PROJECT "X"-CROSS FUNDAMENTAL DATA
Complex and extensive data acquisition projects inspire us the most. In particular, the collection and preparation of data from unstructured data sources, which only simple algorithms or even AI systems rarely lead to a qualitative result.
Our project "the digitization and standardization of fundamental financial data" is an excellent example of this. The data sources are annual reports (PDF files), which vary greatly from report to report. Once the financial fundamental reports (balance sheets, income statements, cash flow statements, etc.) have been located in the annual report, we encounter the next challenge; inconsistent presentation. The different tables and their different structures make it difficult to digitize this data 1:1.
The next challenge is enabling comparability of this data. It is difficult for a quantitative analyst to work with this data. Here we encounter, among other things, accounting legal challenges in terms of their comparability but also general structural and logical differences.
Examples of this are the differences between the cost of sales method and the total cost method in the income statement. Or the direct and indirect method in the cash flow statements. The fundamental reports of two different industries can also differ greatly. Banks place a greater focus on certain metrics compared to a manufacturing industry. Thus, their financial fundamental reports differ as well. Even the same company can change its structure from one year to the next.
Individual international interpretations and a certain freedom in the presentation of fundamental reports reinforce this effect.
After almost two years of technical and professional development and many adjustments, we have launched the product XFData and are able to offer financial fundamental raw data in digital original and standardized form with the unique "X-Cross" feature. We have successfully solved all the above challenges and countless other problems, achieving unprecedented simplicity and transparency in the quantitative comparison of fundamental data.