One-Year-Ahead Value Anomalies: 15 Stocks To Outperform

Summary

• Can investors actually exploit documented abnormal returns reflected in financial statement information?
• A new study by Amor-Tapia and Tascon (2016) documents some confirmation that "fundamental signals derived from financial statements allow for future abnormal stock returns."
• Researchers tested the contributions of the Xue-Zhang FSCORE, Wahlen-Wieland PEIS, Piotroski FSCORE, and the Mohanram GSCORE with significant one-year results.
• This article presents the current top 15 stocks when applying the selected standout scoring model from the Amor-Tapia and Tascon (2016) research.

Introduction

This article begins a new series of anomaly research over a one-year time horizon well suited to value investing. First, the methods and results are presented below according to the authors of each of the models under review. Then I review an assessment of the four value system models in the current research study by Amor-Tapia &Tascon (2016). And finally, 15 of the top current stocks are selected and backtested according to the best performing model from among those tested in the recent literature.

In my continuing research to identify and leverage the most profitable financial market anomalies the following past and active informal studies are available for your consideration:

1. Phase 1: Short-term Momentum Accelerators/Decelerators 15 week test using MDA (summary article)
2. Phase 2: Short-term Momentum Accelerators/Decelerators weekly test using MDA enhanced (ongoing week 33 article)
3. A one year forward looking study of top stocks using the annual Russell 3000 Anomaly
4. A monthly return study of Value Investment Stocks using three forensic analysis models simultaneously (August article)

Additional evaluation of the research documenting abnormal returns will continue toward evidencing significant results on a regular basis. If you would like to follow along for as long I conduct these informal studies to enhance my trading methodologies please select the Follow button at the top of the page.

Background on Value Scoring Systems

Calculating scores and assigning values to stocks based on fundamental data remains one of the most popular methods for value stock investing. Most of us are familiar with such scoring systems as the Value Line Rank (started in 1965), the CANSLIM composite ranking system (started in 1988), the Zacks Rank (started in 1982, first made public in 1992) and many other popular systems that have given us good results over the years. To this day it is not uncommon to find substantial overlap among the best stocks identified by different value ranking methodologies. Most medium to long term investors are well served by taking these models into consideration.

Less well known are the academic composite value models based on fundamentals that continue to be rigorously tested in peer-reviewed financial literature. Some of these published models have their measurement scoring integrated by name into stock screens that are publicly available from various stock analysis websites. Four academic models tested recently by Amor-Tapia and Tascon (2016) are described as follows:

1. The Xue and Zhang (2011) FSCORE: Consists of the sum of eleven individual binary signals from financial statements. The most favorable value score is 11 and the least favorable score is zero. (Amor-Tapia &Tascon, 2016)

2. The Wahlen and Wieland (2011) PEIS (predicted earning increase score): Consists of the sum of six individual binary signals found by ranking firms in quintiles according to accounting ratios. The most favorable value score is positive 6 and the least favorable is negative 6. (Amor-Tapia &Tascon, 2016)

3. The Mohanram (2005) GSCORE: Consists of the sum of eight individual binary signals related to earnings and cash flow profitability. The most favorable value score is 8 and the least favorable is zero.(Amor-Tapia &Tascon, 2016)

4. The Piotroski (2000) FSCORE: Consists of aggregating nine individual binary signals derived from accounting variables related to profitability. The most favorable value score is 9 and the least favorable is zero.(Amor-Tapia &Tascon, 2016)

Performance Results from the Literature

First the results are presented below according to the authors of each of the models under review. Then an assessment is made by the current research study by Amor-Tapia &Tascon (2016). Lastly, 15 top stocks are selected and backtested according to the best performing model from among those reviewed.

1. Xue and Zhang (2011) FSCORE:

"We provide evidence that transient institutional investors (institutions who actively trade securities for short-term returns) trade on fundamental signals. We also show that the abnormal returns associated with fundamental signals increase with transaction costs and arbitrage risk, indicating the existence of limits to arbitrage for this investment strategy" (p. 1156).

