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- W2130051946 abstract "An artificial neural network was trained to support a tactical asset allocation investment strategy. The allocation strategy considers three asset classes: US stocks, bonds and money market. The neural network 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." @default.
- W2130051946 created "2016-06-24" @default.
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- W2130051946 date "2002-11-13" @default.
- W2130051946 modified "2023-09-23" @default.
- W2130051946 title "Tactical asset allocation: an artificial neural network based model" @default.
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- W2130051946 doi "https://doi.org/10.1109/ijcnn.2001.938437" @default.
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