generate significant corporate risks, particularly within sensitive sectors such as the pharmaceutical industry. Using novel reputational data, this research investigates investor perceptions of the consequences of experienced ESG breaches among US pharmaceutical firms. Specifically, we consider the magnitude, timing, and persistence of abnormal returns, testing whether firm-specific characteristics and event-related attributes moderate and account for identified market response differentials. Results indicate the presence of significant negative abnormal returns before the identified media release date, suggesting market anticipation or information leakage, followed by a pronounced negative shock upon formal announcement, with firm size the most robust mitigating factor. Market response shows substantial heterogeneity, while environmental incidents generate significant, delayed negative returns, whereas social and governance events show negligible investor response, indicating a lack of market concern. Companies experiencing recurring incidents experience further deterioration of returns than first-time offenders. Neither the initial news source's reach nor the assessed severity significantly affects the magnitude of market response. These findings highlight the context-dependent nature of ESG materiality in the pharmaceutical sector

What do we know about fraud detection in Peer-to-Peer Lending? A Systematic Literature Review

Machado, M., Coita, I. I., (et al., including Muñiz, J. A.)

SSRN · 2024

Crowdfunding platforms have gained popularity as a means of financing entrepreneurial initiatives but face a high risk of fraud. Fraud is a significant problem due to its impact on trust, ultimately leading to financial instability. Detecting and preventing fraud is therefore paramount for the sustainability of crowdfunding platforms. This study provides a systematic review of the literature and state-of-the-art discussions about crowdfunding fraud. Unsupervised topic modeling highlights that both AI and blockchain are recurrently presented in the literature as effective methodologies for identifying and preventing fraudulent practices. Furthermore, this work describes current market practices of crowdfunding platforms in preventing fraudulent behavior and argues that, while fraud is rare, its high impact necessitates new and innovative forms of fraud detection. A key limiting factor for the application of AI solutions is the lack of available labeled crowdfunding data for training efficient algorithms for fraud detection, which is crucial as it constitutes an anomaly detection machine learning task. In this context, unsupervised machine learning methods are discussed as valuable techniques for detecting anomalies in the absence of labeled fraud cases due to their ability to adapt to evolving fraud patterns. Altogether, this research provides valuable insights into the complexity of detecting and preventing fraudulent activities in crowdfunding and highlights effective detection techniques that, if implemented, offer promising solutions to enhance platform reputation and ensure regulatory compliance.

Investor Sanctions for Greenwashing: Evidence from Fintech Firms

Corbet, S., Muñiz, J. A. and, Staunton, D.

This paper examines the stock market's ability to detect and penalise greenwashingmisrepresentations of environmental responsibility-among fintech firms. We document price declines in fintech firms publicly implicated in greenwashing, with the effect more pronounced in smaller firms and firms with low book-to-market ratios. Additionally, in the two days preceding such news releases, a negative price effect is present, indicating information leakage or anticipatory selling. Contrary to expectations, direct environmental misbehaviour does not systematically elicit larger reactions. The results imply that equity markets can provide market-based penalties in the absence of formal enforcement. The results also highlight the complexity of investor decision-making in contexts where firms are particularly opaque.

Common Global Volatility Shocks and State-Dependent Transmission to Cryptocurrency Markets

Conlon, T., Corbet, S., Muñiz, J. A. and Staunton, D.

Does AI Investment Really Improve Equity Performance? AI Job Creation and Firm Value: Lessons from the S&P 500

Muñiz, J. A., Larkin, C. and Garcia-Lazaro, A.

