投資人在複雜的投資環境中進行投資，無疑是希望能夠賺取更多的報酬來增加自己的財富：近年來，雖然有不少圍內外學者試圖從龐大的股市資料中，運用各種資料探勘的技術預測市場走勢，提供投資人做為投資決策參考的依據：但是，在眾多研究者以各種理論或方法解釋市場表現的研究中，少有研究討論到投資組合資金配置這個重要議題，而且在以類神經網路進行股市預測的研究議題中，大多只是針對未來的指數或個股的走勢進行預測而己，未能針對資料庫知識發掘流程的最後一個階段，知識的呈現、表達與轉譯等步驟進行更深一層的研究，致使從資料庫中所擷取的資訊非常有限：而本研究主要是提出一完整的「財務資料庫知識發掘模型」、針對財務資料庫中的歷史資料，進行前置處理、建立最適投資組合評估模型、回饋式類神經網路預測等步驟，提供投資人一個兼具風險與報酬考量的最適投資組合資金配置策略，之後再以法則萃取演算法，探勘類神經網路結構中的黑盒，使財務資料庫中所隱含的資訊與知識能夠外顯化，提供使用者較佳的投資決策支援環境。最後，本研究以雛型系統開發與實際的財金資料庫資料，驗證此架構的可行性。

The investors made decisions for investment on sophisticated investment environment. 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 prediction 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 financial database, build optimal portfolio analysis model and use recurrent neural network to form optimal portfolio capital allocation strategy. Second, we use a rule extraction algorithm 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 information from database will become useful information, which can help investors to make decision and provide investors optimal investment decision supports. Finally, the feasibility of this method is evaluated by developing a prototype system and testing with real financial data.