202601121442
Status: #reference
Tags: Machine Learning, Financial Machine Learning
State: #nascient

Financial Machine Learning

I was prompted to create this note since Advances in Financial Machine Learning by Prado made the distinction between regular machine learning and this subject. In fact, it says this:
One day in the near future, ML will dominate finance, science will curtail guessing, and investing will not mean gambling. I would like the reader to play a part in that revolution. A third motivation is that many investors fail to grasp the complexity of ML applications to investments. This seems to be particularly true for discretionary firms moving into the “quantamental” space. I am afraid their high expectations will not be met, not because ML failed, but because they used ML incorrectly. Over the coming years, many firms will invest with off-the-shelf ML algorithms, directly imported from academia or Silicon Valley, and my forecast is that they will lose money (to better ML solutions). Beating the wisdom of the crowds is harder than recognizing faces or driving cars. With this book my hope is that you will learn how to solve some of the challenges that make finance a particularly difficult playground for ML, like backtest overfitting. Financial ML is a subject in its own right, related to but separate from standard ML, and this book unravels it for you.

So yeah, in light of that, it makes sense to create a reference note for it.

The book Advances in Financial Machine Learning by Prado is interesting as among other things, it claims that one should endeavor to be part of a team since there are many fields of interest and everyone in the team should be an expert.

Furthermore, you should be a High-Performance Computing (HPC) expert, so I guess I likely should tap into that.

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