Recently I was asked by a good friend of mine, to explain with data, the analytics of decoupling of the real economy and the financial market, or in other words the decoupling of the main street and the wall street. The whole context of the discussion was based on the few months of the pandemic aftermath when the main street economy or the real economy came to a stop whereas the stock market kept going up. One may find many resources explaining the economics logic of it like stimulus liquidity, futuristic outlook etc., what I concentrated was more on how we are looking at the data.
First what I tried to do was avoid trying to understand the broad economy looking at the index. Indexes like Nifty50 – example for India may not always be the best way to understand the broader market. (My explanation will be based on India financial market, but I am sure the same can be applied to any country financial market). The NIFTY Index is reconstituted every six months. And it combines different companies in different weightages. So, in a way it can be said it represents the best or most optimistic representation of the economy.
Next what I did was I created an index of my own – while trying to represent companies from different caps and industries in equal weightages. Below show the two indexes –
So, what we saw, though both were impacted by the pandemic in equal way, the broader market hasn’t been doing well since 2018, whereas the main indexes have been doing better. I left it to the economists and administrators to understand what may have led to this. May be consolidation of markets towards large caps, more index investing etc. What I suggested my friend was he should not only look at main indexes as indicators of the economy but try to understand the broader market a whole.
But my takeaway was how we look at a data and how well we understand the domain makes a huge difference how well we analyze the use case.