A Study of Knowledge Discovery from Recurrent Neural Network for Optimal Portfolio Capital Allocation
| Autor: | Kuo-Dong Huang, 黃國棟 |
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| Rok vydání: | 2001 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 89 The investors made decisions for investment on sophisticated investment environ-ment. Undoubtedly, the investors hope earn more returns to increase their wealth. In recent years, many researchers try to use data mining or other relates techniques trying to discovery patterns from huge financial database to support investors to make decision. However, most of these researchers just explain market performance with theories or methods. The important issues of portfolio capital allocation are addressed relatively few. Moreover, a lot of researches which used artificial neural network for stocks pre-diction focused on prediction for marketing index or stock price. But, past researches after didn’t present or translate the patterns explicitly, which are mined from databases. Therefore, this research mainly proposed a complete “financial database knowledge discovery model” to help investors make investment decision. First, we process finan-cial database, build optimal portfolio analysis model and use recurrent neural network to form optimal portfolio capital allocation strategy. Second, we use a rule extraction al-gorithm to mine unknown rules from the neural network. The intention to mine the unknown rules from the black box of neural network is that finding implicit information or relate knowledge from financial database. The discovered knowledge or informa-tion from database will become useful information, which can help investors to make decision and provide investors optimal investment decision supports. Finally, the fea-sibility of this method is evaluated by developing a prototype system and testing with real financial data. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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