Accepted for publication by The Journal of Financial Economics, Core Earnings: New Data & Evidence proves the superiority of our fundamental data, Core Earnings models, and securities research. Below are key quotes from the paper.
Professors from Harvard Business School (Charles C.Y. Wang & Ethan Rouen) and MIT Sloan (Eric So) wrote the paper. The Journal of Financial Economics is one of the top 3 peer-reviewed journals in the world.
Better data than S&P Global (SPGI):
- “[New Constructs’] Total Adjustments differs significantly from the items identified and excluded from Compustat’s adjusted earnings measures. For example… 50% to 70% of the variation in Total Adjustments is not explained by IBSPI Adjustments, OIADP Adjustments, or OPE Adjustments individually.” – pp. 14, 1st para.
- “A final source of differences [between NC and SPGI] is due to data collection oversights…we identified cases where Compustat did not collect information relating to firms’ income that is useful in assessing core earnings.” – pp. 16, 2nd para.
New, unique factors for new alpha:
- “Trading strategies that exploit non-core earnings produce abnormal returns of 8% per year.” – Abstract, 5th sentence
- “…analysts and other market participants are slow to impound the implications of the distinction between core and non-core earnings, especially those disclosed from the footnotes section of the 10-K” – pp. 35, 1st para.
- “These costs [of analyzing footnotes] point to the potential for increasing inequities in the usefulness of financial statements for sophisticated versus unsophisticated investors who differ in their technological capabilities for processing 10-K information” – pp. 35, 2nd para.
Buried treasure found in footnotes:
- “Roughly half of these [non-core-earnings] items, by frequency and magnitude, are disclosed off of the income statement. These findings suggest that individuals seeking to understand the composition of GAAP earnings need to process a large amount of information disclosed in various parts of the 10-K.” – pp. 3, 2nd para.
- “Economic and statistical significance are largest for adjustments found in the footnotes, where information tends to be less salient and less structured.” – pp. 5, 1st para.
Better, faster, cheaper fundamental data from machine learning:
- “… the machine [NC’s Robo-Analyst technology] learned and replicated human analysts’ judgements based on their prior decisions. It did so with greater speed and scale to produce a database covering a broad cross-section of firms.” – pp. 9, 2nd para.
Better earnings models & forecasting:
- “Our results show that Core Earnings contains information for future Net Income that is incremental to contemporaneous Net Income, as well as other adjusted earnings measures” – pp. 26, 2nd para.
Less bias than Analysts Estimates:
- “Because an independent research firm produces the underlying data, …[it] is less likely to exhibit systematic biases found in street earnings or pro-forma earnings.” – pp. 3, 1st para.
- “…manager-reported non-GAAP earnings are identical to the street earnings reported in IBES, which suggests general agreement between analysts and managers on how to adjust GAAP earnings to reflect core operating performance. This consensus raises the possibility that managerial bias that could be reflected in pro forma earnings is also reflected in street earnings.” – pp. 16, footnotes.
This article originally published on December 9, 2020.
Disclosure: David Trainer, Kyle Guske II, and Matt Shuler receive no compensation to write about any specific stock, style, or theme.