Recognize These Week 29 Winners? Top 10 Stocks Across Multiple Financial Algorithms

by: JD Henning


Week 29 summary of top 10 stocks across Momentum, Forensic, Index Anomaly, and Piotroski-Graham enhanced value portfolios.

The Top 10 Russell 3000 Anomaly portfolio and Forensic portfolio represented 7 of the top 10 stocks this week.

Each portfolio is based on different models from the financial literature with different durations, objectives, and selection criteria.

Summary is based on the last 5 weeks of short-term momentum portfolios and the last 6 months of the long-term fundamental value portfolios.

Selections highlight the success and interconnectedness of different financial models and ideas for your own portfolio development.

Week 29 Summary Results

“It is not the amount of knowledge that makes a brain. It is not even the distribution of knowledge. It is the interconnectedness.”

― James Gleick, The Information: A History, a Theory, a Flood

Company Ticker Performance Week Price Model Portfolio
Avid Bioservices (CDMO) 30.82% 6.07 1-Year Buy/Hold Top 10 Russell 3000 Anomaly
AC Immune (ACIU) 18.23% 16.28 1-Year Buy/Hold Forensic Positive - April
Aquinox Pharmaceuticals (AQXP) 13.58% 3.01 Weekly Week 25 Breakouts
TransEnterix (TRXC) 13.04% 5.2 1-Year Buy/Hold Top 10 Russell 3000 Anomaly
Lonestar Resources US (LONE) 11.56% 10.04 Weekly Week 29 Breakouts
Cellular Biomedicine Group (CBMG) 10.03% 21.4 1-Year Buy/Hold Forensic Positive - June
OFG Bancorp (OFG) 9.66% 15.9 1-Year Buy/Hold Forensic Negative - June
YPF Sociedad Anonima (YPF) 8.63% 16.36 1-Year Buy/Hold Piotroski Value - July
Argenx (ARGX) 8.20% 96.21 1-Year Buy/Hold Forensic Positive - April
Zafgen (ZFGN) 7.79% 10.65 1-Year Buy/Hold Top 10 Russell 3000 Anomaly

* Table excludes weekly momentum portfolios more than 5 weeks old and all other portfolios more than 6 months old.

Click on the link in the portfolio column for more details.

In the same period, the S&P 500 for Week 29 delivered +0.02% as it danced around the key 2,800 support level all week.

The Value Based Approach

While there is some agreement that value strategies have produced superior returns, the interpretation of why they have done so is more controversial. “Behavioralists” believe that investors consistently tend to overpay for “growth” stocks that subsequently fail to live up to expectations (for example, Kahneman & Riepe, 1998 and Gilovich, Griffin, and Kahneman (2002)). In their view, value strategies produce higher returns because they are contrarian to “naive” strategies followed by other investors. (Elze, 2010, p. 528)

My application of the fundamental value algorithms from the financial literature seeks to bridge the gaps from theory to every day practical trading advantages. The two value-based models offered so far at V&M Breakouts include:

  1. Piotroski-Graham enhanced value portfolios that leverage the 9 fundamental ratios/parameters of Piotroski along with the two Benjamin Graham enhancements. The monthly portfolios are set up for one-year buy/hold based on the literature or any intervals in between.
  2. Forensic Portfolios (both Positive and Negative scoring) that leverage the bankruptcy and earnings manipulation detection methods of Beneish, Ohlson, and Altman for a combination of 22 fundamental ratio tests.

Explanations for the value premium by Fama and French (1992, 1996) show that value strategies are fundamentally riskier, so the higher average return on value stocks reflects compensation for bearing this risk. (Sehgal, Subramaniam, & Morandiere, 2012, p. 122)

The Index Anomaly Approach

My application of the Russell 3000 index reconstitution anomaly formed at the end of June each year is a type of momentum portfolio model designed to capture the increased interest in high performing stocks recently added to the Russell small cap index. This cross-sectional momentum approach benefits from return continuations of the price momentum.

Chordia and Shivakumar (2006) have also concluded that, “Two robust and persistent anomalies over the last four decades that have defied rational explanations are the post-earnings announcement drift, or earnings momentum, and the short-run return continuations, or price momentum” (p. 655)

The Weekly Momentum Selection Model

Based on multiple discriminant analysis of the strongest characteristics of momentum stocks, the breakout momentum model leverages short-term weekly gains selecting 8 stocks each week from among 7,700 candidates. These time-series momentum selections continue to outperform the benchmark S&P 500 by more than 4x the returns over the same period.

Momentum is based on the observed phenomenon, “where stocks with low returns over the last year tend to have low returns for the next few months and stocks with high past returns tend to have high future returns” (Fama & French, 2008, p. 1653)

Putting it all together

We find that fund managers demonstrate the ability to time market liquidity at both the portfolio level and the individual fund level — they increase (reduce) market exposure when the market is more liquid (illiquid) (Cao, Simin, & Wang, 2013)

What if the key momentum characteristics can be applied to fundamental value portfolios to enhance timing and short-term returns? What if hedge funds are using this strategy every day to exploit every possible advantage of the interconnectedness of each of these anomalies?

What if we can develop market momentum measurements to better assess when positive short-term breakout conditions are strongest? Taking a cross sectional look at the top 10 best performing stocks for Week 29 across these different financial models not only provides some great stock ideas to consider, but it gives us some good insight into which of these portfolio types are showing better success for us in the short term.

The large representation of Forensic selections and Russell Index anomaly stocks is worth noting. It is especially useful if Nobel laureate Eugene Fama is right about the momentum anomaly where "stocks with high past returns tend to have high future returns."

Extensive details on all these methodologies and their ongoing performance can be found via the links in the performance table for Week 29.

All the best,

JD Henning, PhD, MBA, CFE, CAMS


Cao, C., Simin, T. T., & Wang, Y. (2013). Do mutual fund managers time market liquidity?. Journal of Financial Markets, 16, 279-307.

Chordia, T. & Shivakumar, L. (2006). Earnings and price momentum. Journal ofFinancial Economics, 80, 627-656. doi:10.1016/j.jfineco.2005.05.005

Elze, G. (2010). Value investing anomalies in the European stock market: Multiple Value, Consistent Earner, and Recognized Value. Quarterly Review of Economics and Finance, 50, 527-537.

Fama, E. F. & French, K. R. (2008). Dissecting anomalies. The Journal of Finance, 63(4), 1653-1678. doi:10.1111/j.1540-6261.2008.01371.x

Sehgal, S., Subramaniam, S. & Morandiere, L. P. (2012). A search for rational sources of stock return anomalies: Evidence from India. International Journal of Economics and Finance, 4(4), 121- 134.

Disclosure: I am/we are long ZFGN.

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.