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I applaud this decision. If you can't explain the model, it means you don't know the assumptions that went into the model's output, which means you won't see i

• fjp 8y ago
  > If you can't explain the model, it means you don't know the assumptions that went into the model's output This is true, but there are many, many kinds of models that have basically zero explanatory power but have higher predictive capabilities than models that are easier to explain. They have been around a long time and are used for many different practical applications. Unfortunately, the draw of that seemingly infallible super-high-predictive capability will almost certainly be heavily involved in financial markets before long. I have no problem if some people want to risk a bunch of money in a hedge fund that uses neural net models or whatever else, but having enough money controlled by these models could pose a serious systemic risk.
  • wpietri 8y ago
    > having enough money controlled by these models could pose a serious systemic risk This is the part that worries me. For a decade before the 2008 financial collapse, people were quietly saying, "Gosh, there's a lot of activity in derivatives and we don't really know where the risk is going." One of many factors there was the way rating agencies gave very generous ratings to mortgage securities. Critics note that it was in their short-term financial interest to do that. If people can screw up that badly with models they supposedly understand, it seems to me to be even more risky when working with models where people have just given up understanding and put their faith in the AI oracle. As long as they get the answers that maximize their end-of-year bonus checks, they have a strong incentive not to dig deeper.
    • zeroname 8y ago
      In my estimation, existing models are just oracles with less variables. > One of many factors there was the way rating agencies gave very generous ratings to mortgage securities. Garbage in, garbage out.
• zeroname 8y ago
  Fair argument, but somehow I doubt you know too much about real-world finance. Not a lot of reason to be found there. Frankly, I think the problem here is "too new" or "too many variables". I doubt any of these managers understand Black-Scholes, yet they would sign off on using it because it's established, even though applying it also lost lots of people lots of money. "It outperformed" seems to me to be reason enough, in fact not using it something that outperforms could be construed as "mismanagement" just as well.
  • d--b 8y ago
    I’ve been working in a hedge fund as a quant dev for more than 10 years. Before that I worked in 3 banks. Most option traders definitely understand black scholes, but that’s not really the point, cause there are more complex models that they would use to trade without knowing the details of. The point is that there are quants who you need to trust with the models. And they’re most likely the ones who said: “this seems to work, but we don’t really know why, so we probably shouldn’t use it”. The fact that the top dogs agree with that is a sign of maturity.
    • zeroname 8y ago
      > I’ve been working in a hedge fund as a quant dev for more than 10 years. Before that I worked in 3 banks. I guess you know too much about it, then. > “this seems to work, but we don’t really know why, so we probably shouldn’t use it” The same could be said of human intelligence. With that attitude, you can just throw out machine learning altogether. Most models are not interpretable. It's too many variables. That's how the world works though, there are too many variables for you to ever understand cause and effect at any broader scale. That's especially true with finance, where models have relatively little predictive power anyway. I'm surprised to hear an "appeal to reason" coming out of that corner, of all places.