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This simply suggests the linear model is a poor fit in high frequency. But is this that surprising, even before you crunch the numbers? I argue not, for the following reasons: Even at low frequencies (i.e. monthly or annually), it is known that the classical CAPM (which is what you're running, albeit at a much higher frequency) does not fit well. It'd be ...


A high R-squared (1.0) means that you can explain the movements of one time series using the other. The lower your R-squared is, the worse your explanation is -- that includes the 'quality' of your beta. You can try to improve your R-squared score using different regression types. Beware of overfitting.

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