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- W3100549903 abstract "In asset allocation and time-series forecasting studies, few have shed light on using the different machine learning and deep learning models to verify the difference in the result of investment returns and optimal asset allocation. To fill this research gap, we develop a robo-advisor with different machine learning and deep learning forecasting methodologies and utilize the forecasting result of the portfolio optimization model to support our investors in making decisions. This research integrated several dimensions of technologies, which contain machine learning, data analytics, and portfolio optimization. We focused on developing robo-advisor framework and utilized algorithms by integrating machine learning and deep learning approaches with the portfolio optimization algorithm by using our predicted trends and results to replace the historical data and investor views. We eliminate the extreme fluctuation to maintain our trading within the acceptable risk coefficient. Accordingly, we can minimize the investment risk and reach a relatively stable return. We compared different algorithms and found that the F1 score of the model prediction significantly affects the result of the optimized portfolio. We used our deep learning model with the highest winning rate and leveraged the prediction result with the portfolio optimization algorithm to reach 12% of annual return, which outperform our benchmark index 0050. TW and the optimized portfolio with the integration of historical data." @default.
- W3100549903 created "2020-11-23" @default.
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- W3100549903 date "2019-08-27" @default.
- W3100549903 modified "2023-09-22" @default.
- W3100549903 title "Artificial Intelligence for ETF Market Prediction and Portfolio Optimization" @default.
- W3100549903 hasPublicationYear "2019" @default.
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