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The result would be some naive description of reality, like pre-scientific religious cosmology, a wrong but sophisticated enough 'model' based on 'current consensus'.)Because all one needs is to bullshit one's boss or sponsor. And bullshitting is much more efficient strategy than actual modeling and rigorous research.

Group thinking is much easier and cheaper than proper science, especially when even the best economists are full of doubts.

I took Stanford's financial markets and statistics classes, and those are far behind and unaware of what's currently being used in the industry (I've worked in two highly successful HFT groups since Stanford).

Well there are 40 different exchanges with which to trade in the US alone, Canada adds another 10.

Moreover, the very nature of the markets are too complex to model, especially using such data sources.The activity in advance of a merger should be quite visible.Tracking elected officials should reveal who influences whom, and who's bribing whom.Fines and fees are only anathema when the costs exceed the gains gotten from illicit behavior.Otherwise, paying fines without admitting guilt is the corporate way.And even in states where that is illegal, 501(c)4 / Super PACs allow politicians to do whatever they want with the money with no oversight. I doubt anyone would reveal it, and I don't see why they wouldn't - for the unscrupulous (there are plenty of them), it could be very valuable.With laws like that, why even bother to investigate bribery? Remember the story about Uber tracking journalists, for example. I'm guessing that even in the US, mobile networks only share aggregate or anonymized data about customer location, and don't allow for spying on specific individuals.I took Yale's financial markets courses, so I know what ideas they might have. The bosses in the typical quant firm are also quants themselves, and have no trouble following the rigor used to justify the validity of a trading strategy.Sure, at a certain point in the chain, you're talking with someone (eg.For example, a hedge fund could know how many people went inside a Wal-Mart over the last few months based on cellular location data.If there's an unfavorable downturn in store traffic, the hedge fund can shift its investments based on information that Wal-Mart won't report for another few months.

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