2016 Q1 Q2 Action Score and Value Strategy Performances
In this article:
- Action Score performance and how it works
- OSV value stock performances
- The screeners that will be deleted…
It’s August already.
Time to review some performances.
I missed the Q1 and Q2 updates due to stockpile of projects and happenings so far this year.
- created and launched the Action Score along with OSV Online
- ongoing app enhancements
- in the middle of redesigning the website to reflect the new service
I’ll have more updates on our new website and upcoming comparison and screener feature as it comes.
Let’s start with the predefined stock screeners followed by the Action Score system. I also provide and discuss 2-3 stocks high scoring Action Score stocks next week. For VIP A list email subscribers (anyone who regularly opens emails), I’ll try to send a couple extra stocks to review via email.
Value Stock Screener Performances 2016 Q1 and Q2
2016 started off on the wrong foot and it showed until Q2. Upcoming Q3 results will show stronger results, but until Q2, more than half were lagging the market.
The original goal of these predefined screeners was to provide free stock ideas based on strategies that are off the street.
I.e. unique and under-utilized.
However, based on results of recent years, I’ll be working to come up with new strategies and replace more than half of them over the next year. But some of the new screens will be for OSV Insiders only.
Based on observing the results of all the different strategies I follow, certain strategies are now better suited for quality checks only and not for stock selection.
E.g. The Altman Z score is better suited to verify the stock won’t blow up, as opposed to constructing a portfolio out of the stocks that meet the criteria.
Same for stocks based on insider buys. While it’s a good sign, it’s not a reason you should buy the stock.
Look at the results below and refer to the historical performances to see what I mean. Compare the 3, 5 and 10 year CAGR to see the full picture.
These next results are from Jan 1 to June 30th.
Screener Descriptions and Which Ones to Delete
Based on the past 3-5 year results, here’s a short description of each and what I plan to do with it.
NNWC (Net Net Working Capital)
with the US market hitting new highs, there’s no way NNWC will work for now.
Verdict: HOLD (no updates)
NCAV (Net Current Asset Value)
Working better than NNWC as there are more stocks to choose from. There will always be companies that sell near asset value.
CROIC (Cash Return on Invested Capital)
This one is still going strong. This metric is able to identify strong and profitable companies. A technique that the market doesn’t know about.
FCF Cow (Growing FCF with low P/FCF multiple)
Looks like the market has caught onto this one. FCF has become a lot more mainstream than 10 years ago.
Graham Formula and Graham Checklist (Based on finding stocks cheap using the Graham formula and Graham checklist)
Valuation is not a good screening method because a single adjustment can drastically alter the result. The only reason this one is still working is because the formula is simple.
For the checklist, the times were much different when Graham created the checklist. Because of the differences in the US economy, the Graham Checklist has lost some lustre.
It needs to show improvements in the coming years.
Verdict: KEEP (for now)
Magic Formula (Greenblatt’s Formula)
Based on low EV/EBIT + high ROIC. But with the way it’s under-performed the market for at least 3 years and Greenblatt funds also not doing better than the index, it’s getting close to being axed.
For now, it remains as the 3yr CAGR of 10.85% is acceptable.
Altman Z (finding safe and healthy stocks with scores above 3 threshold)
While the Altman Z is effective for financial analysis and double checking the quality, it’s finding it tough to use it for stock selection.
The 3 year CAGR is keeping it alive for now.
Negative Enterprise (stocks with piles of cash)
Hasn’t worked out for a while now. Strong markets have killed this strategy and the list produces a lot of struggling cash boxes that you don’t want to touch.
NNWC Increasing, Insider Buys, Low Expectations
Keeping it short for these.
“NNWC increasing” is far too inconsistent and showing a lot of value trap stocks. Apparels, small biotechs and other beaten down stocks.
Insider buys don’t signal a stock buying opportunity. It’s just a “nice to have”.
Low expectations is based on low PE, low PB. The “typical” screen performed by everyone.
The Old School Value Action Score
Here’s how I define and rank the stocks into what I call our “Action Score”. It is made up of 3 scores. Quality, Value and Growth.
The Q,V and G score is then averaged to come up with the final Action Score.
If an Action Score gets an “A” for all three criteria (QVG), I call it a Trifecta Stock.
Quality stocks are ranked and scored based on
The Value score is based on
- P/FCF – has the biggest impact on the results and receives the highest weighting
- EV/EBIT – does a great job of identifying cheap stocks and receives the second highest weighting
- P/B – acts as a “cleaning” filter to remove stocks where you overpay for assets. Also a way to remove bad stocks you wouldn’t want to own no matter how cheap it looks
- Piotroski score – assigned a fairly high weighting so that the list removes “lotto” stocks
Growth stocks are created using
- TTM sales percentage change – to find growing companies but also limited to a certain upper percentage to eliminate high flyers
- 5 year sales CAGR – to find growing companies that are not perennial losers
- Gross Profit to Asset Ratio (GPA) – a wonderful measure of profitability to find stocks that are making the best use of their assets to generate sales
- Piotroski F Score – assigned a fairly high weighting so that the list removes “lotto” stocks
Action Score Performance
First, these are purely theoretical results that I use for my backtests.
- Full universe: includes ALL stocks. OTC & ADR
- Filtered universe: excludes OTC, ADR, financials, miners, energy, utilities
What I’ve found through experience is that backtests are a great to test theories and discover new ideas. No backtesting system is perfect and all the work I’ve been putting into developing a software application has revealed why paper results vary with real world results.
Aspects like using the data provider calculations vs my value variations.
Instead of doing everything exactly by the book, I like to adjust formulas slightly that make it work better.
For a more realistic representation, I manually created a model portfolio of 20 stocks.
Here’s how the filtered version (no OTC, ADR, financials, miners, energy utilities) of the Action Score looks at the end of Aug 18th.
Definitely different compared to the backtested numbers.
These stocks were added on Jan 4th. The first day of trading.
Now stay tuned for an email next week because I will send you a couple of free Action Score stock ideas.