Optimal portfolio and trading strategy using machine learning
This research presents machine learning models for forecasting the future returns of a portfolio from NASDAQ semiconductors assets by financial analysis, optimization, and technical analysis to form a trading strategy. The performance of the portfolio is evaluated by back-testing. Data were collecte...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Online Access: | https://repository.londonmet.ac.uk/9844/1/2024338734.pdf |
| _version_ | 1824446504399536128 |
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| author | Ouazzane, Karim Tang, Kai Hung Po Yung Ghanem, Mohamed Chahine |
| author_facet | Ouazzane, Karim Tang, Kai Hung Po Yung Ghanem, Mohamed Chahine |
| author_sort | Ouazzane, Karim |
| collection | LMU |
| description | This research presents machine learning models for forecasting the future returns of a portfolio from NASDAQ semiconductors assets by financial analysis, optimization, and technical analysis to form a trading strategy. The performance of the portfolio is evaluated by back-testing. Data were collected from 2011 to 2019 for the sector of semiconductor companies listed on Nasdaq. The project consists of 4 sub-tasks. The first sub-task is to use the annual financial ratios of each company under the sector of semiconductors from 2011 to 2018 to project the company returns in 2019 using machine learning algorithms. Then, the top 5 highest-return assets would be selected to form a portfolio. After the optimization of the portfolio by Monte Carlo simulation, the classifiers adopt the technical indicators of the portfolio assets from 2011 to 2018 to predict the trading signals (buy or sell) in 2019. The trading actions in 2019 are simulated by back-testing. The result shows that the optimal portfolio using the simulat |
| first_indexed | 2025-02-19T01:16:12Z |
| format | Conference or Workshop Item |
| id | oai:repository.londonmet.ac.uk:9844 |
| institution | London Metropolitan University |
| language | English |
| last_indexed | 2025-02-19T01:16:12Z |
| publishDate | 2024 |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:98442025-02-10T10:53:00Z https://repository.londonmet.ac.uk/9844/ Optimal portfolio and trading strategy using machine learning Ouazzane, Karim Tang, Kai Hung Po Yung Ghanem, Mohamed Chahine 000 Computer science, information & general works 310 Collections of general statistics 330 Economics 380 Commerce, communications & transportation This research presents machine learning models for forecasting the future returns of a portfolio from NASDAQ semiconductors assets by financial analysis, optimization, and technical analysis to form a trading strategy. The performance of the portfolio is evaluated by back-testing. Data were collected from 2011 to 2019 for the sector of semiconductor companies listed on Nasdaq. The project consists of 4 sub-tasks. The first sub-task is to use the annual financial ratios of each company under the sector of semiconductors from 2011 to 2018 to project the company returns in 2019 using machine learning algorithms. Then, the top 5 highest-return assets would be selected to form a portfolio. After the optimization of the portfolio by Monte Carlo simulation, the classifiers adopt the technical indicators of the portfolio assets from 2011 to 2018 to predict the trading signals (buy or sell) in 2019. The trading actions in 2019 are simulated by back-testing. The result shows that the optimal portfolio using the simulat 2024-10-12 Conference or Workshop Item PeerReviewed text en cc_by_4 https://repository.londonmet.ac.uk/9844/1/2024338734.pdf Ouazzane, Karim, Tang, Kai Hung Po Yung and Ghanem, Mohamed Chahine (2024) Optimal portfolio and trading strategy using machine learning. In: Global IEEE Congress on Emerging Technologies (GCET-2024), 9-11 December 2024, Gran Canaria, Spain. (In Press) |
| spellingShingle | 000 Computer science, information & general works 310 Collections of general statistics 330 Economics 380 Commerce, communications & transportation Ouazzane, Karim Tang, Kai Hung Po Yung Ghanem, Mohamed Chahine Optimal portfolio and trading strategy using machine learning |
| title | Optimal portfolio and trading strategy using machine learning |
| title_full | Optimal portfolio and trading strategy using machine learning |
| title_fullStr | Optimal portfolio and trading strategy using machine learning |
| title_full_unstemmed | Optimal portfolio and trading strategy using machine learning |
| title_short | Optimal portfolio and trading strategy using machine learning |
| title_sort | optimal portfolio and trading strategy using machine learning |
| topic | 000 Computer science, information & general works 310 Collections of general statistics 330 Economics 380 Commerce, communications & transportation |
| url | https://repository.londonmet.ac.uk/9844/1/2024338734.pdf |
| work_keys_str_mv | AT ouazzanekarim optimalportfolioandtradingstrategyusingmachinelearning AT tangkaihungpoyung optimalportfolioandtradingstrategyusingmachinelearning AT ghanemmohamedchahine optimalportfolioandtradingstrategyusingmachinelearning |