Using text analytics, we turn millions of textual disclosures into governance insights. The data we provide has been quantitatively verified by academic research and explainable by fundamental investment principles.
Our Data Sets
The key rationales behind our data sets are driven by fundamental investing principles, in that a logical fundamental investor will weigh these factors in their research and eventual portfolio selection. At the same time, the alpha-generating capability of these data sets has been quantitatively verified by academic research. The result is high efficacy data sets that are well understood, allowing for better appreciation of sources of alpha and risks.
The Similarity Index quantifies the textual differences between a given company's annual or quarterly filings on an "as disclosed" basis. Academic research has shown that a portfolio that shorts low similarity scores and longs high similarity scores earns non-trivial and uncorrelated returns over a period of 12-18 months.
Companies regulated by the SEC are required to file a Non-Timely notification when they are unable to file their annual or quarterly disclosures on time. The data set records the date in which a firm files a Non-Timely notification with the SEC.