著者

Mustafa SAMI KACAR Semih YUMUSAK Halife KODAZ

出版者

The Institute of Electronics, Information and Communication Engineers

雑誌

IEICE TRANSACTIONS on Information and Systems (ISSN:09168532)

巻号頁・発行日

vol.E106-D, no.4, pp.477-487, 2023-04-01

被引用文献数

1

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Companies listed on the stock exchange are required to share their annual reports with the U.S. Securities and Exchange Commission (SEC) within the first three months following the fiscal year. These reports, namely 10-K Filings, are presented to public interest by the SEC through an Electronic Data Gathering, Analysis, and Retrieval database. 10-K Filings use standard file formats (xbrl, html, pdf) to publish the financial reports of the companies. Although the file formats propose a standard structure, the content and the meta-data of the financial reports (e.g. tag names) is not strictly bound to a pre-defined schema. This study proposes a data collection and data preprocessing method to semantify the financial reports and use the collected data for further analysis (i.e. machine learning). The analysis of eight different datasets, which were created during the study, are presented using the proposed data transformation methods. As a use case, based on the datasets, five different machine learning algorithms were utilized to predict the existence of the corresponding company in the S&P 500 index. According to the strong machine learning results, the dataset generation methodology is successful and the datasets are ready for further use.