Forensic Value Stock Selections
The following stocks selections using three forensic algorithms from 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 Stock Performance for each of the Forensic Portfolios:
|Forensic Selections with Adverse Scores||Returns YTD||Number of Periods|
|July (-) Forensic Portfolio 1||78.09%||11 months|
|Aug (-) Forensic Portfolio 2||36.34%||10 months|
|Sep (-) Forensic Portfolio 3||26.31%||9 months|
|Oct (-) Forensic Portfolio 4||0.55%||8 months|
|Nov (-) Forensic Portfolio 5||-1.77%||7 months|
|Dec (-) Forensic Portfolio 6||0.42%||6 months|
|April (-) Forensic Portfolio||-7.64%||2 months|
Adverse Scoring Forensic Selections for June:
The following 10 stocks are the highest adverse scoring stocks screened in the database and sorted in descending order by the Ohlson O-score.
Charts of the top 5 highest adverse scoring stocks across all three forensic algorithms are listed below:
Byline Bancorp (BY)
HC2 Holdings (HCHC)
Senseonics Holdings (SENS)
Comstock Resources (CRK)
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. A shift in performance of these portfolios could represent a transition period from favoring aggressive momentum stocks toward favoring more undervalued fundamental stocks as those found in the Positive Forensic selections and Piotroski Enhanced portfolio selections.
Positive Scoring Forensic Selections for June:
The following four stocks are the highest positive scoring stocks screened in the database that had at least two scores available out of the three forensic algorithms.
FreightCar America (RAIL)
Cellular Biomedicine Group (CBMG)
CRISPR Therapeutics (CRSP)
MEIP Pharma (MEIP)
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).
Results so far in this informal forensic study using the most positive scoring stocks across the Beneish M-score, Altman Z-score and the Olson O-score are sampled in the portfolios below:
|Forensic Selections with Positive Scores||Returns YTD||Number of Periods|
|July (+) Forensic Portfolio 1||36.18%||11 months|
|Aug (+) Forensic Portfolio 2||19.42%||10 months|
|Sep (+) Forensic Portfolio 3||73.25%||9 months|
|Oct (+) Forensic Portfolio 4||25.99%||8 months|
|Nov (+) Forensic Portfolio 5||14.95%||7 months|
|Dec (+) Forensic Portfolio 6||5.34%||6 months|
|April (-) Forensic Portfolio||9.04%||2 months|
As the graph below shows, the positive forensic portfolios are all at or near new highs for five out of six of the active portfolios. The positive forensic portfolios show a much more stable and consistent return performance with 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. 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 differently the two categories behave during the current changes in market conditions. This shift toward strong fundamental stock characteristics and lower risk of bankruptcy or adverse financial conditions is becoming much more evident in the difference of performance between Positive and Negative Forensic Portfolios.
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 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 remarkable benefits of these value algorithm portfolios demonstrate a statistically significant return in excess of the benchmark S&P 500 returns across all the time periods measured to date. It is also important to constructively consider why such anomalies may exist in these stock selections. 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.
The forensic portfolios will now be generated every other month throughout 2018 to provide an ongoing analysis of market conditions and the effectiveness of these popular forensic algorithms. This longitudinal study of positive and adverse forensic stock selections serves in part to raise awareness about financial anomalies in the marketplace using publicly available data and published academic algorithms. Circumstances surrounding firms are always subject to change, open to many extenuating circumstances, and models by their very nature always contain a degree of error. Publicly available databases are not without error and due diligence should always be applied before jumping to any conclusions from any of the models offered in this article.The monthly results continue to show somewhat of a U-shaped correlation with both adverse and positive portfolios generating significantly positive monthly returns. However, while the returns of the adverse portfolios are still quite high, they have declined significantly in the past few months, while the positive portfolios have seen large gains and new highs and are currently outperforming the S&P 500 in every portfolio for their respective time period.
Though the sample sizes are small, I do think we are beginning to see the emergence of statistically significant performance results. Prior tests in the literature of the Beneish M-score had shown the algorithm to generate excellent results on an annual basis. Results that only now are beginning to show the emergence of a preference for safer fundamental undervalued characteristics over the more momentum based and riskier investments.
Long-Term Portfolio Returns
As a reminder of the other longer term portfolios being tracked and updated regularly, here are the returns through Week 21. These 23 Buy/Hold portfolios are averaging 17.81% in much less than a full year duration. The Premium Portfolio is an actively managed trading portfolio exclusively for members.
Please click the "Follow" button at the top of the page to enjoy free updates on the progress of each of the different portfolios I offer that are outperforming the S&P 500 in each of their respective time periods.
To Subscribe to Value & Momentum Breakouts, click on the link HERE.
I think these selections will serve you well. All the very best, and have a great week of trading! 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 am/we are long MEIP.
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.
Editor's Note: This article covers one or more microcap stocks. Please be aware of the risks associated with these stocks.