Everybody has access to Bloomberg and Google. Every global macro investor closely follows macro data out of every country. To gain an an edge, one must look beyond headline numbers, and find underutilized datasets.
This applies when finding countries, industries, and individual companies in which to invest. Any time you want to combine top down and bottom up insights, you need to get creative with finding the right data.
Schumpeter and Perez
Joseph Schumpeter pointed out that aggregate figures “conceal more than they reveal”.
Relations between aggregates are
“entirely inadequate to teach us anything about the nature of the processes which shape their variations, aggregative theories of the business cycle must be inadequate too…”
In Technological Revolutions and Financial Capital, ( Carlota Perez emphasizes that new technological paradigms can only be analyzed by looking closely at inner workings of an economy. Within the same country, or industry some subsectors will grow at astonishingly high rates, while others decline. Perez’s framework is valuable to analyzing times of great technological change, which is basically anytime. Examples she uses include the first British Industrial Revolution( the age of Steam and Railways, the Age of steel, electricity, and heavy engineering, age of oil, automobile and mass production.
Top line numbers such as GDP or earnings could deceive an analyst, especially when looking at a new market.
Valuation and pricing
“People living through the period of paradigm transition experience real uncertainty as to the ‘right’ price of things(including that of stocks, of course).”
Extreme jumps in productivity change relative price structures in the economy. “The change in relative price structure is radical and centrifugal. Money buying electronics and telecommunications today does not have the same value as money buying furniture or automobiles.” Therefore, looking at inflation or deflation in aggregate is deceptive. Many years after Perez’ book, this now exacerbated by the Amazon effect. To some effect this may impact valuation in some industries.
Long term aggregate data, spanning multiple periods of technological change are senseless. This goes for GDP, corporate earnings etc. Yet disaggregated stats are rarely available(except during more stable phases), as Perez points out.
The internet has provided more opportunities to find disaggregated, unique, underutilized datasets. Often this means poking around on weird regulatory websites, and following up on footnotes to academic papers.
This process might be about to get a lot easier.
Google launched a new dataset search engine. I’m excited to see how its impact snowballs as more datasets are added. Although intended for journalists, it is likely to be a valuable tool for investors seeking differentiated alpha.
Of course that means today’s edge, will be tomorrow’s table stakes.
See also: The hard thing about finding easy things