Some of the readers here may remember my previous mentions on digging deeply into obscure information that’s publicly available
Some of that is data-oriented and covers everything from tax records, court filings, police reports, durable equipment purchases, and real estate transactions
Some of it is more ephemeral such as employee chatter, patterns of response to investor inquiry, visitor traffic at corporate offices, and social media.
Over a period of time I developed Project V-ger, who’s mission was to “learn all that is learnable and report to its creator” (Trekkies understand)
In short, it is a data mining operation to collect and report information and once it is “tuned into the targets” related to a company, it collects and condenses relevant information for a higher level review by a human (me)
The biggest problem was the sheer volume of collected information and the time needed to review it. The collection process was greatly amplified and it did pan many useful nuggets of information.
Which brings me back to the topic here, GSPE
One of the V-ger data filters that I had trouble tuning was “quiet periods” and how to notice subtle differences in employee chatter, investor inquiry response, and social media.
I can detect the pattern changes but it is very difficult to develop an automated filter to do that.
I have picked up on a definite pattern change in GSPE in a couple areas that I believe points to developments with partners other than Delek
Obviously, I am not going to be more specific on the data inputs or references (If anyone is serious about a discussion about “buying in” on the development of V-ger, you’re welcome to contact me)
I’m simply putting it out there for anyone to take or leave as they see fit
spec
News 
Market Data 
Discover 
