| Summary: | 碩士 === 淡江大學 === 經濟學系碩士班 === 106 === This research aims to develop the system for Robo-Advisor to perform time-series forecasting and portfolio optimization in automated investment management. We designed the core algorithm, artificial intelligence to perform time-series forecasting in the financial market, as part of the system for Robo-Advisor in automated investment. There are many studies in algorithmic trading and portfolio management in various forms of applications; however, there is a paucity of literature focusing on the applications which are designed for Robo-Advisors. In this research, we used a deep learning approach, Long Short-Term Memory (LSTM), to develop our algorithm to solve the complexity of sequence dependence in time-series forecasting. We developed an automated system to perform time-series forecasting and used the result to construct a day trading strategy and to perform portfolio optimization to show that LSTM based algorithm added value. The system we built is expandable and can be used as a framework when developing Robo-Advisors for automated investment management.
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