Financial Artificial Intelligence : Overcoming the Bangladeshi Butter/ Long Term Capital Problem
Financial Artificial Intelligence : This helps to mitigate the risks of spurious correlations. To be sure, a lot of these investment styles we hold in a positive light will turn out to be a result of spurious correlations; they will not be good indicators of stock performance in the future.
However, by relying on many different investment styles in order to make our investing decisions, we can make sure that at least some of our factors are meaningful, and thus the fate of the fund does not rely on making sure any 1 investment style will continue to perform in the future.
A corollary to this is that using this modified Bayes learner to make predictions about our stocks, is that it is more resilient to changess in future performance of investment styles. As we know, sometimes one investment style can be superior to another style for very long periods of time. Throughout the 90’s, growth strategies were significantly better than value strategies, and after the dot-com crash, suddenly value again reigned supreme.
Thus, even if a certain investment style tends to do well over the long-term, it can certainly suffer prolonged periods of lackluster performance. Because our investment decisions rely on such a variety of different investment styles, the effect of a couple styles suddenly switching their performance characteristics is mitigated.
So, to wrap things up, we can see that long-term investing horizons gives some hurdles to applying artificial intelligence then many other fields. However, with a certain amount of foresight and careful design of the learner, these challenges can be surmounted and I fully expect to see machine learning algorithms playing a larger part in actually investing in the stock market, as opposed to just trading in it.
Written by Jeremy Newton