This forensic value analysis began with a check of four different algorithm scores of Tesla Inc. (TSLA) at the request of members of my service back in October 2018. The question they asked at the time was, "what risk does Tesla Inc. pose as an investment?" In an ad hoc query of top forensic algorithms and a well-known value model I obtained very surprising results. The set of adverse scores on Tesla at that time were shared by no other company with a market cap over $1 billion across any of the US exchanges.
That struck me as a very significant finding and became even more interesting when Tesla subsequently declined by more than 40% from its peak in December.
I have since dubbed this set of forensic and value scores shown in the table above as the "Tesla Test" and run periodic reviews for any large-cap stocks that have reached such previously adverse levels. The following selections show other stocks with equal or worse scores even more recently that signal a failing score on the "Tesla Test."
Previous Results of the "Tesla Test"
Three sets of stocks since the Tesla Test back in October have been screened on this forensic and value screen and were published on May 22, 2019, July 3, 2019, and Aug. 22.
I. On May 22, the first three companies that screened as the largest firms to meet or exceed all the adverse values of Tesla from last October were Clovis Oncology (CLVS), Sorrento Therapeutics (SRNE), and TG Therapeutics (TGTX).
From the original article for May 22:
The results of these three stocks as of August 22nd (3 months later):
CLVS has declined -65.2% to $5.95/share.
SRNE has declined -36.2% to $2.29/share.
TGTX has declined -4.85% to $6.47/share.
II. On July 3, 2019, the second group of the three largest stocks with scores as bad or worse than Tesla at its December top were McDermott International (MDR), Centric Brands Inc. (CTRC) and Portola Pharmaceuticals (PTLA).
The results of these three stocks as of Aug. 20 (nearly two months later):
MDR declined -54.6%, CTRC declined -14.46%, and PTLA gained 11.25% over that time period. These three companies averaged a -19.27% decline using the same adverse forensic values that signaled a prior top for Tesla.
The Current 3 Largest Stocks of the "Tesla Test"
Remarkably, when I ran the test for this article only one stock with a market cap over $1 billion made the list with a similar or worse combination of scores than TSLA registered last October. That also was the same case when TSLA was selected. That finding may be highly significant in itself for identifying future high risk outliers. Also noteworthy is that only 25 stocks across all the exchanges achieved such adverse scores. All the stocks beyond the three largest selected were below market caps of $300M and as low as $1M market cap with share prices as low $0.05.
The current screen of the largest companies with failing scores on this forensic/value test in August are Precision BioSciences (DTIL), Portola Pharmaceuticals (PTLA) and Sorrento Therapeutics (SRNE). Both PTLA and SRNE are prior selections from earlier this year that still show adverse conditions.
Precision BioSciences (DTIL)
Precision BioSciences, Inc. operates as a genome editing company and develops therapeutic products in the United States. It operates through two segments, Therapeutic and Food. The company offers ARCUS, a genome editing platform to cure cancers and genetic disorders. The Therapeutic segment develops allogeneic CAR T immunotherapy that recognizes and kills cancer cells, and engages in the in vivo gene correction activities. This segment develops PBCAR0191, an allogeneic CAR T cell therapy targeting the tumor target CD19 for acute lymphoblastic leukemia and non-hodgkin lymphoma; and CD20, BCMA, and CLL-1, CAR T cell therapies targeting the tumor antigens.
DTIL has had net purchases by insiders of nearly +$1.7M in the past year.
With institutional investors the active positions have increased while more positions have been sold than bought in the most recent quarter: (Source: Nasdaq.com)
Portola Pharmaceuticals (PTLA)
Portola Pharmaceuticals, Inc., a biopharmaceutical company, develops and commercializes novel therapeutics in the areas of thrombosis and other hematologic disorders and inflammation in the United States. The company offers Andexxa, an antidote for patients treated with rivaroxaban and apixaban when reversal of anticoagulation is needed due to life-threatening or uncontrolled bleeding, and Bevyxxa (betrixaban), an oral, once-daily Factor Xa inhibitor for the prevention of venous thromboembolism in adult patients hospitalized for an acute medical illness.
