Ordinary least squares (OLS) regression is probably the most commonly used statistical method in quantitative finance (and likely in other quantitative fields). It is very fast to compute, and the results are often quite interpretable. Due to its simplicity, it serves as the cornerstone for many more complex statistical or machine learning models. Also, it has been studied so thoroughly historically, that many of its limitations can be covered by various techniques. For example, the original OLS model treats all instances in the training set of equal importance, and one of the common approaches is to introduce weights on the instances to reflect our beliefs.

In this article, we aim to introduce a systematic and elegant approach to incorporate history’s relevance to the regression process.

Briefly speaking, this method ranks all history instances based on their “relation” to the current input independent variables and selects those that are more informative and similar to regress on.