This paper investigates the out-of-sample forecast performance of the autoregressive fractionally integrated moving average [ARFIMA (0,d,0)] specification, both when the underlying value of the fractional differencing parameter (d) is known a priori and when it is unknown. Forecast performance is measured relative to simple deterministic models and a random walk model, for forecast horizons up to 100 periods ahead. Overall, the linear models tend to outperform the ARFIMA specification for both the positive and negative values of d for the simulated series, and for positive d values from the real time-series data. The results of the study question the use of the ARFIMA specification as a forecast tool.