I took a quick look to see if any of the top forensic algorithms commonly used by certified fraud examiners show any unique signals ahead of the accusations released on GE (NYSE:GE). Part of my regular stock analysis and selections include articles of the most positive and negative forensic stocks in the market: Forensic Stock Analysis For August: Do You Own These Stocks?
I also used certain combinations of forensic scores to predict stock price performance like Tesla's (TSLA) recent 40% decline:The 3 Largest Stocks Now Scoring Worse Than Tesla Before Its 40% Decline.
These models are designed to flag certain elements where examiners could look deeper to assess conditions of a company. I would expect some irregularities to show up on the heels of such a strenuous accusation against GE. Certainly other methods and models were brought to bear in the Markopolos' analysis, but curiosity compels me to see what the resulting scores are for General Electric. The full Harry Markopolos, CFA, CFE, report is available on Scribd here.
A key assertion from the Whistleblower Report spans from 2012 through 2018:
"We calculated GE should have taken a reserve hit as early as 2012, and certainly no later than 2015, but they waited until new management came in and booked what little reserve they could afford in late 2017/early 2018, a $15 billion commitment that they had to request a special exemption from the Kansas Insurance Department (KID) to spread over a seven-year period because, plainly put, GE isn’t liquid right now and likely won’t survive long enough to make their last few years of reserve payments anyway."
Certainly a $15 billion alteration from 2012 with application to the books in 2017 could be a strong contributor to conditions in the forensic scores above. The activity documented in 2017 seems to be the main contributor to the shift in forensic scores from 2016 to 2017.
Between 2016 and 2017 you can see substantial changes in scores across all the forensic algorithms, even the deep value analysis Piotroski F-score. The Beneish M-score that was previously credited with detecting Enron's irregularities only turned negative (red) in 2017 and has remained in relative low scoring conditions from 2012 through today.
Oddly, the Montier C score was the only indicator to improve in condition in 2017 and more recently in 2019 has turned more adverse than in prior years. This algorithm is intended to detect manipulation in the financial statements.
Most notably the Ohlson O-score that sets a probability for bankruptcy over the next two years jumped from 7% in 2016 to over 54% in 2017. This marked the most significant deterioration across any of the scores and it corresponded to the time in question highlighted in the Whistleblower Report.
Beyond the period of 2016/2017 there's no strong signal of further accounting or bankruptcy changes in scores, while the Altman Z score and the Ohlson O score have remained in adverse warning conditions for at least several years. The GE forensic scores do not look particularly adverse across all the measures and showed some improvement from the 2017 shift.
Markopolos claims there's still more to come and that remains to be seen:
GE’s $15 billion LTC reserve hit was a nasty market surprise and it’s about to get $29 billion worse.
I believe I have a few smoking guns on GE,” Markopolos told Yahoo Finance’s The Final Round, adding that information was held back from the report for law enforcement. ~ Yahoo Finance
As of this point in time there's no strong signal beyond the Altman Z-score and Ohlson O-score that are trending more positively to suggest forensic conditions have become increasingly worse.
We may discover shortly how these figures bear out in the books with more investigators and regulators analyzing the claims.
These models are certainly not foolproof and were designed by academic researchers to improve the chance of detection of irregularities leading to bankruptcy, earnings manipulation, or flag the presence of financial distress.
At the same time, these models are among the best peer-reviewed forensic models in the financial literature and have some significant documented value.
The Beneish model for example has "correctly identified, in advance of public disclosure, a large majority (71%) of the most famous accounting fraud cases that surfaced after the model's estimation period." (Beneish, Lee, & Nichols, 2013, p. 57).
I trust the insight from these different forensic and value algorithms will give you added value to your investment goals and objectives in the days ahead.
JD Henning, PhD, MBA, CFE, CAMS
Altman, E. I. (1968). The Prediction of Corporate Bankruptcy: A Discriminant Analysis. The Journal of Finance, 23(1), 193-194. doi:10.1111/j.1540-6261.1968.tb03007.x.
Beneish, M. D. (1999). The Detection of Earnings Manipulation. Financial Analysts Journal, 55(5), 24-36. doi:10.2469/faj.v55.n5.2296.
Beneish, M. D., Lee, C. M. C., and Nichols, D. C. (2013). Earnings Manipulation and Expected Returns. Financial Analysts Journal, 69.2, 57-82.
Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109. doi:10.2307/2490395.
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