This study examines the impact of the creation of AI job vacancies on firm value within the S&P 500 firms. Our study highlights the role of AI adoption and strategic investments in human capital. With a unique dataset from Lightcast and Compustat, we analyse more than 3.4 million job spanning from 2022Q3 to 2024Q1. Our findings reveal a significant positive relationship between AI job postings and firm value, measured through Tobin's Q (0.27%), Return on Equity (ROE) and Return on Assets (ROA) metrics. Firms advertising AI-related roles signal their commitment to innovation, aligning workforce strategies with technological advancements, which investors perceive as a driver of competitive advantage and growth. Robustness tests, including propensity score matching and instrumental variable approaches, confirm the validity of these findings. Furthermore, we create and index based on the AI jobs adverts per firm where our results also show that firms, independently of the sector, that have more AI job postings have a significant positive association with its firm value. Our research contributes to understanding how AI talent acquisition influences firm valuation and market perceptions, offering novel insights into workforce dynamics and technological innovation.

Strategic Transparency: The Impact of Early Sustainability Reporting on Financial Performance

Muñiz, J. A., Larkin, C. J. and Corbet, S.

This study investigates the financial implications of disclosing socially responsible information by analysing a sample of 2,857 publicly traded companies in the United States immediately after the Paris Agreement came into force. The results show a clear financial advantage for firms reporting sustainability information, with those reporting before the Paris Agreement experiencing significantly stronger financial performance than their non-reporting counterparts. This 'reporting premium' highlights the criticality of timely reporting when capturing the benefits of positive market reception to such transparency. Such analysis diverges from conventional reliance on ESG ratings by directly correlating voluntary disclosure practices with elevated financial performance, thereby identifying a tangible advantage in proactively meeting investors' growing demand for transparency. Results highlight the increasing relevance of aligning with investor expectations, advocating for broader adoption of transparency in non-financial reporting, and presenting evidence that cloaking or delaying disclosures generates comparative underperformance.

The Financial Costs of Corporate Social Irresponsibility in the Beverage-Producing Industry

Corbet, S., Cameron, M. and Muñiz, J. A.

This study investigates the financial market implications of negative ESG-related reputational events within the beverage industry, analysing abnormal stock returns surrounding 11,632 distinct incidents between 2007 and 2023. Significant negative abnormal returns are identified, emerging up to ten days prior to public disclosure through media dissemination, suggesting strong information leakage. Investor response upon media announcement is immediate, negative, and asymmetric, often followed by rapid mean reversion, indicative of potential ESG fatigue and heterogeneity of response as a result of event perception by investors. Further, this effect is found to be moderated by both event-related factors and corporate characteristics. Interestingly, repeat ESG offenders are found to experience significantly more negative returns than first-time offenders, suggesting the presence of market learning, declining market intolerance with poor corporate behaviour and escalating accountability expectations. Such results clearly demonstrate the tangible financial consequences of corporate social irresponsibility, providing particular evidence of a particularly nuanced market response, signalling both investor desensitisation to ESG events and particular learnings from corporate ESG history.

Echoes of Innovation: Abnormal Market Returns Surrounding GenAI Releases

Conlon, T., Corbet, S., and Muñiz, J. A.

Generative Artificial Intelligence (GenAI) has proliferated rapidly, resulting in widespread market valuation ambiguity due to forecasting uncertainty. This research examines investors' response to major GenAI releases and developments, focusing specifically on the cumulative abnormal returns of US publicly traded corporations as a bellwether for investor expectation and revaluation. The results indicate that GenAI announcements constitute and incorporate risk factors outside of traditional market estimation benchmarks, acting in a compensatory manner due to the inherent ambiguities of the new technology and the unproven speculative potential. Evidence of substantial heterogeneity in market responses is presented, contingent on firm characteristics, where smaller corporations with lower profitability levels and reduced leverage are associated with significantly stronger positive post-announcement CARs, suggesting that investors initially perceive GenAI's value additivity in terms of efficiency gains and competitive catch-up potential rather than scale-dependent advantages. Sector-specific analysis reveals particularly distinct anticipatory pre-announcement trading in the technology and finance sectors. Pronounced market enthusiasm is identified when considering the release of GPT-3, which is compared with muted responses to subsequent GenAI models, providing strong evidence of rapid market learning.