A pipeline has been developed to extract executive compensation data from SEC filings, specifically targeting Summary Compensation Tables within DEF-14A proxy statements. Utilizing MinerU for parsing PDFs and extracting table images, along with Qwen3-VL-32B for classifying and structuring the data, the project addresses challenges such as tables spanning multiple pages and format variations between pre- and post-2006 filings. Although still in development with some bugs, the pipeline aims to compile a comprehensive dataset of executive compensation from 2005 to the present for all US public companies. This initiative is crucial for improving transparency and accessibility of executive compensation data, potentially aiding research and analysis in corporate governance and financial studies.
The development of a pipeline to extract executive compensation data from SEC filings marks a significant advancement in financial data analysis. By leveraging technologies like MinerU and Vision Language Models (VLMs), this project addresses the complex task of parsing and categorizing data from DEF-14A proxy statements. These documents are crucial for understanding executive pay structures, which have implications for corporate governance, shareholder interests, and regulatory compliance. The ability to automate this extraction process allows for more efficient and comprehensive analysis, providing stakeholders with valuable insights into how executives are compensated across various industries.
One of the key challenges tackled in this project is the handling of tables split across multiple pages and the differences in document formatting before and after 2006. This is important because these variations can significantly complicate data extraction efforts, leading to potential inaccuracies or incomplete datasets. By developing a system that can adapt to these changes, the pipeline enhances the reliability of the extracted data. This is crucial for researchers, investors, and policymakers who rely on accurate data to make informed decisions about executive compensation practices and their broader economic impacts.
The use of advanced technologies like Qwen3-VL-32B for classifying and extracting structured data from tables represents a cutting-edge approach to data processing. This model can effectively discern which tables are relevant compensation tables, streamlining the data collection process. The structured JSON output facilitates easier integration with other data analysis tools, enabling more sophisticated analyses and visualizations. This technological advancement not only improves the efficiency of data extraction but also opens up new possibilities for analyzing trends and patterns in executive compensation over time.
While the pipeline is still a work in progress with some bugs, such as duplicate tables and occasional parsing errors, the ongoing development and availability of the code and dataset samples invite collaboration and improvement. By sharing these resources on platforms like GitHub and HuggingFace, the project encourages community engagement and innovation. This collaborative approach can lead to further refinements, ultimately resulting in a robust tool that can serve as a valuable resource for anyone interested in the intricacies of executive compensation. The potential for this project to enhance transparency and understanding in corporate governance underscores its importance in the financial and regulatory landscape.
Read the original article here
Comments
2 responses to “Pipeline for Extracting Executive Compensation Data”
The use of MinerU and Qwen3-VL-32B for extracting and classifying data from DEF-14A proxy statements is a smart approach to overcoming the challenges of varied table formats. It’s exciting to see a project focused on enhancing transparency in executive compensation, which could significantly benefit corporate governance research. How do you plan to address the bugs in the pipeline to ensure the dataset’s accuracy and reliability?
The project aims to enhance the accuracy and reliability of the dataset by continuously refining the parsing algorithms and incorporating feedback from test runs to address bugs. Regular updates and testing are suggested to ensure that the pipeline adapts to variations in table formats and improves over time. For more detailed insights, please refer to the original article linked in the post.