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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sethi, M. Treleaven, P. |
| Copyright Year | 2015 |
| Description | Author affiliation: Centre for Financial Comput., Univ. Coll. London, London, UK (Sethi, M.; Treleaven, P.) |
| Abstract | Neural Network methods for stock prediction can be used to successfully signal when to buy individual stocks. The formation of weighted portfolios of such signaled stocks has however received little attention in the literature. Classical Mean-Variance based portfolio optimization techniques assume that stock returns fall into the Elliptical Family of Distributions and as such are not well suited to use with Neural Network based predictors. This paper introduces a new distribution independent framework for stock portfolio construction. Testing shows that the framework could be used to form profitable stocks portfolios when applied to a Neural Network stock predictor. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 1388510 |
| Page Count | 8 |
| File Format | |
| ISSN | 21614407 |
| e-ISBN | 9781479919604 |
| DOI | 10.1109/IJCNN.2015.7280361 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-07-12 |
| Publisher Place | Ireland |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Manganese Convergence Portfolios Computational Finance Neural Networks Neural Network Applications Information Mining and Forecasting |
| Content Type | Text |
| Resource Type | Article |
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