Public Signals of Python-Enabled AI in Finance: Disclosure Patterns and Outcome Claims in NYSE Institutions

🌐 Research Ecosystem This Zenodo record forms part of a broader open-science initiative focused on artificial intelligence, financial technology, computational finance, and reproducible research. Resource Access 🐙 GitHub Repository https://github.com/vdrakopoulou/nyse-python-ai-disclosure-replication 📦 Replication Package https://doi.org/10.5281/zenodo.18646800 📄 SSRN Working Paper https://ssrn.com/abstract=6267458 👩‍🔬 Research Profile https://github.com/vdrakopoulou 🏛️ Affiliation Higher Colleges of Technology (HCT) 🎓 Academic Collaboration Embry-Riddle Aeronautical University ✨ Why This Repository Matters Modern financial institutions increasingly communicate their technological capabilities through fragmented public signals rather than standardized disclosures. This repository provides a transparent, reproducible framework for studying how Python-enabled AI capabilities are represented in public-facing corporate information. The replication package includes: Component Purpose Data Processing Pipelines Cleaning and harmonization of evidence sources Disclosure Classification Framework Systematic coding of AI-related disclosures Python Library Mapping Identification of AI technology ecosystems Statistical Analysis Scripts Reproducible empirical workflows Figure Generation Automated creation of publication-quality visuals Documentation Full methodological transparency ⚠️ Repository Scope Notice This repository contains a testing and demonstration sample of the analytical framework developed for the research project Public Signals of Python-Enabled AI in Finance: Disclosure Patterns and Outcome Claims in NYSE Institutions. The code, sample datasets, workflows, and documentation included in this archive are intended to illustrate the methodological approach and support transparency and reproducibility. They do not represent the complete research dataset, full evidence corpus, or final analytical environment used in the study submitted for academic publication. Repository Contents Availability Demonstration Code ✅ Included Sample Data ✅ Included Example Workflows ✅ Included Documentation ✅ Included Full Research Dataset ❌ Not Included Complete Evidence Corpus ❌ Not Included Final Publication Materials ❌ Not Included Peer-Review Files ❌ Not Included The full research project remains under scholarly evaluation and publication consideration. Accordingly, this repository should be viewed as a representative research sample rather than the complete publication archive. Open Science Statement All materials are released to support transparency, reproducibility, and cumulative scientific progress. Researchers, students, practitioners, and policymakers are encouraged to inspect, reproduce, extend, and build upon the analytical framework provided in this repository. 🔗 Repository GitHub: https://github.com/vdrakopoulou/nyse-python-ai-disclosure-replication ⭐ Citation & Reuse If this repository contributes to your research, teaching, or professional work, please cite the associated paper and Zenodo archive. Contributions, forks, issue reports, and replication studies are welcome.