A Heuristic Stock Portfolio Optimization Approach Based on Data Mining Techniques
| atmire.migration.oldid | 764 | |
| dc.contributor.advisor | Alhajj, Reda | |
| dc.contributor.author | Koochakzadeh, Negar | |
| dc.date.accessioned | 2013-03-11T16:39:34Z | |
| dc.date.available | 2013-06-15T07:01:49Z | |
| dc.date.issued | 2013-03-11 | |
| dc.date.submitted | 2013 | en |
| dc.description.abstract | Portfolio optimization is the process of making investment decisions on holding a set of financial assets to meet various criteria. A variety of investment assets around the world make this multi-faceted decision problem very complicated. Econometric and statistical models as well as machine learning and data mining techniques have been used by many researchers and analysts to propose heuristic solutions for portfolio optimization. However, a literature review shows that the existing models are still not practical as they do not always perform better than even the naïve strategy of investing in all available assets in the market. The methodology proposed in this thesis is an alternative heuristic solution to help investors make stock investment decisions through a semi-automated process. The proposed solution is based on the fact that the investment decision cannot be fully automated because investors’ preferences that are the key factors in making investment decision, vary among different people. For this purpose, a semi-automated framework called SMPOpt (Stock Market Portfolio Optimizer) has been designed and implemented. In the proposed framework, the goal is to learn from the historical fundamental analysis of companies to discover the optimum portfolio by considering investors’ preferences. The Portfolio optimization problem is formulated and broken down into steps to be able to apply data mining techniques such as Clustering and Ranking, and Social Network Analysis. Some of these techniques are customized based on the temporal behaviour of financial datasets. For instance, the ranking algorithm based on Support Vector Machine (SVMRank) is modified and a new algorithm called Time- Series SVMRank is proposed. A comprehensive experimental study has been conducted using the real stock exchange market datasets from the past recent decades to evaluate the proposed portfolio optimization solution. The obtained results confirmed the strength of the proposed methodology. | en_US |
| dc.identifier.citation | Koochakzadeh, N. (2013). A Heuristic Stock Portfolio Optimization Approach Based on Data Mining Techniques (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24756 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/24756 | |
| dc.identifier.uri | http://hdl.handle.net/11023/569 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Computer Science | |
| dc.subject.classification | Data Mining | en_US |
| dc.subject.classification | Stock Market Portfolio Optimization | en_US |
| dc.subject.classification | Social Network Analysis | en_US |
| dc.title | A Heuristic Stock Portfolio Optimization Approach Based on Data Mining Techniques | |
| dc.type | doctoral thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| ucalgary.item.requestcopy | true |