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- W4317383797 abstract "Quantitative investment and trading are key considerations for investors. Among them, quantitative investment and trading of stocks are particularly important. In order to quantitatively invest in stocks, this paper builds a stock prediction model based on ARIMA-GARCH-MLP (summation autoregressive moving average model-summation autoregressive moving average model-multilayer perceptron), which has been used as a quantitative investment transaction. Taking the data of 2020 as the research object, three control groups are set up for analysis, and optimized weighted predictions are made. The results show that the combined prediction model has excellent prediction accuracy and robustness, with the R2 and MAE being 0.988 and 0.146, respectively. The weighted machine learning model constructed in this paper can better predict stocks, so as to provide quantitative suggestions for investors' decision-making." @default.
- W4317383797 created "2023-01-19" @default.
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- W4317383797 date "2022-12-11" @default.
- W4317383797 modified "2023-10-16" @default.
- W4317383797 title "Research on stock quantitative investment strategy based on ARIMA-GARCH-MLP Model" @default.
- W4317383797 doi "https://doi.org/10.1109/tocs56154.2022.10015947" @default.
- W4317383797 hasPublicationYear "2022" @default.
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