Healthcare Biotech Stocks Top The Positive Forensic Value Selections For April

by: JD Henning

New April selections for both positive & negative forensic value stocks. Biotech stocks take the top spots in these long-term value picks.

After 14 portfolios tested, the annual return of the positive forensic portfolios is 28.39% and leads the negative forensic portfolios average 1-year return of 17.21%.

The most significant performance differences are being detected in the 1-3 year range where positive portfolios have produced 32% higher returns.

Samples of the top positive and negative stock selections from across three of the best forensic algorithms in the financial literature are provided for April.

Introduction to the Positive/Negative Forensic Value Stock Selections

This quantitative study continues a series of multi-year tests of the top three forensic algorithms used to detect bankruptcy risk, earnings manipulation, and financial irregularities. My forward testing compiles portfolio selections from the highest positive and highest negative scoring stocks across the U.S. stock exchanges to measure performance variances between portfolios and benchmark indexes.

The different algorithms created by Beneish, Ohlson, and Altman are well documented from financial literature and rely exclusively on fundamental data, including year-over-year operational performance measures. The combination of all three bankruptcy and financial irregularity algorithms creates a unique "deep dive" on key value characteristics and applies a total of 22 different fundamental financial variables for assessment.

The methods and independent detection success of the three different forensic algorithms are detailed in the methodology section at the end of this article.

Negative Scoring Forensic Value Selections for April:

The following 4 stocks are the highest adverse scoring stocks from among 69 qualifying stocks screened in the database across all three algorithms. The results are sorted along the Ohlson O-score probability percentage.

The intended duration of meaningful results as tested by the scholars who developed these algorithms ranges from 1-3 years. Portfolios constructed in my forward testing that are older than 3 years will be dropped from the study over time.

Charts of the top 4 highest adverse scoring stocks across all three forensic algorithms are listed below:

Odyssey Marine Exploration (OMEX)

Odyssey Marine Exploration, Inc., together with its subsidiaries, engages in deep-ocean exploration activities worldwide.

Cadiz Inc. (CDZI)

Cadiz Inc. operates as a land and water resource development company in the United States. It engages in the water resource, and land and agricultural development activities in San Bernardino County properties.


SI-BONE, Inc., a medical device company, develops and commercializes a proprietary minimally invasive surgical implant system in the United States and Internationally.

TG Therapeutics (TGTX)

TG Therapeutics, Inc., a biopharmaceutical company, focuses on the acquisition, development, and commercialization of novel treatments for B-cell malignancies and autoimmune diseases in the United States.

Negative Scoring Stock Performance for each of the Forensic Portfolios:

Bars in blue: 7 Completed Annual Portfolio Returns

Black line: Return from Formation of Portfolio

Completion of the seven one-year portfolio results from 2017-2019 show that the negative forensic portfolios have much more volatility in return performance than the positive forensic portfolios. The average annual return is lower than the positive forensic value portfolios, but still remains a higher cumulative average than the S&P 500 annual returns in these years. After seven completed test runs, we may be seeing a stronger divergence between the positive and negative forensic value portfolios.

My working theory is that stocks that achieve high adverse scores on all three forensic algorithms may be much more dependent on momentum and investor sentiment for price performance than on fundamental and financial value characteristics. This dependency on weaker fundamental financial data toward more behavioral investment behavior makes the firms more susceptible to larger price fluctuations and potentially lower annual returns. - JD Henning, 2017

Positive Scoring Forensic Selections for April:

Once again, only five stocks qualified this month as the highest positive scoring stocks screened in the database with positive scores across all three forensic algorithms with a share price above $2/share. The top two stocks are listed below. This is a measure of low probabilities of bankruptcy or earnings manipulation that are being tested to find correlations to price returns over a one-year buy/hold strategy.

No particular sort of the variables was needed due to the limited number of qualifying stocks across the U.S. markets.

Stocks remaining on the positive list from the February selection portfolio include: Xencor, Inc. (XNCR). Xencor has also been in the top positive portfolio selections since December 2018.

