Forensic Stock Selections For August - First Year Portfolio +96.80%

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Includes: ACIU, ACY, AI, CETC, CFG, CHKE, COE, CRK, DSKE, FRD, HTZ, IMGN, IMMU, INVA, JBT, LILA, NTN, NTNX, NWPX, OMER, ONCE, PCYO, RCM, RTNB, VRAY, XOMA, YGYI
by: JD Henning
Summary

Sample of the new Forensic Positive & Negative Value Stocks exclusively for subscribers.

The one-year performance of the 2017 Negative Forensic July portfolio ends up +96.80% to lead the pack of the Negative Forensic Portfolios.

The one-year performance of the 2017 Positive Forensic July portfolio ends up +61.82% to lead the pack of the Positive Forensic Portfolios.

Samples from the Top 5 Positive/Negative Forensic stock selections. New weighting for August to the Beneish M-Score from the Ohlson O-Score probability percentage algorithm.

A discussion of why these three financial forensic models have been applied as well as a review of the first year portfolio results.

Forensic Value Stock Selections

The following stock selections using three forensic algorithms from the financial literature rely exclusively on fundamental data and year-over-year operational performance measures. The combination of all three bankruptcy and financial irregularity algorithms use a total of 22 fundamental financial variables for assessment. The three different forensic algorithms are detailed in the methodology section at the end of this article.

Significant new changes in the trend of performance between positive and negative forensic portfolios are detailed below.

Adverse Scoring Forensic Selections For August 2018:

A sample of three stocks from the full portfolio of 10 stocks are provided below representing the highest adverse scoring stocks screened in the database across all three algorithms. The full portfolio selections are available to members of the Value & Momentum Breakout community.

Starting now for August the descending sort will be tested on the Beneish M-Score instead of the Ohlson O-score probability percentage. The magnitude of the Beneish M-Score may also be a factor in future returns that will be tested as well.

A sample of the 3 highest adverse scoring (Beneish-weighted) stocks across all three forensic algorithms are listed below:

Arlington Asset Investment Corp (AI)

Comstock Resources (CRK)

Citizens Financial Group (CFG)

Adverse Scoring Stock Performance For Each Of The Forensic Portfolios:

Forensic Selections with Adverse Scores Returns YTD Number of Periods
July (-) Forensic Portfolio 1 96.80% 1 Year Ended
Aug (-) Forensic Portfolio 2 42.31% 1 Year Ended
Sep (-) Forensic Portfolio 3 13.57% 11 months
Oct (-) Forensic Portfolio 4 -1.11% 10 months
Nov (-) Forensic Portfolio 5 0.17% 9 months
Dec (-) Forensic Portfolio 6 -5.63% 8 months
April (-) Forensic Portfolio -13.73% 4 months
June (-) Forensic Portfolio 10.92% 2 months

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 financial performance. In the terminology of the financial literature, glamour stocks vs. fundamental value stocks. Glamour stocks tend to outperform in periods of high momentum, while value stocks tend to gain more steadily and consistently over longer periods.

An analysis of the 17 stocks from both the July and August 2017 completed forensic negative portfolios provides some fascinating results:

An analysis of the stock composition from both the July and August 2017 Negative Forensic portfolios after one year shows some unique results relative to the Positive Forensic portfolio returns detailed below. These negative forensic selections had a large standard deviation with 6 stocks gaining more than 130% including 2 stocks gaining over 200% and one losing over 90%. The average return of the negative portfolio stocks was +60.46% with 5 of 17 (29.4%) of the stocks declining over the year, but 6 of 17 (35%) gaining over 130% for the year.

Looking back, the seven stocks within the adverse parameters of all three detection algorithms for July 2017 were: Omeros Corporation (OMER), China Online Education Group (COE), Immunomedics (IMMU), XOMA Corporation (XOMA), root9B Technologies (OTC:RTNB), ImmunoGen (IMGN), and ViewRay (VRAY). For the negative scoring selections with the highest adverse scores in August of 2017, four stocks continued from the July portfolio and ten new stocks were added.

Only COE, IMGN XOMA, and VRAY remain from the 7 stocks selected in July. The 10 new stocks for August include: Liberty Global (LILA), Nutanix (NTNX), Hertz (HTZ), Innoviva (INVA), Daseke (DSKE), Aerocentury (ACY), NTN Buzztime (NTN), Cherokee (CHKE), Youngevity International (YGYI), Hongli Clean Energy (CETC).

