This research examines the use of various forms of time series models to predict the total NAV of an asset allocation mutual fund. In particular, the mutual fund case used is the Vanguard Wellington Fund. This fund maintains a balance between relatively conservative stocks and bonds. The period of the study on which the prediction of the total NAV is based is the 24-month period of 2010 and 2011, and the forecasting period is the first 3 months of 2012. Forecasting the total NAV of a massive conservative allocation fund, composed of an extremely large number of investments, requires a method that produces accurate result. Achieving this accuracy has no necessary relationship to the complexity of the variety of the methods typically present in many financial forecasting studies. Various types of methods and models were used to predict the total NAV of the Vanguard Wellington Fund. The first set of model structures included simple exponential smoothing, double exponential smoothing, and Winter′s method of smoothing. The second set of predictive models used represented trend models. They were developed using regression estimation. They included linear trend model, quadratic trend model, and an exponential model. The third type of method used was a moving-average method. The fourth set of models incorporated the Box-Jenkins method, including an autoregressive model, a moving-average model, and an unbounded autoregressive and moving-average method.