Providing Data on Financial Results of Public Companies Enriched with Provenance for OBInvest
Authors: Saulo A. Almeida, Gilberto Gil F. Gomes Passos, Valquire da S. de Jesus, Sérgio Manuel Serra da Cruz, Jorge Zavaleta
Venue: WWW
Year: 2023

Financial Literacy (FL) initiatives, aimed at young people in formal or informal learning spaces, are defended and implemented in several countries, being encouraged since 2005 by the Organization for Economic Co-operation and Development (OECD). In Brazil, the teaching and learning process in several areas has been stimulated through Academic Competitions generally called Knowledge Olympics, which are essentially student contests that aim to encourage, find talent and awaken interest in the field knowledge presented in the competition. It was precisely for this purpose that the Brazilian Investment Olympics (OBInvest) was born, aiming to democratize access to education and promote reflections on economic and financial issues, through a FL perspective for high school students from all over the country. One of OBInvest’s objectives is to help boosting the development of computational tools, aiming to provide easier access to fundamental data for decision-making in the field of finance. However, from the tools developed by OBInvest, it was noted that the creation of new educational tools would be enhanced through the use of datasets enriched with provenance and aligned with FAIR principles. This work aims to offer a computational strategy based on data science techniques, which is easy to use and also provides curated data series through a reproducible pipeline, using open data on financial reports from publicly listed Brazilian companies, provided by the Brazilian Security and Exchange Commission, called Comissão de Valores Mobiliarios (CMV). During the exploration of related works, we found just a few academic works that use CVM data with little expressive results, which motivated the development of a tool called DRE-CVM, that was supported by computational tools, with a focus on the Python language, Pandas library, the KNIME workflow platform, and Jupyter integrated development environments, running on the Anaconda3 platform over a Docker container. It’s also possible run this experiment in the Google Colabotory cloud environment. This processing it’s capable of executing reproducible pipelines and using curated, fairified, and annotated data with the retrospective source metadata of the financial statements of publicly traded Brazilian companies. The artifact uses pipelines that can be reused by students and other interested parties in finance to study the behaviors of a company’s time series results and thus introduce research on predicting future results. The last executable version of the DRE-CVM experiment can be accessed through Zenodo website at https://doi.org/10.5281/zenodo.7110653 and can be reproduced using a Docker Container available on DockerHub repository. Some improvements can be incorporated into the presented work, the main suggestions for future work are: (i) Perform more substantial analyses on the created dataset, such as predicting results based on the history of demonstration results; (ii) Recover other types of information made available by CVM, to be used during the activities of the Brazilian Investment Olympics; (iii) Adapt the docker image so that it can be executed in the My Binder cloud environment, aiming to improve reproducibility issues.