PTLA has had net increase in purchases by insiders of over $7M in the past year. However, all the most recent transactions have been sales of over $3M.
Institutional investors have been net sellers in the most recent quarter, while active institutional owners have been increasing positions overall.
Sorrento Therapeutics (SRNE)
SRNE has had net sales by insiders of nearly -$1.5M in the past year.
Standard Caution with all Forensic Assessments
It's important to stress that firms identified by these academic models may not be in actual distress or suffer from any adverse irregularities whatsoever. 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).
Further, in a survey of 169 chief financial officers of public companies, Dichev, Graham, and Rajgopal (2012) reported that respondents estimated that approximately 20% of all companies manage earnings to misrepresent economic performance. While three different financial forensic models are applied in the selection of these portfolios, researchers associated with testing the M-score described their approach this way:
Our main hypothesis was that companies that share traits with past earnings manipulators (i.e., those that "look like manipulators") represent a particularly vulnerable type of growth stock. Because of their strong recent growth trajectory, these companies are likely to be more richly priced. At the same time, they exhibit a number of potentially problematic characteristics, indicative of either lower earnings quality or a more challenging economic environment. Although the accounting games such companies engage in might not be serious enough to warrant legal action, we posited that their earnings trajectory is more likely to disappoint investors (i.e., they have lower earnings quality)" (Beneish, Lee, & Nichols, 2013, p. 57).
One other important consideration from my own experience is that biotechnology stocks tend to be over represented in the population of adverse scoring stocks. Over the years, I have observed that the research-directed nature of biotech and pharmaceutical companies with low sales, low earnings, and typically high valuations and high share issuance may distort the results of these scholarly models without necessarily indicating that these firms are at higher risk. Another way to say this is that companies with a more standard business model like Tesla, building and selling products with a more standard track record of sales and earnings may be a much better application for these forensic algorithms.
Normally, I'm focused exclusively on long positions. Finding stocks with the best fundamental value, highest positive momentum, and a strong assortment of technical timing indicators for breakouts are at the top of my screening objectives. The powerful effects and timing of the Tesla Test measures was almost exclusively based on (1) the deep dive value characteristics of the Piotroski value score, (2) the earnings manipulation warning score of the Beneish model, (3) the two-year bankruptcy probability value of the Altman Z-score, and (4) the two-year distress model of the Ohlson O-score probability. The significant declines in prior selections reinforces my interest in this anomaly to see if these patterns return with consistency in a short term test for the stocks selected above.
I have been fascinated by forensic and value algorithms for many years and apply them in many different tests and market conditions. Each of these scores and methodologies is analyzed and detailed in my recurring monthly portfolios to find market opportunities through many different quantitative models that have survived the test of time:
- Forensic Stock Analysis For August: Do You Own These Stocks?
- Top Forensic Algorithms Check Whistleblower's $6B Claims Against Disney
- Running Top Forensic Algorithms On 2012-2019 GE Financials
The goal of this article is not to revisit all the forensic analysis of scholarly research already addressed in prior articles but to apply this uniquely effective combination of scores near the top of Tesla's decline to the current population of large firms across the US exchanges.
We will see together how this broad application of the unusually adverse scores of Tesla from October 2018 just prior to a significant 40% decline may or may not forecast results among the three largest firms currently qualifying under the same values. The average -29.3% decline among prior selections remains quite significant among the small sample to date.
Where's Tesla at now on the scores?
For those who may still be curious, TSLA scores today in this month of August 2019 are significantly improved from October with only the Altman Z-score and the Ohlson O-score probability percentage in adverse condition. The Ohlson O-score is now below 50% which is typically considered positive on the scale. The Piotroski value score has improved significantly from lows at 2 now up over 6 today showing much stronger value on a scale to 9.
I trust this research 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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Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.