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An Intelligent Market Cycle Detection System
The detection of stock market cycles has attracted the attention of finance scholars and market practitioners. Accurately identifying the direction of a market can significantly increase the returns of investors. Despite this importance, conventional methodologies in the literature have predominantl...
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| Format: | Thesis |
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AUC Knowledge Fountain
2021
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| _version_ | 1867613419186683904 |
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| access_status_str | Open Access |
| author | Azer, Michael |
| author_browse | Azer, Michael |
| author_facet | Azer, Michael |
| author_sort | Azer, Michael |
| collection | Thesis |
| description | The detection of stock market cycles has attracted the attention of finance scholars and market practitioners. Accurately identifying the direction of a market can significantly increase the returns of investors. Despite this importance, conventional methodologies in the literature have predominantly attempted to evaluate the effect of subsets of factors as precedents to stock market cycles and with little agreement on what constitutes critical factors. There seems to be a lack in the literature for a comprehensive study that examines a multitude of factors at the same time on the S&P500 as the laboratory. Factors are categorized into: political events, economic factors, market fundamental indicators and technical trading rules. The main objective of this research is to detect a 5-tier market cycle and conduct a comprehensive analysis of the above factors by employing different machine learning and deep learning classification techniques. Our findings were that the Extreme Gradient Boosting method was of the highest accuracy in identifying the cycles, which led to returns that beat the buy-and-hold strategy by 88% versus 37% between 01- 2018 and 12-2020. ARIMA, Fourier Cycles, and Remaining days to the next presidential elections together were found to be of the highest importance in detecting the 5-tiers. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2612 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:50.652Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2612 An Intelligent Market Cycle Detection System Azer, Michael The detection of stock market cycles has attracted the attention of finance scholars and market practitioners. Accurately identifying the direction of a market can significantly increase the returns of investors. Despite this importance, conventional methodologies in the literature have predominantly attempted to evaluate the effect of subsets of factors as precedents to stock market cycles and with little agreement on what constitutes critical factors. There seems to be a lack in the literature for a comprehensive study that examines a multitude of factors at the same time on the S&P500 as the laboratory. Factors are categorized into: political events, economic factors, market fundamental indicators and technical trading rules. The main objective of this research is to detect a 5-tier market cycle and conduct a comprehensive analysis of the above factors by employing different machine learning and deep learning classification techniques. Our findings were that the Extreme Gradient Boosting method was of the highest accuracy in identifying the cycles, which led to returns that beat the buy-and-hold strategy by 88% versus 37% between 01- 2018 and 12-2020. ARIMA, Fourier Cycles, and Remaining days to the next presidential elections together were found to be of the highest importance in detecting the 5-tiers. 2021-06-15T07:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1588 https://fount.aucegypt.edu/context/etds/article/2612/viewcontent/MichaelAzer_thesis_final.pdf Theses and Dissertations AUC Knowledge Fountain Stock Market Cycles Machine Learning Presidential Elections Economic Factors Market Fundamentals Fourier Analysis Finance and Financial Management |
| spellingShingle | Stock Market Cycles Machine Learning Presidential Elections Economic Factors Market Fundamentals Fourier Analysis Finance and Financial Management Azer, Michael An Intelligent Market Cycle Detection System |
| title | An Intelligent Market Cycle Detection System |
| title_full | An Intelligent Market Cycle Detection System |
| title_fullStr | An Intelligent Market Cycle Detection System |
| title_full_unstemmed | An Intelligent Market Cycle Detection System |
| title_short | An Intelligent Market Cycle Detection System |
| title_sort | intelligent market cycle detection system |
| topic | Stock Market Cycles Machine Learning Presidential Elections Economic Factors Market Fundamentals Fourier Analysis Finance and Financial Management |
| url | https://fount.aucegypt.edu/etds/1588 https://fount.aucegypt.edu/context/etds/article/2612/viewcontent/MichaelAzer_thesis_final.pdf |
| work_keys_str_mv | AT azermichael anintelligentmarketcycledetectionsystem AT azermichael intelligentmarketcycledetectionsystem |