Returns-based style analysis is widely used due to its relatively light input data requirements. All you need are fund returns and premia for the style factors you want to evaluate.

Kernels can be populated with whatever modules the project requires when you're doing code-focused explorations, such as the Python Data Analysis Library (pandas) and SciKit-Learn (sklearn). I ...

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