Investment Philosophy: Difference between revisions

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A high level of confidence and understanding also allows us to differentiate signal from noice and panic first once real problems evolve.   
A high level of confidence and understanding also allows us to differentiate signal from noice and panic first once real problems evolve.   


== 100% Transparent logic based consistent investment models ==
== 100% Transparent logic based investment models ==
We believe investing is no rocket science but deeply logical and can be understood with common sense.  
We believe investing is no rocket science but deeply logical and can be understood with common sense.  


In fact when doing hypothesis testing of cause-effect relationships we do look both for strong historical correlations <u>and</u> logical explanations for those correlations.
In fact when studying industries, companies, balance sheets we strive to understand the unique situation and are not blindly applying ratios.
 
We do <u>not</u> accept a correlation as true and build it into our models if we cannot understand the root cause of it.  


When doing hypothesis testing of cause-effect relationships we do look both for strong historical correlations <u>and</u> logical explanations for those correlations.


We do weigh our confidence in a correlation significantly lower if we cannot understand the root cause of it and disregard it completely if have good reasons to believe that it is coincidental.
We explicitly <u>invite you</u> to challenge all our assumptions if they are not logical or feel inconsistent to you, or you think we are missing an important point. We will then take your considerations into account, revisit the assumptions and correlations if needed or simply do at better job of mapping out the logic of those assumptions better and increase the transparency of the model.  
We explicitly <u>invite you</u> to challenge all our assumptions if they are not logical or feel inconsistent to you, or you think we are missing an important point. We will then take your considerations into account, revisit the assumptions and correlations if needed or simply do at better job of mapping out the logic of those assumptions better and increase the transparency of the model.