• K

In data we trust

Yesterday, Bloomberg reported that data coming out from China was massively under-reported (which was not unprecedented at all). It's not about China, but countries all over the world are being engaged in "pie throwing", hurling accusations at each other over the cleanliness of their data, what's right and what's wrong. Everyone wants to claim the global authority on publishing their numbers. It's not healthy for investors around the world.

For the longest time, analysts and business leaders of large corporations globally have been using information provided from government authorities, regulators and organizations which are known to hold reliable sources of data. All of decision making involving billions of dollars basically rests on that, if you think about it.

If something on a scale this large can be ‘mis-reported’, the world wonders what else could have been wrong - GDP numbers? PMI data? Household disposable incomes? Who’s providing the right data? And who stands to gain from releasing these stats?

Valuation theory attempts to codify how investors and professionals around the world agree on asset pricing, which are mostly based on future expectations - Expectations derived from the collection and analysis of macro-economic data, interviews, research and expert opinions.

If you think about it: the whole purpose of valuation is not about getting a ‘correct’ number on the price but to allow analysts and investors to run scenarios and make data-driven decisions.

If those inputs don’t reflect correctly what is going on in the market, then decision making becomes ill-informed and the whole process of asset pricing breaks down. Garbage in garbage out.

The efficient-market hypothesis is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.

If you can’t even trust underlying data and analytics, what can you trust?



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