This is a question about the David Laibson Video. The question I would ask was sparked by a quick diversion that the conversation took. He mentioned that the stock market is a combination of many peoples forecasts and predictions. But, he mentions that there isn’t enough data to incorporate machine learning in order to make a forecast of the stock market. I am curious what he means by this and would ask him to elaborate. Is it that there aren’t enough data points or that there is too much unknown. I assume it is the latter, but I would be really interested to hear what kind of unknowns would be necessary to make this kind of prediction.
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I also had the same question when watching his interview. If I am not mistaken, there are some "bots" (although I am not sure if they are AI or not) that sell, buy and hold stocks depending on their trend. That said, I would also have liked to hear from him what exactly are the difficulties in developing a model for the stock market through AI.
I think when it comes to the stock market, my bet it has to do a lot with unknown unknowns. It is probably impossible, even for a perfectly intelligent machine or being, to accurately predict where the stock market is going to go just based on past stock market data. I mean you could use it a little, but really only a little. So as a result you need to think about all sort of outside factors from politics to big movies coming out. It's really hard to know and classify all the different possible factors, which is I think why it is hard.