“The implications of these findings are potentially far-reaching for investors and for researchers.”
– page 31, 2nd paragraph, 1st sentence
In “Core Earnings: New Data and Evidence”, Ethan Rouen and Charles C.Y. Wang of Harvard Business School (HBS) and Eric So of Massachusetts Institute of Technology (MIT) Sloan School of Management use our “novel database” of earnings adjustments to reveal market inefficiencies and show investors how to:
- Measure & predict earnings more accurately
- Lower risks of using misleading data from traditional providers
- Generate alpha
“…this is the most comprehensive dataset that captures what a fundamental analyst would be likely to identify as transitory or non-operating earnings items…”
– page 9, 2nd paragraph, 2nd sentence
Only our research utilizes the superior data and earnings adjustments featured by the paper.
This paper shows how our superior data and research helps you:
- Pick better stocks
- Avoid losses from using other firms’ data
- Build better cash flow & valuation models
- Exploit market inefficiencies
- Fulfill fiduciary duties
Don't just take our word for it. See what HBS and MIT Sloan professors say in the paper:
Pick better stocks:
“Trading strategies that exploit cross-sectional differences in firms’ transitory earnings produce abnormal returns of 7-to-10% per year.” – Abstract, 4th sentence
“The findings…suggest that firms in the highest decile of Total Adjustments outperform firms in the lowest decile by approximately 9-10% in the year after firms’ 10K filings.” – page 29, 3rd paragraph, 2nd sentence
Avoid losses from using other firms’ data:
“…many of the income-statement-relevant quantitative disclosures collected by NC do not appear to be easily identifiable in Compustat…” – page 13, last paragraph, 1st sentence
“To further explore Compustat’s treatment of non-recurring items that appear on the income statement, we examined a random sample of 30 firm-years that reported economically meaningful items on their income statements to determine if and where Compustat reported these items. In all instances, NC identified the items as non-operating, and Core Earnings includes adjustments for these items. In 10 of the firm-years, the item was not reported in any Compustat variable; the other 20 items were reported in 13 different variables.” – page 14, 1st paragraph, 4th-7th sentences
Build better models:
“Core Earnings [calculated using New Constructs’ novel dataset] provides predictive power for various measures of one-year-ahead performance…that is incremental to their current-period counterparts.” – page 3-4, 3rd paragraph, 2nd sentence
“Because of the comprehensive nature of NC’s approach to identifying non-operating and transitory income-statement related items, and because of its status as an independent research firm, the resulting measure of core earnings is less likely to exhibit the systematic bias that has been found in managers’ pro-forma earnings.” – page 2, 2nd paragraph, 4th sentence
Exploit market inefficiencies:
“These results suggest that the adjustments made by analysts to better capture core earnings are incomplete, and that the non-core items identified by NC produce a measure of core earnings that is incremental to alternative measures of operating performance in predicting an array of future income measures.” – page 26, 1st paragraph, 2nd sentence
“Analysts and market participants are slow to impound the implications of transitory earnings.” – page 1, Abstract, 3rd sentence
“…these figures show that the NC dataset provides a novel opportunity to study the properties of non-operating items disclosed in 10-Ks, and to examine the extent to which the market impounds their implications.” – page 19, 2nd paragraph, last sentence
Fulfill fiduciary duties:
“An appropriate measure of accounting performance for purposes of forecasting future performance requires detailed analysis of all quantitative performance disclosures detailed in the annual report, including those reported only in the footnotes and in the MD&A.” – page 31, 2nd paragraph, 4th sentence
“These findings also suggest that ‘Income before Special Items’ is not a good measure of operating or core income.” – page 21, 2nd paragraph, 5th sentence
“…Core Earnings is a superior accounting measure of a company’s operating earnings, and incremental to other measures when predicting future performance.” – page 25, 1st paragraph, 1st sentence
Conclusion: Have Your Cake and Eat It Too
Now everyone can benefit from more accurate fundamental data and rigorous research without spending hours scouring financial filings.
This HBS and MIT Sloan paper shows that our Robo-Analyst technology delivers materially better data that can improve stock-picking. Unconflicted and comprehensive fundamental research is finally available to all who care to make it a part of their investing process.
This article originally published on October 11, 2019.
Disclosure: David Trainer, Kyle Guske II, and Sam McBride receive no compensation to write about any specific stock, sector, style, or theme.
 Harvard Business School features the powerful impact of our research automation technology in the case New Constructs: Disrupting Fundamental Analysis with Robo-Analysts.