In this paper, we apply stochastic (backward) automatic differentiation to calculate stochastic forward sensitivities. A forward sensitivity is a sensitivity at a future point in time, conditional on future states (ie, it is a random variable). A typical application of stochastic forward sensitivities is the exact calculation of an initial margin valuation adjustment, assuming the initial margin is determined from a sensitivity- based risk model. The ISDA Standard Initial Margin Model is an example of such a model. We demonstrate that these forward sensitivities can be obtained in a single stochastic (backward) automatic differentiation sweep with an additional conditional expectation step. Although the additional conditional expectation step represents a burden, it enables us to utilize the expected stochastic (backward) automatic differentiation: a modified version of the stochastic (backward) automatic differentiation. As a test case, we consider a hedge simulation requiring the numerical calculation of 5 million sensitivities. This calculation, showing the accuracy of the sensitivities, requires approximately 10 seconds on a 2014 laptop. However, in real applications the performance may be even more impressive, since 90% of the computation time is consumed by the conditional expectation regression, which does not scale with the number of products.