An online portfolio strategy based on trend promote price tracing ensemble learning algorithm
How to carry out an investment portfolio efficiently and reasonably has become a hot issue. This study mainly addresses the problem of the instability of forecasting stock price investment and the difficulty in determining investment proportion by proposing the trend peak price tracing (TPPT). First...
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| Vydáno v: | Knowledge-based systems Ročník 239; s. 107957 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
05.03.2022
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | How to carry out an investment portfolio efficiently and reasonably has become a hot issue. This study mainly addresses the problem of the instability of forecasting stock price investment and the difficulty in determining investment proportion by proposing the trend peak price tracing (TPPT). First of all, because of the influence of stock price anomaly, TPPT strategy sets adjustable historical window width. It uses slope value to judge prediction direction to track price change, which uses exponential moving average and peak equal weight slope value three-state price prediction method. Secondly, the accumulated wealth target is refined, and the fast error Back Propagation based on gradient projection algorithm (BP) is added. The algorithm solves investment proportion and feedbacks the increasing ability of assets to the investment proportion in order to maximize the accumulated wealth. Finally, comparison of eight empirical strategies in five typical data and statistical tests show that TPPT strategy has great advantages in balancing risk and return, and it is a robust and effective online portfolio strategy. |
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| AbstractList | How to carry out an investment portfolio efficiently and reasonably has become a hot issue. This study mainly addresses the problem of the instability of forecasting stock price investment and the difficulty in determining investment proportion by proposing the trend peak price tracing (TPPT). First of all, because of the influence of stock price anomaly, TPPT strategy sets adjustable historical window width. It uses slope value to judge prediction direction to track price change, which uses exponential moving average and peak equal weight slope value three-state price prediction method. Secondly, the accumulated wealth target is refined, and the fast error Back Propagation based on gradient projection algorithm (BP) is added. The algorithm solves investment proportion and feedbacks the increasing ability of assets to the investment proportion in order to maximize the accumulated wealth. Finally, comparison of eight empirical strategies in five typical data and statistical tests show that TPPT strategy has great advantages in balancing risk and return, and it is a robust and effective online portfolio strategy. |
| ArticleNumber | 107957 |
| Author | Liang, Chu-Xin Dai, Hong-Ming Adnan, Rana Muhammad Huang, Cui-Yin Dai, Hong-Liang |
| Author_xml | – sequence: 1 givenname: Hong-Liang surname: Dai fullname: Dai, Hong-Liang email: hldai618@gzhu.edu.cn organization: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China – sequence: 2 givenname: Chu-Xin surname: Liang fullname: Liang, Chu-Xin organization: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China – sequence: 3 givenname: Hong-Ming surname: Dai fullname: Dai, Hong-Ming organization: School of Information and Automation, Guangdong Polytechnic of Science and Trade, Guangzhou 510430, China – sequence: 4 givenname: Cui-Yin surname: Huang fullname: Huang, Cui-Yin organization: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China – sequence: 5 givenname: Rana Muhammad surname: Adnan fullname: Adnan, Rana Muhammad organization: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China |
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| Keywords | Ensemble learning algorithm Online portfolio investment Three-state price Gradient projection Price anomaly Investment ratio |
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| SubjectTerms | Algorithms Averages Ensemble learning Ensemble learning algorithm Forecasting Gradient projection Investment ratio Investments Machine learning Online portfolio investment Portfolios Price anomaly Prices Statistical tests Stock prices Strategies Strategy Three-state price Tracing Value Wealth Width |
| Title | An online portfolio strategy based on trend promote price tracing ensemble learning algorithm |
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