skip to main content
Close Icon We use cookies to improve your website experience.  To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy.  By continuing to use the website, you consent to our use of cookies.
Global Search Configuration

Data Simply Financial Scores: Getting bang for one’s buck

EPFR has joined forces with Data Simply to provide investors with better and faster ESG and financial data insights. This blog post will be the first in a series which looks at the added value of ESG Financial Scores.

Firstly, Data Simply analyzes corporate SEC filings by word count through a combination of artificial Intelligence and analyst expert. Key words are identified and classified from filings as either positive, neutral or negative. The total word count per sentiment is counted for each company and published as a total financial score on a monthly basis for companies that file with the SEC.

For greater clarity, Data Simply additionally publish the breakdown components of these total financial scores as positive, negative and neutral financial scores. All these scores are normalized, with assigned integer values between 1 and 100.

In this blog post, we investigate whether Data Simply’s offering adds ‘more bang for your buck’. The EPFR team back-tested each of these scores, on a sector-neutral basis, on both the Russell 1000 and 2000. The analysis uses monthly holding periods from March 2016 through the end of 2018.

Table 1.0

Within each period, for each factor, we divided each universe into fifths (quintiles) within sectors, so that each sector is equally represented in each quintile. Table 1.0 below shows annualized equal-weight quintile returns, for each universe and factor, in excess of that universe’s equal-weight return. The annualized Sharpe ratio shown is that associated with each factor’s quintile spread on each universe, with the ratio between the returns to the top and bottom quintiles.

As you can see from the table, Data Simply’s total financial score has predictive power on both the Russell 1000 and 2000 indices. As you might expect, the results do a better job discriminating amongst the smaller stocks. The negative financial score, also as expected, underperforms, with companies scoring highly on that factor proceeding to do poorly, on average, over the forward month. Interestingly, perhaps because they are more vulnerable, the negative financial score plays a bigger part amongst small companies than larger ones.

Now that we’ve established this model works in general, investors can review individual stock level with Data Simply scoring, here’s an example - the biopharmaceutical company, Dynavax Technologies. Chart 1.0 below shows cumulative return together with monthly positive and negative scores for this stock.


As you can see from the chart, early on, when the negative score outweighed the positive score, the stock tanked. As more positive words surfaced in the filings, it rose again, until finally succumbing to a torrent of negative words.

Data Simply also ranks companies on ESG, but that is another blog…stay tuned.

Follow our ESG blog series over the next 5 weeks, where we will focus on five major sectors, top companies within major sectors and more.

If you have any questions, please contact us at:


Any questions? Speak to a specialist

Would you like to request sample data or analysis from Informa Financial Intelligence? 

See how our tailored solutions can help you gain a competitive advantage: