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Stock Chart Pattern with Machine Learning to Construct the Optimal Portfolio
stock chart pattern
|Issue Date: ||2020-09-02 11:51:21 (UTC+8)|
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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|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0107358028|
|Data Type: ||thesis|
|Appears in Collections:||[風險管理與保險學系 ] 學位論文|
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