Novel Alpha from Truth Stocks & Earnings Distortion: AltHub

Earnings Distortion is orthogonal to all currently known value and profitability factors.

Earnings Distortion = Reported Earnings – Core Earnings

Idiosyncratic Alpha with Earnings Distortion & Truth Stocks

To enable managers to easily monetize our new factor, we worked with AltHub to develop three initial strategies:

1. Earnings Distortion S&P 500 Smart Beta Portfolio: 10-yr annualized return of 13.9% vs 12.1% for the S&P 500 with a Sharpe Ratio of 0.97.
2. Earnings Distortion ML Model S&P 500 Smart Beta Portfolio: 3-yr annualized return of 18.2% vs 13.7% for the S&P 500 with a Sharpe Ratio of 0.82.
3. Truth Stocks (Companies without Earnings Distortion) Portfolios:
   1. S&P 500 Universe: 10-yr annualized return of 16.8% vs 12.8% for the S&P 500 with a Sharpe Ratio of 0.7 and
   2. Russell 3000 Universe: 9.5-yr annualized return of 28.4% vs 12.6% for the S&P 500 with a Sharpe Ratio of 1.

We find it reassuring, in the meme-stock environment, to see proof that the market likes companies that tell the truth about their earnings.

Get the details in the research paper prepared by AltHub.

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, 3^rd 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:

1. Generate significant, scalable and idiosyncratic alpha, and
2. are based on proprietary data.

Get more papers that show how quantitative and fundamental portfolio managers can leverage our proprietary Core Earnings data to generate more alpha and improve their performance.

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.

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Click here to download a PDF of this report.