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題名：回饋式類神經網路知識發掘應用於最適投資組合資金配置
書刊名：中山管理評論
作者：黃國棟／許中川／黃金生 引用關係
作者(外文)：Huang, Kuo-dong／Hsu, Chung-chian／Huang, Chin-sheng
出版日期：2002
卷期：10:4
頁次：頁651-682
主題關鍵詞：資料庫知識發掘；資料探勘；回饋式類神經網路；法則萊取；資金配置；Knowledge discovery in databases；Data mining；Recurrent neural network；Rule extraction；Capital allocation
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0)
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  • 共同引用共同引用:0
  • 點閱點閱:42
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.
期刊論文
1.Fayyad, U. M.、Piatetsky-Shapiro, G.、Smyth, Padhraic、The, K. D. D.(1996)。Process for Extracting Useful Knowledge from Volumes of Data。Communications of the ACM，39(11)，27-33。  new window
2.Brachman, R. J.、Khabaza, T.、Kloesgen, W.、Piatetsky-Shapiro, Gregory、Simoudis, E.(1996)。Mining Business Databases。Communications of the ACM，39(11)，42-48。  new window
3.官美蘭、苑守慈(2000)。透明化與個人化之股市預測分析。資訊管理學報，6(2)，211-239。  延伸查詢new window
4.Craven, Mark W.、Shavlik, Jude W.(1996)。Extracting tree-structured representations of trained networks。Advances in Neural Information Processing Systems，1，24-30。  new window
5.Fu, Li Min(1999)。Knowledge discovery based on neural networks。Communication of The ACM，42(11)，47-50。  new window
6.Omlin, Christian W.、Giles, C. Lee(1996)。Rule revision with recurrent neural networks。IEEE Transactions on Knowledge and Data Engineering，8(1)。  new window
7.Setiono, R.、Liu, Huan(1996)。Symbolic representation of neural networks。IEEE Computer，29(3)，71-77。  new window
8.Taha, Ismail A.、Ghosh, Joydeep(1999)。Symbolic interpretation of artificial neural networks。IEEE Transactions on Knowledge and Data Engineering，11(3)，448-463。  new window
9.Tkacz, Greg(2001)。Neural network forecasting of Canadian GDP growth。International Journal of Forecasting，17(1)，57-69。  new window
會議論文
1.Odom, M.、Sharda, R.(1990)。A neural networks model for bankruptcy prediction。International Joint Conference on Neural Networks。San Diego, CA。163-168。  new window
2.許中川、洪鋕鋒(1997)。資料庫知識發掘前置處理與欄位拆解。沒有紀錄。362-369。  延伸查詢new window
3.陳稼興、張應華(1999)。應用柔性計算技術於股票交易決策支援模型之建構。沒有紀錄。  延伸查詢new window
4.Gavrilov, M.、Anguelov, D.、Indyk, P.、Motwani, R.(2000)。Mining the stock market: Which measure is best?。沒有紀錄。487-496。  new window
5.Kimoto, T.、Asakawa, K.(1990)。Stock market prediction system with modular neural networks。San Diego, CA。1-6。  new window
6.Moody, J.、Wu, L.(1997)。Optimization of trading systems and portfolios。沒有紀錄。300-307。  new window
7.Wuthrich, B.、Cho, V.、Leung, S.、Permunetilleke, D.、Sankaran, K.、Zhang, J.(1998)。Daily stock market forecast from textual web data。San Diego, CA。2720-2725。  new window
圖書
1.謝劍平、蔡祖銘(1995)。投資學。投資學。沒有紀錄：三民書局。  延伸查詢new window
2.Elton, Edwin J.、Gruber, Martin J.(1995)。Modern Portfolio Theory and Investment Analysis。John Wiley and Sons, Inc.。  new window
3.Han, Jiawei、Kamber, Micheline(2000)。Data mining: Concepts and techniques。Morgan Kaufmann Publishers。  new window
4.Piatetsky-Shapiro, G.、Frawley, W. J.(1991)。Knowledge Discovery in Databases。Cambridge, MA：AAAI。  new window
5.Berry, Michael J. A.、Linoff, Gordon S.(1997)。Data Mining Techniques for Marketing, Sales and Customer Support。John Wiley & Sons, Inc.。  new window
6.Fayyad, Usama、Smyth, Padhraic、Piatetsky-Shapiro, G.(1996)。From Data Mining to Knowledge Discovery: An Overview。Advances in Knowledge Discovery and Data Mining。沒有紀錄。  new window
7.Goonatilake, S.、Treleaven, P.(1995)。Intelligent Systems for Finance and Business。Intelligent Systems for Finance and Business。沒有紀錄。  new window
8.Bigus, Joseph P.(1996)。Data mining with neural networks。New York：McGraw-Hill。  new window
9.Fischer, Donald E.、Jordan, Ronald J.(1995)。Security analysis and portfolio management。沒有紀錄：Prentice Hall。  new window
 
 
 
 
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