The answer may surprise you!
Neurologists learn in medical school that the brain can't change, isn't plastic, can't repair itself; MBAs learn that the global investment market is efficient.
No matter what data, experiments and research show, dogmas continue.
Thus, neuroscientists have proven over and over again how the brain adapts, heals - sometimes astonishingly fast (Read 'The Brain that Changes Itself' by Norman Doidge or 'The Tell-tale Brain' by VS Ramachandran for the fascinating experiments and evidence) but neurologists, by and large, continue with the dogmas they were taught.
Finance professionals stick to the Efficient Market Theory, disregarding the evidence of their own eyes - in an extraordinary display of ostrich-like behaviour.
After all, if a stock price can easily have a 50% range in a quarter with no fresh news flow, how can every price be 'right and efficient'?
Another instance, look at the calculation of Beta - try actually doing it for actual stocks and you will come up with completely different values for different time periods or phases of the stock market, meaning the calculated value is not something which can be used for any predictive purpose.
For example, often you will find that an outperformer rises more than the market in an upmarket and falls less than the market in down markets.
In the last few years, such stocks on the Indian stock market have included names like Mindtree, Deepak Nitrites, Havells India etc; in the US these have included Nvidia, Eli Lily, Target, AMD and several others.
The question: is the Beta of such stocks less than one or greater than one, because the calculated Beta will be over 1 in bull phases and less than 1 in bear phases.
This is at the simplest level without going into any arcane theory or mathematics.
The truth is that neither the assumptions, nor the assertions of an efficient market hypothesis come through in real life.
Moral of the story: Please put Data before Dogma.
Look for evidence and open your eyes to the data instead of seeing what you want to see.
And yes, your teachers and textbooks may have been wrong!
#DatabeforeDogma
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