Garbage In... Radical Transparency Out?
from the one-hopes... dept
However, the good news is that, as a result of this mess, there may actually be some movement towards the solution a few of us have been suggesting for a while: radical transparency. Back in November, we suggested that public companies should be allowed to do away with quarterly reports in favor of real-time data dumps in standardized formats, that would allow anyone to build tools on top of the data to analyze it themselves. Rather than obscuring the real situation within companies, as is the case today, this would expose everything, and let anyone build tools to analyze the real underpinning fundamentals. It would also serve to get rid of the extremely damaging focus on "quarterly" returns at the expense of long-term thinking. And, finally, it would help combat the problem described above where everyone's relying on a black box to pop out risk metrics. Yes, many might adopt the same formulas, but by exposing all of the underlying data in a real-time format with a full API, anyone could structure their own system for reading the data and analyzing it. Then we wouldn't have silly situations where everyone believes that bundles of toxic mortgages have a AAA rating.
Of course, almost every discussion I've had with anyone about the subject had people saying the concept was so insane no one was actually thinking about it. Turns out that might not be entirely true. Daniel Roth discusses an almost identical plan in Wired, suggesting that the idea isn't so far-fetched after all. That doesn't mean anyone is going to implement such an idea any time soon, but at least the idea is out there and permeating and getting some attention. It may take a while, but eventually people will begin to realize that it makes much more sense than anything else going on these days. We're not going to fix a broken Wall Street by throwing extra money at the problem, but we might be able to fix it by opening up, adopting radical transparency, and then letting the market more accurately value things based on real data.