Novel Alpha From Earnings Distortion: CloudQuant
Earnings Distortion is orthogonal to all currently known value and profitability factors.
Earnings Distortion = Reported Earnings – Core Earnings
Idiosyncratic Alpha: 60% Return with 85% Pure Alpha
To enable managers to easily monetize our new factor, we worked with CloudQuant to develop two initial strategies:
- The dollar-neutral long-short portfolio returned 60% over 10 years with a Sharpe Ratio over the last five years of ~1.
- The long-only portfolio outperformed the S&P 500 index by an average of 4% per annum over 10 years and averaged 18.4% per annum.
Get the details in the research paper prepared by CloudQuant.
Get the Paper
Proprietary Data: Never Available to the Market Before Now
Core Earnings: New Data & Evidence, a new paper in The Journal of Financial Economics, unequivocally proves that the market does not impound the earnings impact of our proprietary analysis of footnotes disclosures.
Professors from Harvard Business School (Charles Wang and Ethan Rouen) and MIT Sloan (Eric So) wrote the paper. Here are a few quotes:
“market participants are inefficient in impounding the implications of non-core earnings, especially those stemming from the footnotes of the 10-K, into stock prices.” – pp. 34, 3rd para.
“Core Earnings contains information about future performance that is incremental to Street Earnings” – pp. 29, 2nd para.
Get a summary of the 70+ page paper here.
Conclusion
This new factor and our Core Earnings data merit your attention because they:
- Generate significant, scalable and idiosyncratic alpha, and
- are based on proprietary data.
This article originally published on September 13, 2021.
Disclosure: David Trainer, Kyle Guske II, Alex Sword, and Matt Shuler receive no compensation to write about any specific stock, style, or theme.
Analyst's Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours.
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