1816 papers
This study develops a Net Captured Value framework to demonstrate that optimizing multi-barge routing flexibility in offshore well intervention campaigns can significantly increase economic value by up to 112% through strategic prioritization and timing, rather than simply maximizing well coverage.
This paper provides evidence that the release of Generative AI, specifically ChatGPT, reduced investors' processing costs for complex financial disclosures, thereby attenuating the post-earnings-announcement drift primarily for firms with high information complexity and low institutional ownership, which supports the costly processing explanation over competing risk or arbitrage-limit theories.
Using a unique firm-project-level dataset spanning 185 countries from 2003 to 2023, this study demonstrates that renewable energy foreign direct investment significantly accelerates the global power sector's green transition by increasing renewable electricity generation and employment, with effects that are strongest for wind and solar projects in high-income economies and through expansion initiatives rather than new investments.
Using data from Chinese listed firms (2007–2022), this study demonstrates that Confucian culture positively influences corporate cash dividend payouts by enhancing altruism, alleviating financing constraints, and reducing agency costs, with these effects being particularly pronounced in non-state-owned firms, competitive industries, and regions with fewer foreign cultural shocks.
This paper demonstrates that for regional trade accounts, where economic structures change slowly, the value of new data collection is often low compared to leveraging existing benchmarks, as old direct observations frequently outperform sophisticated estimators using current data and much of the apparent decay in aged benchmarks stems from measurement regime shifts rather than actual economic changes.
This paper finds that equity markets do not currently price the risk of AI vendor concentration because mandatory disclosure rules are absent, leading to inconsistent and often missing information in public filings that prevents investors from distinguishing between firms that rent versus those that own their AI models.
This study analyzes panel data from 15 Ethiopian commercial banks (2012–2022) to reveal that credit risk and operational inefficiency significantly impair financial performance (ROA and ROE), while capitalization and bank size exhibit mixed effects, underscoring the need for stricter credit appraisal, cost efficiency, and optimal capital management.
This paper proposes a hybrid graph machine learning framework that integrates dynamic stock correlation networks with Transformers to enhance portfolio optimization by leveraging multiscale topological dependencies, resulting in improved risk-adjusted returns and interpretable allocation signals across both Chinese and American markets.
Using firm-level data from 1,435 Brazilian industrial enterprises, this study demonstrates that the innovation benefits of digitalization emerge only when firms adopt a comprehensive, complementary portfolio of six advanced technologies rather than isolated tools, suggesting that systemic digital transformation is a more stable strategic commitment than piecemeal adoption.
This paper employs an evolutionary game-theoretic model to demonstrate how truthful but selectively visible, aspirational outcomes in matching markets distort agents' perceptions of attainability, leading to excessive high-target search, reduced commitment, and a decline in durable match formation despite individual adaptiveness.