| dc.description.abstract | With the advancement of technology, financial technology (Fintech) has become the main topic of many researches, so using technology to help people make portfolios is also one of the key researches. In the financial market, high-return products are accompanied by high risks. Therefore, how to effectively allocate the weight of different investment targets while maintaining a certain level of return is also a major research topic. In this research,
we propose a method that uses an LSTM-based autoencoder and an autoencoder that adds a predictive decoder to try to extract deeper features from complex and changeable
financial market data, and Borrow new features. RankSVM, which learning to rank, tried to select the top 10 stocks with the biggest gains and declines next week from the 30
stocks in the Taiwan stock market, and used these 10 stocks as a weekly portfolio. After that, we conducted a backtesting on the cumulative return for 2019 and 2020. From the perspective of profitability, when the overall environment trend is obvious, adding a combination model of predictive decoders can increase the return. However, when the market is in a consolidation state, only by passing data through the LSTM autoencoder can the rate of return be maintained. As the global stock market is hit hard by the COVID-19 in 2020, we found that after more similar incidents enter RankSVM, the ability to respond to major incidents will be improved. Finally, we compared the mean-variance model. and Taiwan Top50 Tracker Fund (TTT). It turns out that our weekly portfolio is generally more profitable than the mean-variance model and the TTT. | en_US |