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Forensic 'Tesla Test' Year-End Results: Plus 4 Stocks For December

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Includes: AMRS, CLVS, CTRC, DERM, DTIL, IMGN, IMMU, MDRIQ, PTLA, SRNE, TGTX, TSLA
by: JD Henning
Summary

The "Tesla Test" looks for the same forensic valuations that preceded a 40% drop in TSLA stock earlier this year.

Only 1 stock qualified over $1 billion in market cap in this December test.

A total of 25 stocks across all the major US exchanges achieved such adverse scores and all but the four selected stocks were below market caps of $300M.

These are the end-of-year results and analysis of the tests conducted throughout 2019.

After significant declines in these samples we are seeing very large positive breakout reversals toward these previously out of favor stocks.

Introduction

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 was shared by no other company with a market cap of 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."

The Current 4 Largest Stocks of the "Tesla Test"

Immunomedics (IMMU) is the only multi-billion dollar market cap stock to qualify at these levels in December and the first one since the test was run in August 2019. The stock price has been doing very well since October, but these forensic accounting scores reflect some divergence from recent earnings: Immunomedics Reports Wider-Than-Expected Loss in Q3

(FinViz)

Sorrento Therapeutics (SRNE) has been a recurring selection throughout 2019 as detailed in the different portfolio test results detailed below. It has steadily declined from a price of $3.59/share in May down to lows around $1.50 into October. However, recent speculation of it being a takeover target sent the price soaring over 125% in the past month, while forensic accounting scores remain in the qualifying adverse conditions: Sorrento Turns Down Two All-Cash Takeover Bids, Stock Jumps

(FinViz)

Dermira, Inc. (DERM) makes its first appearance in the forensic fundamental screen with these high outlier parameters. Fundamentals from their November 5th earnings statement look very positive. It may be that such combinations of 1,508% Q/Q sales growth and high debt levels 24 times equity and a Price/Book ratio of 29.8 combine to trigger adverse scores. Watching the performance of DERM may help test the reliability of this unusual forensic measurement.

(FinViz)

Amyris, Inc. (AMRS) was among the selections in the most recent October screen and since then is down -17.4%. Scores again for December qualify it as one of the largest market cap stocks with the adverse 'Tesla Test' parameters.

(FinViz)

Previous Results of the "Tesla Test"

Five sample sets of stocks since the Tesla Test back in October 2018 have been screened this year using this forensic and value screen. The sets of portfolios were published on May 22, July 3, August 22, October 1, and now December 2. This analysis reviews the results of the prior four tests.

I. First Portfolio Test: On May 22, the first three companies beyond the October 2018 Tesla Test that screened as the largest firms to meet or exceed all the adverse values of Tesla were Clovis Oncology (CLVS), Sorrento Therapeutics, and TG Therapeutics (TGTX).

(Source: UncleStock)

From the original May article: The 3 Largest Stocks Now Scoring Worse Than Tesla Before Its 40% Decline

Back in the October report (4 months later) down an average of -49.48%:

CLVS declined -77.5% to $3.84/share.

SRNE declined -44.9% to $1.89/share.

TGTX declined -26.03% to $5.03/share.

Scores in December are still low, but much improved from the last 'Tesla Test' report:

(Source: UncleStock)

CLVS and SRNE prices are still down from May, but all the stocks have improved very significantly since October. This suggests that the range of effectiveness of the set of scores lasts to about 4 to 5 months with only SRNE still at adverse levels. Certainly, other external factors may explain the strong recovery since October of these prior adverse scoring stocks, but the overall results have correlated highly throughout the year.

2 out of 3 stocks are still down significantly from their May origination levels. TGTX - the one stock that is now positive - had positive results in October with their Phase 2b Trial and is no longer in the extreme adverse levels measured previously.

II. Second Portfolio Test: 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 October results of the Second Portfolio Test of three stocks nearly 3 months later down an average of -36.37%:

MDR declined -80.9% from $8.99/share to $1.71/share.

