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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hurwitz, E. Marwala, T. |
| Copyright Year | 2011 |
| Description | Author affiliation: University of Johannesburg, Faculty of Engineering, Dean of Engineering, South Africa (Marwala, T.) || University of Johannesburg, Faculty of Engineering, Doctoral Candidate, South Africa (Hurwitz, E.) |
| Abstract | The potential of machine learning to automate and control nonlinear, complex systems is well established. These same techniques have always presented potential for use in the investment arena, specifically for the managing of equity portfolios. In this paper, the opportunity for such exploitation is investigated through analysis of potential simple trading strategies that can then be meshed together for the machine learning system to switch between. It is the eligibility of these strategies that is being investigated in this paper, rather than application. In order to accomplish this, the underlying assumptions of each trading system are explored, and data is created in order to evaluate the efficacy of these systems when trading on data with the underlying patterns that they expect. The strategies are tested against a buy-and-hold strategy to determine if the act of trading has actually produced any worthwhile results, or are simply facets of the underlying prices. These results are then used to produce targeted returns based upon either a desired return or a desired risk, as both are required within the portfolio-management industry. Results show a very viable opportunity for exploitation within the aforementioned industry, with the Strategies performing well within their narrow assumptions, and the intelligent system combining them to perform without assumptions. |
| Starting Page | 80 |
| Ending Page | 84 |
| File Size | 547568 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781457706523 |
| ISSN | 1062922X |
| e-ISBN | 9781457706530 |
| DOI | 10.1109/ICSMC.2011.6083646 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-10-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Noise Machine learning Equations Portfolios Mathematical model Learning systems Noise measurement agent Share Portfolio Risk Technical Analysis Temporal Difference Energy function Data generation |
| Content Type | Text |
| Resource Type | Article |
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