Charts of the top 2 highest positive scoring stocks across all three forensic algorithms are listed below:

Menlo Therapeutics (MNLO)

Menlo Therapeutics Inc., a late-stage biopharmaceutical company, focuses on the development and commercialization of serlopitant for the treatment of pruritus associated with dermatologic conditions in the United States. The company has completed Phase II clinical trials in pruritus associated with prurigo nodularis.

Xencor, Inc.

Xencor, Inc., a clinical stage biopharmaceutical company, focuses on the discovery and development of engineered monoclonal antibody therapeutics to treat severe and life threatening diseases with unmet medical needs. It develops its antibody product candidates to treat autoimmune and allergic diseases, cancer, and other conditions.

According to research conducted by Beneish, Lee, & Nichols (2013), the results produce a strong independent approach to forecasting future returns.

"[The evidence] indicates that [the Beneish] M-score has significant ability to predict one-year-ahead cross-sectional returns. Our results show that this predictive power does not come from its correlation with value, momentum, size, accruals, or short interest" (p. 65).

Positive Scoring Stock Performance for each of the Forensic Portfolios:

Bars in blue: 7 Completed Annual Portfolio Returns

Black line: Return from Formation of Portfolio

The positive forensic portfolios show a more stable and consistent return performance than the negative forensic portfolios over 7 one-year portfolios tested to date.

These two forensic categories of positive and negative portfolios that rely upon advanced fundamental analysis to assess bankruptcy risk and financial irregularities may be showing how strong positive forensic scores produce more reliable positive future outcomes. While the negative scoring stocks with higher potential risks of bankruptcy or earnings manipulation may deliver higher returns as compensation to investors for the risks they may be undertaking. The true explanation is not yet known, but as these portfolios are tested throughout the coming year from each monthly formation period, we may increase the reliability of profitable selections or generate higher risk/return opportunity for higher gains based on the nature of these algorithms.

Ultimately, I intend to add the best algorithms as additional enhancements to my momentum breakout evaluation process to further develop larger long-term results.

Methodology Review

The purpose of this monthly value selection list is to provide investors with additional tools to evaluate financial irregularities according to three different detection models from academic research. Circumstances surrounding firms are always subject to change, open to extenuating circumstances, and models by their very nature always contain a degree of error.

I learned of these financial detection algorithms and others during my doctoral research on financial anomalies as well as through texts on financial statement irregularities in my certification programs. Again, it is 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).

To my knowledge, no similar longitudinal study of positive and adverse forensic scoring using all three models simultaneously has ever been conducted before. The significant benefits of these portfolios are already emerging in two short months. It is also important to constructively consider why such anomalies may exist in these stock selections at this moment in time. The resulting data which varies from month to month may prompt firms and investors to consider further due diligence of publicly available financial characteristics to mitigate any risk or error present in the marketplace.


With 14 portfolios (7 positive forensic value/7 negative forensic value) having completed a one-year test period, we are beginning to see some significant variation occur. Though the sample sizes are still small, I do think we are seeing statistically significant performance results that the positive portfolios deliver higher and more stable returns over time.

Perhaps the most significant result is observed in the formation returns beyond one year as shown in the charts above with a black line. After holding negative forensic portfolios for one year or longer, 6 out of 7 had negative returns greater than -2% and an average formation return of -21.13%. However, after holding the positive forensic portfolios for one year or longer, only 2 out of 7 had a negative return greater than -2% and the average formation return was a positive +11.51%

Beyond a one-year holding period, the positive and negative forensic value portfolios are generating a statistically significant difference of 32.64%. This is still well within the intended 1-3 year effective range of the models' design.

Prior tests in the literature of the Beneish M-score have shown the algorithm to generate excellent results on an annual basis for positive scores. The tests continue and more explanations may develop over time.

All of these key weekly and monthly selections feed into the Premium Portfolio database selection model portfolio that is now up 21.83% YTD

I trust this research and stock selections will give you added value to your investment goals and returns in 2019!

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.

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.