This could be renamed "Common Machine Learning Mistakes". Great advice all around, of course. Insufficient data, lack of or incorrect data prep, and poorly defined success criteria are problems that plague all forms of machine learning.
As other commentors pointed out, the biggest problem with stock market modeling is with trade execution and automated trading. A skilled ML practitioner will know how to deal with the size of the data sets and the normalization of the data. Placing the trades properly and taking into account the myriad sources of information available will give even experts a hard time.
As other commentors pointed out, the biggest problem with stock market modeling is with trade execution and automated trading. A skilled ML practitioner will know how to deal with the size of the data sets and the normalization of the data. Placing the trades properly and taking into account the myriad sources of information available will give even experts a hard time.