2. Wahlen and Wieland (2011) PEIS:

"A hedge portfolio strategy that utilizes our approach within each consensus recommendation level generates average annual abnormal returns of 10.9 percent over our 12-year sample period, after controlling for previously identified risk factors" (p. 89).

3. Mohanran (2005) GSCORE:

"Firms with high GSCORE ratings significantly outperform low GSCORE firms, despite their lower systematic, unsystematic and ex-ante risk. Further, the GSCORE strategy returns positive returns in 21 years out of 23 years analyzed" (p. 167).

4. Piotroski (2000) FSCORE:

"In addition, an investment strategy that buys expected winners and shorts expected losers generates a 23% annual return between 1976 and 1996, and the strategy appears to be robust across time and to controls for alternative investment strategies" (p. 1).

These financial statement value models were each designed and documented to produce scores that allow the investor to earn abnormal returns according to the literature. However not all the value score anomalies documented in these studies using US markets were able to generate the same significant results when applied by Amor-Tapia & Tascon (2016) to four European markets. In fact, the only two surviving anomalies were found in Piotroski's FSCORE and Mohanram's GSCORE, with only slightly better results for the Piotroski's FSCORE in the observed period.

15 Stocks to Outperform

Based on the documented research and the recent evidence from the Amor-Tapia and Tascon (2016) application to the European markets, the Piotroski model was selected as the value score system for analysis. The top 15 stocks were selected using the Piotroski screen combined with a percentage score from the UncleStock.com proprietary database.

The resulting top 15 stocks from the August 15 selection screen were from highest to lowest scores: Willis Lease Finance (WLFC), Hibbett Sports (HIBB), Tortoise Energy (TYG), THL Credit (TCRD), Pangaea Logistics (PANL), TICC Capital Corp (TICC), Peak Resorts (SKIS), SSR Mining (SSRM)*, China Yuchai International (CYD), Grupo Simec (SIM), TOR Minerals International (OTCPK:TORM), Hecla Mining (HL), Tahoe Resources (TAHO), Schnitzer Steel (SCHN), Seadrill Partners (SDLP).

*name change 01Aug17 not updated in UncleStock database listed as both SSRI and SSRM in table.

The backtesting methodology available through UncleStock.com yielded a Yearly Renew annual return of 13% (Table 1) over seven years and a Buy/Hold annual return of 11% (Table 2) over seven years. Based on these estimated yearly results we may expect similar (or perhaps better) average returns from the top 15 value stocks selected above. Time will tell.

Table 1.
Table 2.

Conclusion

This ad hoc stock selection study is not intended to replace robust academic research, validate, or reject any prior methodologies or findings. This article is intended to briefly highlight four value scoring systems available in the financial literature with a sample of selected stocks from one of the better scoring systems documented. I hope this work is of some benefit to those who are interested in value investing, stock anomalies, and want to follow along with me on another financial study toward identifying ways to increase profits. Please select the Follow button at the top of the page if this or other articles I write may be of any potential benefit to you. All the best!

References

Amor-Tapia, B., & Tascón, M.,T. (2016). Separating winners from losers: Composite indicators based on fundamentals in the European context *. Finance a Uver, 66(1), 70-94.

Mohanram, P. S. (2005). Separating winners from losers among LowBook-to-market stocks using financial statement analysis. Review of Accounting Studies, 10(2-3), 133-170.

Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.

Wahlen, J. M., & Wieland, M. M. (2011). Can financial statement analysis beat consensus analysts' recommendations? Review of Accounting Studies, 16(1), 89-115.

Xue, Y. and Zhang, . M. H. (2011), Fundamental Analysis, Institutional Investment, and Limits to Arbitrage. Journal of Business Finance & Accounting, 38: 1156-1183. doi:10.1111/j.1468-5957.2011.02265.x

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

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.