I vaguely recall some academic work showing this to be true, or more generally if you're predicting the variable X_t over time, the previous period's value tends to be a better predictor than more complicated models.

These get called AR(1) models, for autoregressive 1.

Most complicated models that I'm familiar with include both the previous value and other factors (since there is generally more going on than a random walk).