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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/131518

    Title: 利用股票市場圖形與機器學習配置最佳投資組合
    Stock Chart Pattern with Machine Learning to Construct the Optimal Portfolio
    Authors: 何聿涵
    He, Yu-Han
    Contributors: 黃泓智
    Huang, Hong-Chih
    He, Yu-Han
    Keywords: 機器學習
    Machine Learning
    stock chart pattern
    Date: 2020
    Issue Date: 2020-09-02 11:51:21 (UTC+8)
    Abstract: 近年隨著電腦技術及硬體設備的進步,人工智慧廣泛應用於各領域當中,因此本研究將利用其中圖形辨識之技術,搭配機器學習,期望創造高於台灣加權指數之報酬。
    In recent years, with the advancement of computer technology and hardware equipment, artificial intelligence is widely used in various fields. Therefore, this study will use the technology of pattern recognition and machine learning to create a reward higher than the Taiwan Capitalization Weighted Stock Index(TAIEX).
    This study uses the most common candle charts and volume charts in the stock market as database data, and performs two-stage compression and dimensionality reduction on the stock chart pattern, which are AutoEncoder and principal component analysis, and successfully reduce total of 500 data features.
    Input the data after dimensionality reduction into the XGBoost model, predict the stock price in the next 20 days, and use cross-validation to prevent the model from overfitting, and finally select 10 stocks as the investment portfolio to construct the optimal portfolio.
    Finally, this study evaluates the investment portfolio through empirical analysis. The backtesting period is from 2012 to the end of 2019 and 2012 to the end of May 2020. Before COVID-19, the investment portfolio deliver an annual return rate was 25.2% and the annualized Sharpe ratio was 1.44. After the epidemic was covered, although the maximum drawdown rate changed drastically, the annualized return rate was still 20.6% and the annualized Sharpe ratio was 1.17. Both of periods are better than TAIEX.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107358028
    Data Type: thesis
    DOI: 10.6814/NCCU202001231
    Appears in Collections:[風險管理與保險學系 ] 學位論文

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