RE: THE MOST COMMON ERROR IN STOCK MARKET RESEARCH

Jeff Miller of Dash of Insight wrote a brilliant post on common research mistakes. It can be found here.

He breaks it down into two things:  Selecting the Right Data and Comparison.

Selecting the Right Data.  As the saying goes, if you torture the data for long enough it will tell you anything.  There is so much data out there that you can make it say whatever you want.  The most important thing about finding the right data is accepting the limitations of it and understanding it.  Look at this way, I like a fast car but insurance and gas will cost more. Does that justify the tradeoffs for me?  Yes.  You?  Maybe.  All data is local.

Comparison.  Mr. Miller calls is comparison but I prefer the word context.  Most anyone can look at a data or chart and come to a reasonable outcome.  Data records it does not tell the story.  It can can tell a story but for the most part they are without context.  Yes you can throw a couple more pieces of data in and get closer but who knows.  Think about how ridiculous it would sound if I showed you a picture from 1950’s Chicago and asked you what it was like.

What Mr. Miller didn’t mention, probably because of time or my inabilities, is that data is secondary to what we do with it.  For the most part we are untrained at using it.  We are 14 year olds at the Playboy mansions. We get data F@#%&ed, data blocked, data complacent or whatever you want to call it.

I don’t give a F@$% about data.  What I want to know is how to use it and then I can decide if it is right for me.  The issue is that people do not understand sunk costs.  They, me included, spend so much time learning to find it that they are afraid to use it.  Because once it is used and it doesn’t work you are the hook for it.  The work is not done because you can tell within a reasonable tolerance where the market is going to go, the point is make enough money to compensate the risk and time you spend. That is it.  I do not care about being or sounding smart, I can’t pay for anything with that. (Obviously people get paid to write and talk and produce research but if it does not eventually produce profits for the audience it wont last.)

Most of the time they throw the data out with the bathwater.  They going in with one idea and once they realize it is something else they get rid of it. Never thinking about why they felt that way in the first place. I am not saying there is not ineffective data but there is more ineffective use of data.

It is not the data it is the execution.

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