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Suppose I want to estimate the following regression: $R_t=\alpha + \beta X_{t-1} +\epsilon_t$. Where I use asset returns as the dependent variable. Both overlapping as well as non-overlapping returns can be used as the dependent variable. Which considerations do you have to make to choose between these two? What are the advantages and disadvantages of both approaches?

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Actually, overlapping samples is a big problem in financial machine learning which is called concurrency. Marcos Lopez de Prado discusses this issue in Chapter 4 of his book

Advances in Financial Machine Learning

Ideally, non-overlapping returns should be used to train the model, however this constraint massively decreases the length of your training dataset that is why you need to solve this problem in other way. If you use ensemble methods (Random Forest, Bagging Classifier), Sequential Bootstrapping is the answer. To answer your question, prefer non-overlapping returns if it doesn't decrease the length of your dataset massively.

Note: I am one of the authors of mlfinlab package which implements concepts described in Marcos' book. Project link: https://github.com/hudson-and-thames/mlfinlab

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  • $\begingroup$ Thank you for your response! $\endgroup$ – amars96 Jul 12 at 14:01

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