This blog is a follow-up to our research paper “Machine Readable Filings (MRF) Word Count Alpha” which is an extension of Harvard Lazy Prices. This blog focuses on word count, sentiment factors and changes in those factors associated with regulatory filings.
SMA partnered with S&P Global Market Intelligence to provide textual data in U.S. SEC filings organized by headings with textual data underneath (i.e. Parts, Items). Textual data is parsed to create historical baselines for 10-Ks, 10-Qs, and other regulatory filings. There are 20 filing types in the MRF product; this paper analyzes 10-Ks and 10-Qs to focus on quarterly changes.
Subscribers of the MRF dataset can create derivative metrics stemming from the seven factors. For instance, one metric explored in this paper is Sentiment per Word. That factor is calculated by dividing Sentiment Sum by Word Count. This and other derivative factors are calculated to normalize sentiment based on document length.
Methodology
This analysis looks at the Quarter-over-Quarter changes in regulatory filings. Each 10-K and 10-Q is compared to the most recent 10-K and 10-Q from the same company. The Percent Change in Word Count is the difference between the word count in two filings divided by the word count of the previous filing.
The Universe used for this analysis includes all securities over $5. The benchmark used, called ‘Universe’, is the average return of all stocks in any Quintile portfolio at that point in time. The analysis begins in 2007 and concludes in July 2022.
When computing calendar-time portfolio returns, stocks are selected into buckets depending on the factor or change in that factor. Stocks enter the portfolio on the last market day of the month the report was released. Portfolios are rebalanced monthly to introduce new filings submitted in the most recent month.
Results
The graphs and metrics below are calendar-time portfolio returns. Quintile 1 contains stocks with the lowest factor value while Quintile 5 encompasses stocks with the highest factor value.
