| Summary: | 碩士 === 國立中山大學 === 財務管理學系研究所 === 107 === This study constructs a Robo-Advisor with the theme of asset allocation. First, to clearly understand the category of the fund, the classification model is used to classify each fund. There are US market, european market, japanese market, asian non-Japan. Markets, emerging markets, corporate bonds, high-yield bonds, emerging market bonds, and long-term US Treasury bonds in fund categories. Then proceed with the allocation of index. This study uses common indices for asset allocation. The result of index allocation is used as the basis of the construction of the follow-up fund portfolio and the benchmark for backtesting. Finally, perform the fund selection by two fund selection methods: the Dominant Strategy and the XGBoost Scoring Model. The former selects funds by historical performance; the latter is the inputs of the technical indicators and the macro indicators for XGBoost training. Each fund will be scored every training, and the funds with higher scores are selected as the components of the fund portfolio.
The major empirical results are as follows:
1. The Dominant Strategy had higher return and risk in the historical performance.
2. Although the XGBoost Scoring Model has not stabilized over the Dominant Strategy in the historical performance, its risk has a lower volatility than the benchmark or the Dominant Strategy. Especially, the XGBoost Scoring Model had higher return and lower risk in the bear markets.
The XGBoost scoring model proposed by this study is suitable for the construction of Robo-Advisor because of its lower risk. It could help investors build a profitable and riskless fund portfolio.
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