2/7/2015 Fundamental Parameters

Sector Model	XLU	0.00%
Full Model	Date	Return	Days
ESI	8/4/2014	-48.57%	187
EDU	10/27/2014	-16.60%	103
UVV	12/2/2014	19.84%	67
JOY	12/8/2014	-12.90%	61
RS	12/11/2014	-12.15%	58
JNPR	1/5/2015	4.37%	33
COG	1/12/2015	-10.66%	26
GNW	1/20/2015	6.30%	18
AGCO	1/23/2015	10.16%	15
DNR	2/4/2015	3.13%	3
(Since 5/31/2011)
S&P	Annualized	12.17%
Sector Model	Annualized	24.99%
Full Model	Annualized	20.35%

 

A little better tape this week:

The Sector Model flipped over to XLU after that sector suffered an alarming collapse on Friday.

The Full Model is still down for the year, but has recovered most of its lost ground.  ESI continues to be a plague, with attempted rallies that quickly fizzle.  Had I bought it one day later I’d be flat, but that first day was a disaster.

So where does that leave me?

Quite well on the revised fundamentals, actually:

Greenblatt	-14.44%
Graham	8.60%
Adaptive	-12.09%
Matrix	-18.70%
5 Year Sales Growth	4.04%
Adaptive-2	745.30%

 

This is a record of stocks based on fundamental parameters across all time frames.  I keep track of stock performance even after I sell the stock, and record the average return rate for those stocks within the fundamental filter that was applied to them in the selection process.

The Greenblatt and Graham parameters are those defined in Reese’s “Guru Investor” and used on the model portfolios on www.validea.com.

The Adaptive formula was my first attempt at an evolutionary process.  The correlation tables were not well designed, and so I moved onto my Sector and Style Matrix – which led to the ESI disaster.

The 5 Year Sales Growth was a complete accident.  I was attempting to reconstruct a Graham model and had an error in my script that only retained the final sort.

So, two accidents and a disaster – but the second accident was a fortunate one that led me to re-evaluate the concept for an adaptive correlation matrix.   Obviously the returns are wildly inflated by short term moves in the market, but I do know that the algorithm is sound, and the results make sense to me:

Ø  Debt: should be low relative to capital.

Ø  Volume: should be high (i.e. not some kind of thinly traded micro-cap).

Ø  Long term Beta: should be high (if I want to out-perform the market, then I have to find a stock that moves more than the market).

Ø  Analyst estimates for earnings, book value growth, cash flow, and return on capital: all should be low.  I want a stock that analysts despise so much that they have chased away everyone who was likely to sell.

Ø  3 and 5 year stock return: disasters. You can’t buy low unless the stock is low.

Ø  Sales Growth: 1 year, 5 year, 10 year sales growth should all be high.

Basically the model wants a low debt company that is growing faster than the stock, and has a lot of talking heads scaring people away. And it wants to find that stock when its entire industry has been blown by extreme panic.

Granted, the new adaptive filter has only been in place for about 6 weeks, but the metrics make sense to me and they are performing well so far.  My goal is only to outperform the Sector Model, hopefully by about 5% a year.  If I get a 10% kick I’ll be pleased beyond belief.  The 745% is just an illusion of timing, so we’ll have to wait and see.  But I feel that I’m on the right track on the Full Model for the first time since I began experimenting with fundamentals back in November 2011.

As my friend Bill Spetrino (of Dividend Machine fame) likes to say, “time, as always, will tell”.

Tim