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Avoiding data mining bias when testing technical analysis strategies - a methodological study
When seeking to identify a profitable technical analysis (TA) strategy, a na¨ıve investigation will compare a large number of possible strategies using the same set of historical market data. This process can give rise to a significant data mining bias, which can cause spurious results. There are va...
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| Format: | Thesis |
| Language: | English |
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Centre for Actuarial Research (CARE)
2021
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| _version_ | 1867613292463128576 |
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| access_status_str | Open Access |
| author | Douglas, Rowan |
| author2 | Gilbert, Evan |
| author_browse | Douglas, Rowan Gilbert, Evan |
| author_facet | Gilbert, Evan Douglas, Rowan |
| author_sort | Douglas, Rowan |
| collection | Thesis |
| description | When seeking to identify a profitable technical analysis (TA) strategy, a na¨ıve investigation will compare a large number of possible strategies using the same set of historical market data. This process can give rise to a significant data mining bias, which can cause spurious results. There are various methods which account for this bias, with each one providing a different set of advantages and disadvantages. This dissertation compares three of these methods, the step wise Superior Predictive Ability (step-SPA) method of P.-H. Hsu, Y.-C. Hsu and Kuan (2010), the False Discovery Rate (FDR) method of Benjamini and Hochberg (1995) and the Monte Carlo Permutations (MCP) method of Masters (2006). The MCP method is also extended, using a step wise algorithm, to allow it to identify multiple profitable strategies. The results of the comparison show that while both the FDR and extended MCP methods can be useful under certain circumstances, the stepSPA method is ultimately the most robust, making it the best choice in spite of its significant computational requirements and stricter set of assumptions. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/32620 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:49.949Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Centre for Actuarial Research (CARE) |
| publisherStr | Centre for Actuarial Research (CARE) |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/32620 Avoiding data mining bias when testing technical analysis strategies - a methodological study Douglas, Rowan Gilbert, Evan Maritz, Erich Actuarial Science When seeking to identify a profitable technical analysis (TA) strategy, a na¨ıve investigation will compare a large number of possible strategies using the same set of historical market data. This process can give rise to a significant data mining bias, which can cause spurious results. There are various methods which account for this bias, with each one providing a different set of advantages and disadvantages. This dissertation compares three of these methods, the step wise Superior Predictive Ability (step-SPA) method of P.-H. Hsu, Y.-C. Hsu and Kuan (2010), the False Discovery Rate (FDR) method of Benjamini and Hochberg (1995) and the Monte Carlo Permutations (MCP) method of Masters (2006). The MCP method is also extended, using a step wise algorithm, to allow it to identify multiple profitable strategies. The results of the comparison show that while both the FDR and extended MCP methods can be useful under certain circumstances, the stepSPA method is ultimately the most robust, making it the best choice in spite of its significant computational requirements and stricter set of assumptions. 2021-01-21T10:59:41Z 2021-01-21T10:59:41Z 2020 2021-01-21T08:40:27Z Master Thesis Masters MCom http://hdl.handle.net/11427/32620 eng application/pdf Centre for Actuarial Research (CARE) Faculty of Commerce |
| spellingShingle | Actuarial Science Douglas, Rowan Avoiding data mining bias when testing technical analysis strategies - a methodological study |
| thesis_degree_str | Master's |
| title | Avoiding data mining bias when testing technical analysis strategies - a methodological study |
| title_full | Avoiding data mining bias when testing technical analysis strategies - a methodological study |
| title_fullStr | Avoiding data mining bias when testing technical analysis strategies - a methodological study |
| title_full_unstemmed | Avoiding data mining bias when testing technical analysis strategies - a methodological study |
| title_short | Avoiding data mining bias when testing technical analysis strategies - a methodological study |
| title_sort | avoiding data mining bias when testing technical analysis strategies a methodological study |
| topic | Actuarial Science |
| url | http://hdl.handle.net/11427/32620 |
| work_keys_str_mv | AT douglasrowan avoidingdataminingbiaswhentestingtechnicalanalysisstrategiesamethodologicalstudy |