# Determining significant difference between to return series

I want to analyse whether two return series are different. I was told to run the following regression:

diff = return series 1 - return series 2
constant = beta * diff


Where I set the constant equal to 1. I use a t-test to evaluate this regression with HAC standard errors. In R this would look like this:

diff <- as.numeric(series1[startRow:endRow]) - as.numeric(series2[startRow:endRow])
reg <- lm (formula = diff ~ 1, na.action = na.omit)
coeftest(reg, vcov=NeweyWest(reg, lag = 1, prewhite=FALSE), df=length(diff)-1)


The results are as follows:

t test of coefficients:

Estimate | Std. Error | t value | Pr(>|t|)
(Intercept) -8.7425e-05 | 9.3240e-05 | -0.9376 |  0.3485


However, I do not know how to interpret the results. What does the constant do? What does the beta mean? What does it mean when the results are significant or insignificant?

(Problems with my sample: return series are dependent & individual return series are non-normal)