Tactical asset allocation: An artificial neural network based model

An artificial neural network was trained to support a tactical asset allocation investment strategy. The allocation strategy considers three asset classes: U.S. stocks, bonds and money market. The neural net-work was trained to forecast the probability that each asset class would outperform the other two by the end of a one-month period. The neural network was trained with the backpropagation algorithm. A tactical asset allocation portfolio was invested in the asset class expected to have the best performance according to the neural network prediction. The strategy was simulated during a one-year period During the simulation period the strategy outperformed the S&P500 Index by 1,792 basis points. The artificial neural network prediction was accurate 92% of the time.