This paper examines the convergence rate and mean-square-error performance of momentum stochastic gradient methods in the constant step-size and slow adaptation regime. The results establish that momentum methods are equivalent to the standard stochastic gradient method with a re-scaled (larger) step-size value. The equivalence result is established for all time instants and not only in steady-state. The analysis is carried out for general risk functions, and is not limited to quadratic risks. One notable conclusion is that the well-known benefits of momentum constructions for deterministic optimization problems do not necessarily carry over to the stochastic setting when gradient noise is present and continuous adaptation is necessary. The analysis suggests a method to enhance performance in the stochastic setting by tuning the momentum parameter over time.