Sep 2026· Advances in Economics, Management and Political Sciences· 0 citations
Abstract
Since the launch of ChatGPT in late 2022, generative AI has become a major focus for global capital markets. However, the market reactions to AI-related announcements of listed companies are very heterogeneous, and the underlying mechanisms are still underexplored. In this study, this paper analyze AI announcements by firms in a fixed list of S&P 100 constituents over the period 2023–2024. Researchers test, using event study methods and cross-sectional regressions, whether implementation intensity and technological capabilities are associated with short-term market reactions. The mean cumulative abnormal return (CAR) for the [-5, +5] window is 3.0%, which is statistically different from zero at the 5% level and is based on 26 announcements with complete return data. This paper find that implementation-oriented announcements are associated with CARs that are about 4.6 percentage points higher than intention-oriented announcements, although the effect is only marginally significant at the 10% level and becomes insignificant under alternative specifications. The implementation hypothesis is thus supported in a suggestive but not robust way. The interaction of implementation stage with R&D intensity is not significant. Hence, the technological-capability hypothesis is not supported. Overall, the observed implementation evidence seems to be associated with more positive market reactions within the short window, but the small sample and the sensitivity of the results to the specification urge caution in interpretation.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first o…
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
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