Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. Join them; it only takes a minute:

Sign up
Here's how it works:
  1. Anybody can ask a question
  2. Anybody can answer
  3. The best answers are voted up and rise to the top

How can I compute the predicted return from a linear regression that includes a number of different terms. For instance, suppose my equation is:

$r_{future} = \alpha + \beta_1 r_{history} + \beta_2 x_{news} + \beta_3 r_{history} * x_{news} $

Where $r$ is the geometric return, and $x$ is a news dummy variable (0 or 1 depending on whether news existed).

Can I still conclude that the expected return $r_{future} = \sum \beta_i$?

share|improve this question

If the equation satisfies all the assumptions of OLS, particularly homoscedasticity and no autocorrelation in the errors, then the expected return for the equation you laid out is


If the unconditional expected return is zero (as is likely to be approximately true for short horizon returns), then


These types of return regressions usually do not satisfy the conditions for OLS, so your coefficients estimated using OLS or (more likely) your standard errors may be biased.

share|improve this answer

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

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