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This is when machine learning becomes almost an art. When you deploy machine learning in trading, you can use several tools to understand if the model has learned some input-output relationship or memorized that relationship.There is no"special" recipe to answer this question.
In your experience, what are the main challenges when applying machine learning to financial time series and portfolio construction?financial time series. We are now at a point where new researchers are taking these models forward to the next generation. But we're still at the inception of portfolio construction and machine learning.
It's very important that the researcher, who is myself in this case, can look through the model and see what the model has detected from the data samples provided.The biggest challenge for me was to understand the fundamental drivers of cryptocurrencies. In traditional assets, you have valuations, and people agree on valuation methods.
With all the technology we have at our disposal, I think it won't be very pleasant to see that the industry won't enjoy economies of scale in the future. I think asset managers will start being smaller. We've seen this already in the hedge fund space. Hedge funds that use many quantitative tools tend to run their operations tighter with a smaller number of people than you'd see in the traditional buy side.
Most of the AI and machine learning models today were built decades ago. We can use them today simply because we now have the computing power that enables us to run these models.