Research on Market Transaction Based on Dynamic Programming Model
DOI:
https://doi.org/10.54691/bcpbm.v26i.1944Keywords:
dynamic programming; market transaction; wavelet transform; time series model; backpropagation neural network.Abstract
Based on the principle of dynamic programming model, this paper studies the market transaction problem and establishes a transaction model to help decision-making. First, on the basis of the historical price data of gold and bitcoin, the data is processed by the wavelet transform method to ensure the accuracy of the model. Second, a time-series forecasting model and a back-propagation neural network are employed to predict the future prices of gold and bitcoin. And use a dynamic programming model to recursively determine the specific investment ratio of each asset in each transaction to achieve the best return. Finally, the effect of the model is verified by the evaluation index and transaction cost sensitivity test. The results show that transaction costs affect both strategy and outcomes. As transaction costs increase, both transaction frequency and total assets show a downward trend.
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