A question I sometimes ask when visiting a new company is ‘Whom, among the analyst community, do you think most reliably summarizes your prospects?’. This is of course a round-the-garden way of asking to whom do you drop the most timely and informed updates?
The paper highlighted this week, from Zengquan Li and T. J. Wong from the universities of Southern California and Michigan respectively takes a more holistic view of this process. By analyzing public data on common schools, hometowns, geography and business relationships of analysts and company managements they wonder if ‘connected’ analysis produce more accurate earnings forecasts? They do.
You’d have guessed that so there’s not much science there. What’s new is what the researchers found about how ‘connected’ analysts affect overall industry forecasts; they make them better. So much so that when a connected analyst retires industry forecast accuracy drops by nearly 12%.
The researchers rule out ‘herding’ i.e. the habit of less well informed analysts to simply crib the work of better informed peers. Instead they flag an information transit mechanism I can confirm first-hand is very much a fact of financial market information dissemination, institutional client’s loose lips.
The bottom line here is companies have favorite analysts (the paper relates to China but you know this is the case EVERYWHERE). These analysts either get information faster than peers or, in many cases due to shared connections*, just make more sense of it and that leads to more accurate forecasts; and that’s important why?
Not just because it helps we grubby stock-pickers make better investment decisions, it also helps reduce the company’s cost of capital; and this informal network and data distribution mechanism is especially important in a developing market like China where formal knowledge sharing channels (i.e. domestic rating agencies) are still works in progress.
You can access the paper in full via the following link Embedded Financial Analysts.
Happy Sunday
[* This week’s paper is a useful follow-on to one I flagged earlier this year on how language commonality led to more informed forecasts. That’s at Language Commonality (June ’18) if you missed it first time around.]