One-Year Performance Chart for Negative Forensic Portfolio One-Year Bar Chart of Negative Forensic Portfolio

Positive Scoring Forensic Selections For August 2018:

Only three stocks qualified as the highest positive scoring stocks screened in the database with positive scores across all three forensic algorithms. 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.

The three qualifying stocks for August are available exclusively to members of the Value & Momentum Breakout community, while a small sample of the highest negatively scoring forensic selections for August are available above.

According to research conducted by Beneish, Lee, & Nichols (2013):

"[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).

To further enhance the test of the published evidence, the Forensic Portfolio selection will sort on the Beneish M-Score starting in August. Results so far sorting on the Ohlson O-score probability percentage in this informal forensic study and using the most positive scoring stocks across the Beneish M-score, Altman Z-score and the Olson O-score are listed in the portfolios below:

Forensic Selections with Positive Scores Returns YTD Number of Periods
July (+) Forensic Portfolio 1 61.82% 1 Year Ended
Aug (+) Forensic Portfolio 2 44.70% 1 Year Ended
Sep (+) Forensic Portfolio 3 54.29% 11 months
Oct (+) Forensic Portfolio 4 25.15% 10 months
Nov (+) Forensic Portfolio 5 14.02% 9 months
Dec (+) Forensic Portfolio 6 -0.35% 8 months
April (+) Forensic Portfolio 0.10% 4 months
June (+) Forensic Portfolio 4.83% 2 months

The positive forensic portfolios show a much more stable and consistent return performance with nearly every portfolio outperforming the S&P 500 in their respective time periods.

This price performance behavior across the monthly portfolios supports my working theory that strong positive fundamental characteristics and undervalued stocks become more attractive to investors during higher periods of risk and negative momentum.

The positive forensic selections from the 2017 July and August portfolios over a one-year period had a much smaller standard deviation with 1 stock gaining over 100% and none of the stocks in the two portfolios declining. In fact, the worst-performing stock from the positive portfolios was up 11% for the year. The average return of the positive portfolio stocks was +47.9% compared to the average negative portfolio return of +60.46%.

At the time of selection, the seven stocks within the favorable parameters of all three detection algorithms for July 2017 were: John Bean Technologies (JBT), Spark Therapeutics (ONCE), Northwest Pipe Company (NWPX), AC Immune (ACIU), Pure Cycle, Friedman Industries, and R1 RCM, Inc. (OTCPK:RCM).

The four stocks with the highest positive scores for each of the three algorithms for Aug 2017 were: John Bean Technologies, Northwest Pipe Company, Friedman Industries (FRD) and Pure Cycle Corporation (PCYO) with substantial carryover from July. I have since begun skipping months for the Forensic Portfolio formations to reduce the degree of noticeable overlap that sustains in consecutive months. This phenomenon may also lend additional credibility to the scoring parameters put forward by the scholars whose algorithms are being applied simultaneously.

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 known, but as these portfolios are tested throughout the coming year from each monthly formation period, we may increase the reliability of profitable selections and generate higher risk/return opportunity for higher gains based on the nature of these algorithms.

One-Year Performance Chart For Positive Forensic Portfolio One-Year Bar Chart Of Positive Forensic Portfolio

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 selection 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.

Conclusion

So far, the four portfolios (2 negative/2 positive) that have completed a one-year buy/hold period from their original date of formation have all greatly exceeded the benchmark S&P 500 one-year returns by more than double. Though the sample sizes are small, I do think we are beginning to see the emergence of statistically significant performance results.

The two positive forensic portfolios (July/Aug) had 100% of the stocks gain over 11% and as much as 131% in the year long period. While nearly 30% of the stocks in the two negative forensic portfolios declined, and one stock by more than 90%. Despite significant declines the negative forensic portfolios still managed to deliver higher average returns of 60.46% compared to 47.9% for the positive portfolios. The negative forensic portfolios delivered 6 stocks out of 17 (35%) with over 130% gains including two stocks gaining substantially more than 200% for the year.

Prior tests in the literature of the Beneish M-score had shown the algorithm to generate excellent results on an annual basis for positive scores. What we may be seeing now are the early results that stronger fundamental scores perform more consistently, but riskier adverse scores have the potential to generate much higher returns. In this one-year buy/hold test of four portfolios, the difference could be as much as 25% higher on average for the negative scoring than for the positively scoring stocks.

The tests continue and more explanations may develop over time. I welcome thoughts and ideas of readers as this anomaly is tested out over more portfolios and enhanced by weighting the Beneish M-score more heavily. I trust these selections will be a significantly positive addition to your investment goals and returns in 2018!

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/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.

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