Quarterly earnings season may be winding down, but the real earnings season – annual 10-K filing season – is ramping up.

Quarterly earnings reports provide investors with limited and often misleading data. In “Core Earnings: New Data and Evidence,” professors from Harvard Business School (HBS) and MIT Sloan empirically demonstrate that corporate managers manipulate earnings by burying gains & losses in footnotes. Only by reading all of the financial footnotes and MD&A, which are only in annual 10-K reports, can investors analyze true core earnings.

This report highlights one firm that looks more profitable than its GAAP results indicate and demonstrates how GAAP net income growth doesn’t lead to increased market cap.

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Footnotes Help Us Find Undervalued Companies

After analyzing its 65 page 2019 10-K last week, Kimberly-Clark (KMB)[1]  looks even more profitable than GAAP earnings suggest.

Here’s why: the following hidden unusual expenses cause GAAP earnings to understate core earnings:

Each of these charges are related to KMB’s 2018 Global Restructuring Program. In addition, we found the following reported non-operating expense:

  • $91 million in nonoperating expense reported on the income statement – Page 24

The company reported $2.2 billion in GAAP net income, but our adjustments show that they earned $2.5 billion in net operating profit after-tax (NOPAT) and $2.3 billion in core earnings in 2019. After uncovering these details, we upgraded KMB to a Very Attractive rating due to its improved profitability and undervalued stock price.

All the media buzz during “earnings” season tends to push investors into shortsighted trades. We help long-term investors create a durable competitive advantage by reading thousands of 10-Ks so they can invest with the level of diligence they’ve always wanted but could not get without the help of our new Robo-Analyst technology.

GAAP Earnings Don’t Affect Valuation – In the Long Term

Figure 1 shows that GAAP net income growth since 2014 has almost no impact on the change in market cap for companies in the S&P 500 since 2014.[2]

Figure 1: GAAP Net Income Growth and Change in Market Cap Since 2014

Sources: New Constructs, LLC and company filings.

Finding Truth in the Footnotes: ABT vs TXT

Note that Abbott Laboratories (ABT) and former Danger Zone pick Textron Inc. (TXT) have each grown GAAP net income by 44% since 2014. However, ABT’s market cap has more than doubled while TXT’s has fallen by 8%, per Figure 2.

Figure 2: ABT & TXT’s GAAP Net Income Growth Vs. Change in Market Cap Since 2014

Sources: New Constructs, LLC and company filings.

Non-operating income and expenses included in operating earnings distort both firms’ GAAP earnings growth. For instance, ABT’s 2018 GAAP net income understated the true profitability of the firm due to:

  1. $111 million in net hidden non-operating expense
    1. Includes items found only in the footnotes such as $37 million in restructuring charges recorded in SG&A and $32 million in amortization charges related to inventory step-ups
  2. $777 million in net reported non-operating expense included in operating earnings

Over the trailing twelve months (TTM), we identified $27 million in net hidden non-operating expense and $524 million in net reported non-operating expense that cause ABT’s GAAP earnings to understate NOPAT.

On the other hand, Textron’s 2018 GAAP net income overstated its true profitability due to:

  1. $15 million in net hidden non-operating income related to prior service cost (credit)
  2. $444 million in reported non-operating income related to the sale of a business segment

Over the trailing twelve months, we identified $15 million in net hidden non-operating income and $26 million in net reported non-operating income that cause TXT’s GAAP earnings to overstate NOPAT.

When we strip out accounting distortions, ABT’s NOPAT has grown by 87% since 2014 while TXT’s NOPAT is flat over the same time, per Figure 3.

Figure 3: ABT & TXT’s NOPAT Growth Vs. Change in Market Cap Since 2014

Sources: New Constructs, LLC and company filings.

Cash is a fact. Earnings are an opinion. Investors who base their investment decisions on accounting earnings put their portfolios at risk. Advisors who make investment recommendations without performing proper due diligence are not fulfilling their fiduciary responsibilities.

Leveraging Machine Learning to Provide Footnotes Diligence at Scale

ROIC is much better than GAAP earnings at explaining changes in valuation. Unfortunately for investors, it is very difficult to calculate accurately. It’s not enough to read financial statements. A rigorous calculation of ROIC must account for items that are buried in hundreds of pages of footnotes.

For a human analyst, performing this level of analysis on just a handful of companies is a daunting task. Applying that level of rigor to thousands of companies is downright impossible – until now. The professors of the HBS & MIT Sloan paper mentioned earlier said of our database of footnotes adjustments:

“To our knowledge, this is the most comprehensive dataset that captures what a fundamental analyst would be likely to identify as transitory or non-operating earnings items in detailed analyses of firms’ 10- Ks.” Pp. 9-10

We use machine learning and natural language processing to automate the analysis of corporate filings. Our statistical comparison engine, which has been trained on over 120,000 human-verified filings and grows more sophisticated every day, can filter through SEC filings to recognize and tag important data, automatically building company models.

Of course, human analysts remain a vital part of the process. From mid-February through the end of March, our expert team of analysts will be coming in early and staying late to validate the data and models built by the machine and interpret unusual items that cannot be automatically processed.

This combination of computerized processing power and human expertise allows us to provide investors with the most accurate research from the “real” earnings season. Harvard Business School featured the powerful impact of our Robo-Analyst technology in the case study “New Constructs: Disrupting Fundamental Analysis with Robo-Analysts.” This paper compares our analytics on a mega cap company to Bloomberg and Capital IQ (SPGI) in a detailed appendix.

Best of all, our partnerships with TD AmeritradeE*TRADERefinitiv/Thomson ReutersInteractive Brokers and EY enable us to deliver our investment ratings and research on over 10,000 stocks, ETFs and mutual funds to millions of self-directed investors, financial advisors, and corporate executives.

This article originally published on February 21, 2020.

Disclosure: David Trainer, and Kyle Guske II, and Matt Shuler receive no compensation to write about any specific stock, sector, style, or theme.

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[1] Kimberly-Clark (KMB) was originally featured as a Long Idea in November 2017 (+27% vs. S&P 500 up 30%).

[2] Figure 1 excludes companies for which we don’t have data going back five years or whose GAAP net income five years prior is negative. After these exclusions, the regression analysis contains 467 companies.

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