Value Investment Stock Selections Using Forensic Analysis - August

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

Summary

Financial forensic methods can be useful in identifying anomalies in the stock market.

The stock selection screen leverages academic models of the Altman Z-score, Ohlson O-score, and the Beneish M-score.

Evidence suggests that reliability is enhanced by using a blend of financial ratios and different models for detection.

The firms identified by the models may be at risk or undervalued, but circumstances change and models always contain a degree of error.

Review of July returns for positive and adverse selections.

Intro

The purpose of this monthly article is to provide investors with additional tools to evaluate financial irregularities according to three different detection models from academic research. It is my hope that this contribution toward improving due diligence in the market place using publicly available data and published algorithms is of some value to the reader. 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.

To my knowledge no similar study of positive and adverse irregularities using these three models simultaneously has been conducted before. I hope that benefits are also constructively considered regarding why such anomalies may exist in these models 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.

Methodology

The process for screening stocks begins by applying the positive and adverse parameters for each of the three academic models simultaneously: In the adverse selection, stocks are screened for the combination of values less than 1.81 on the Altman Z-score, greater than 50% on the Ohlson O-score probability, and greater than -2.22 on the Beneish M-score model. In the positive selection, stocks are screened for the combination of values greater than 2.99 on the Altman Z-score, less than 10% on the Ohlson O-score probability, and less than -4.44 on the Beneish M-score model.

Altman Z-score

The Altman (1968) Z-score was developed by Edward Altman as a bankruptcy prediction model for "practical decision making." He broke from traditional ratio analysis and introduced a unique approach leveraging multiple discriminant analysis (MDA) to determine which financial ratios provided the most effective signals of financial distress when used simultaneously. His work with MDA was also inspiration for my ongoing research towards identifying the strongest characteristics of price momentum behaviors.

The five financial ratios that Altman relied upon are: working capital/total assets, retained earnings/total assets, earnings before interest and tax/total assets, market value of equity/book value of total liabilities, and sales/total assets. Within his sample of 66 firms, the model had a 95% accuracy in classifying corporate failure within a one-year event horizon.

The parameters used for Altman Z-score span the values from the safe zone > 2.99 to the risk zone < 1.81.

Ohlson O-score

The Ohlson (1980) O-score probability was developed by James Ohlson and used a multi-factor financial model to predict business failure that relied on nine financial ratios. Olson used a sample of 105 bankrupt firms and 2,058 non-failing firms to classify event failures with financial ratios. The results did not perform as well as MDA, but well enough to contribute additional high probability of distress detection.

The nine financial ratios are: firm size, total liabilities/total assets, working capital/total assets, current liabilities/current assets, a dummy variable whether total assets were greater or less than total liabilities, net income/total assets, funds from operation/total liabilities, a final dummy variable whether net income was negative for the last two years, and change of net income.

The parameters used for the Ohlson O-score probability set that firms with probabilities above 50% would likely default within two years, while values less than 10% represent strong underlying fundamentals.

Beneish M-score

The Beneish (1999) M-score was developed by Messod Beneish and used a weighted blend of eight different indexes to measure year-over-year changes in order to detect earnings manipulations. This scoring method was also accurate in anticipating the high risk of earnings manipulation prior to Enron's demise. The eight ratios used are: Days' sales in receivables index (a measure of current year over prior year sales and receivables), gross margin index (a ratio of prior year's gross margin to the current year), asset quality index (a ratio of current year's non-current assets other than P&E to total assets over the prior year's value), sales growth index (a ratio of current year's sales to prior year's sales), depreciation index (a ratio of prior year rate of depreciation over current year depreciation rate), SG&A expense index (a ratio of current year over prior year's sales, general, and administrative expenses), leverage index (a ratio of current year over prior year's total debt to total asset ratio), and lastly the total accruals to total assets ratio.

The parameters used for the Beneish M-score set a benchmark of at-risk firms having a value greater than -2.22, while values less than -4.44 show a low risk of earnings manipulations or irregularities.

The August Stock Screen Results

The August stock screen was run for both the positive and adverse parameters of each of the three models and results were obtained using a proprietary database at UncleStock.com. In the positive selection screen using all three models' parameters simultaneously, the query returned only four stocks above $3 per share from among 4,880 stocks in the database for all US exchanges, excluding OTC stocks. These four stocks were the only stocks that had complete data at the time of the query for all three models and satisfied the parameters detailed above. The four stocks are shown in Table 1 below:
Table 1.

There were no new additions to Table 1 from the July selection period. Three stocks left the list either due to changes in scores, share price, or a lack of data to satisfy all three models for the August query.

The adverse selection screen from the UncleStock.com database returned 14 stocks above $3 per share from among 4,880 stocks on US exchanges not including OTC stocks. This result doubled the number of stocks from the July list to the August list. Stocks are sorted in descending order based on last traded stock price from highest to lowest. The resulting selection Table 2 represents the stocks above $3 per share that had simultaneous adverse scores for Altman Z, Ohlson O, and Beneish M tests. The 14 stocks are shown in Table 2 below:

Table 2. 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).

Review of July Stock Selections

The first set of selections were made on July 24th and are documented in the July value investment stock selection report here. The reporting period has been shortened to readjust the timing of these monthly value selection articles to the first week of the month. The abbreviated results for July are presented below.

At the time of selection, the seven stocks within the favorable parameters of all three detection algorithms for July were: John Bean Technologies (JBT), Spark Therapeutics (ONCE), Northwest Pipe Company (NWPX), AC Immune (ACIU), Pure Cycle (PCYO), Friedman Industries (FRD ), and R1 RCM, Inc. (OTCPK:RCM). The average YTD performance of this portfolio was 9.54%.

Since the time of selection the favorable stock selections based on the three models returned the following results:

For comparisons, the seven stocks within the adverse parameters of all three detection algorithms for July were: Omeros Corporation (OMER), China Online Education Group (COE), Immunomedics (IMMU), XOMA Corporation (XOMA), root9B Technologies (OTC:RTNB), ImmunoGen (IMGN), ViewRay (VRAY). The average YTD performance of this portfolio was 78.55%.

Since the prior selection period, the adverse stock portfolio returned the following results: In this time period both portfolios returned negative results. The two portfolios failed to return statistically significant results that would indicate the search criteria provided meaningfully different price performance. The continuation of some stocks and the removal of other stocks from the monthly query results may also contribute some meaningful indicator that has yet to be determined.

I leave the interpretation of these results to the reader and welcome your feedback. Time will tell how significant these very different outcomes may be, but for now this study provides a small baseline from which to consider the fundamental selection criteria using a simultaneous combination of Altman Z, Ohlson O, and Beneish M scores.

Conclusion

The goal of this article is to raise awareness about financial anomalies in the marketplace using publicly available data and published academic algorithms. My ongoing research on accelerating breakout and breakdown momentum seeks to identify irregularities in whatever form they may arise. 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.

It is my sincere hope this research is of some value to you as a practical guide in the marketplace applying more tools from well known academic methods. I hope you will join me in observing what results emerge from this monthly study.

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

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.

Editor's Note: This article covers one or more microcap stocks. Please be aware of the risks associated with these stocks.