Algorithmic specified complexity (ASC) is an information metric that measures meaning in an event, based on a chance hypothesis and a context. We prove expectation of ASC with regard to the chance hypothesis is always negative, and empirically apply our finding. We then use this result to prove expected ASC is conserved under stochastic processing, and that complexity for individual events is conserved under deterministic and stochastic processing.