Nov 2026· Journal of Sustainable Finance & Investment· 67 references
Financial Markets and Investment Strategies
Abstract
We explore the impact of artificial intelligence (AI) workforce adoption on shareholder value in the context of climate change, using stock market reactions to the Paris Agreement as a natural experiment. Based on a large sample of publicly listed U.S. companies and leveraging a novel measure of AI-skilled employees derived from large-scale resume data, we provide empirical evidence that AI workforce adoption significantly enhances cumulative abnormal returns during this critical policy event. Firms with greater climate change exposure derive smaller benefits from AI adoption, likely because of competing priorities between investing in AI initiatives and addressing climate-related strategies. Moreover, AI adoption enhances R&D efforts, signaling strong innovation potential, but raises concerns about resource allocation in highly profitable firms. Finally, we demonstrate that abnormal returns attributable to AI workforce adoption predict long-term firm value, emphasizing AI's strategic importance in driving shareholder value and adaptability in a climate-conscious economy.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
