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?


1 Answer 1


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

  • $\begingroup$ Thank you for your response! $\endgroup$
    – amars96
    Commented Jul 12, 2019 at 14:01
  • $\begingroup$ could u give some examples of concurrency in observations? for example when would prices and returns be concurrent, since I think observations in neither of these series would be concurrent amongst themselves in their own respective time series, but rather, returns would be concurrent with prices because returns are a function of two price observations $\endgroup$
    – develarist
    Commented Jul 3, 2020 at 15:11
  • $\begingroup$ @develarist you can find some examples and detailed descriptions in the blog post: hudsonthames.org/… $\endgroup$ Commented Sep 22, 2020 at 7:48

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.