CTRC declined -25.6% from $ 3.32/share to $2.47/share.

PTLA declined -2.61% from $26.48/share to $25.79/share.

Scores in December are low, but again much improved from the last 'Tesla Test' report:

(Source: UncleStock)

Through to December, MDR retains the lowest scores of the three stocks and has declined -90.7% from $8.99/share to $0.84/share.

CTRC is recovering, but still down from July values with adverse scores. PTLA is now up +4.3% from July and has remained fairly flat over the last 8 months.

So 2 out of 3 stocks are down significantly from the start and one remains relatively flat since July.

III. Third Portfolio Test: On August 22, the third group of three largest firms to meet or exceed all the adverse values of Tesla from last October were Precision BioSciences (DTIL), Portola Pharmaceuticals and Sorrento Therapeutics. Both PTLA and SRNE are prior selections from earlier this year that still show adverse conditions.

(Source: UncleStock)

The October results of the Third Portfolio Test of three stocks nearly 1 month later down an average -14.13%:

PTLA declined -11.7% from $29.07/share to $25.67/share.

DTIL declined -16.3% from $8.72/share to $7.30/share.

SRNE declined -14.4% from $2.29/share to $1.96/share.

In contrast to the original August scores above, the scores at the start of December now show the following:

(Source: UncleStock)

PTLA scores improved slightly in line with share price moves from $25.67 to $27.61, but still down from August.

DTIL scores improved significantly and the stock price gained over 130% since the last report with improved Q3 results.

SRNE scores remain low, but the stock price has gained +73.9% following news of management rejecting two all-cash takeover bids as posted above.

IV. Fourth Portfolio Test: On October 1, the fourth group of three largest firms to meet or exceed all the adverse values of Tesla from last October were ImmunoGen, Amyris and Sorrento Therapeutics. Both PTLA and SRNE are prior selections from earlier this year that still show adverse conditions.

(Source: UncleStock)

When I ran the test for the October article no stocks had a market cap over $1 billion with an equal or worse combination of scores than TSLA registered last October. That finding may be highly significant in itself for identifying future high risk outliers.

In contrast to the original October scores above, the scores at the start of December now show the following:

(Source: UncleStock)

From October to December, the forensic scores have improved slightly for the three selected stocks, while the share prices for IMGN and SRNE have improved significantly. A major disrupting factor to this testing period is that all three stocks reported third-quarter earnings results in November that alter the fundamental accounting and investor sentiment. It is difficult to make any clear assessment except that only SRNE remains within the parameters for consideration into December.

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 overrepresented 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.

Conclusion

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 reinforce 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:

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.

My end-of-year takeaway on this ad hoc 2019 study is that the results have been very significant predictors of short-term declines with few exceptions. It also shows that the most reliable results occur following earnings reports and not when earnings surprises may occur during the measurement period. Because these forensic accounting ratios are based heavily on fundamental data, external events and surprising news will alter stock price performance independent of any forecast. Despite a few exceptions, most of the stocks performed as expected, and in the case of MDR experienced declines larger than 90% in less than a year.

Where is Tesla at now on the scores?

For those who may still be curious, TSLA scores today in this month of December 2019 are now significantly improved from the initial October 2018 values. The Piotroski value score has improved from lows at 2 now up over 5 today, but down from October highs above 6.5 and showing a much stronger value score on a scale to 9.

Current TSLA scores are still well above the initial adverse scores last year when a subscriber first asked me about the condition of TSLA.

(Source: UncleStock)

TSLA is well out of the parameters originally used in the 'Tesla Test' and the price has been showing more strength in the last 6 months. I will continue to run this anomaly study into 2020 as I find it very unusual to have multi-billion dollar corporations scoring poorly across all the extreme values applied. It is surprising to find any single outliers from among more than 8,000 stocks and raises my interest every time.

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

References

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.

Disclosure: I am/we are long FNGU. 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.