NASHVILLE, TN / ACCESSWIRE / November 19, 2019 / New Constructs, the leading provider of insights into the fundamentals and valuation of private and public businesses, today announced an update to its Oct. 15 announcement of Harvard Business School (HBS) and Massachusetts Institute of Technology (MIT) Sloan School of Management’s findings that markets inefficiently assess core earnings because too few investors read footnotes, which include a steadily increasing number of material unusual gains/losses. The professors used New Constructs’ AI-powered Earnings Distortion Scorecard to reveal the first-ever empirical evidence that corporate managers are biased when reporting earnings and exploit footnotes to manipulate quarterly results.
The New Constructs dataset solves a very big problem for investors: how to get an accurate measure of profits. In the past quarter, New Constructs’ Earnings Distortion Scorecard accurately predicted earnings beats and misses for major publicly traded equities including AmerisourceBergen, Qualcomm, CVS, AbbVie, Dupont de Nemours, and Lam Research.
New Constructs’ founder and CEO David Trainer commented, “There’s a new landscape for fundamental data and research. We now have proof that we can’t just trust the numbers analysts or executives give us. Money managers and advisors have a fiduciary duty to provide advice based on the true earnings or put their clients at undue risk. On the bright side, we are seeing the democratization of access to higher-quality research based on this new technology. Individual investors can now get the same unvarnished data as large institutional investors.”
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 used New Constructs’ “novel dataset” to analyze disclosures in corporate 10-Ks, including those hidden in the footnotes and the MD&A. They show that disclosures of non-operating income-statement items are both frequent and economically significant, and increasingly so over time. Further, core earnings and street earnings from other well-known financial, statistical and market information databases do not properly account for such unusual items and are subject to significant bias, which creates more risk for investors.
New Constructs’ technology brings critical and material footnotes research to the market for the first time ever, enabling analysts to measure and predict profits more accurately and deliver more alpha for clients. The Earnings Distortion Scorecard captures all the non-recurring adjustments featured in the HBS and MIT Sloan paper and provides daily updates to registered users of the system.
About New Constructs
New Constructs provides insights into the fundamentals and valuation of private and public businesses. Combining human expertise with natural language processing (NLP), machine learning (ML) and artificial intelligence (AI) technologies, the firm’s research shines a light in the dark corners (e.g., footnotes) of hundreds of thousands of corporate financial filings to unearth critical details that drive uniquely comprehensive and independent debt and equity investment ratings, valuation models and research tools. New Constructs’ technology brings critical and material footnotes research to the market for the first time ever, enabling analysts to measure and predict profits more accurately and deliver more alpha for clients. Elite money managers, advisors and institutions have relied on New Constructs to lower risk and improve performance since 2004. New Constructs and its research have been featured in national business news including CNBC, The Wall Street Journal, Barron’s, Forbes, Seeking Alpha, Benzinga and more. Strategic content partnerships with TD Ameritrade, E*TRADE, Refinitiv/Thomson Reuters, Interactive Brokers and EY enable New Constructs 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.
SOURCE: New Constructs, LLC
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