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研究生:鄭敦維
研究生(外文):Dun-Wei Cheng
論文名稱:一個基植於遺傳演算法與模糊理論最佳化之支援向量機選股模型
論文名稱(外文):A HYBRID STOCK SELECTION MODEL USING GENETIC ALGORITHMS, FUZZY THEORY AND SUPPORT VECTOR REGRESSION
指導教授:黃健峯
指導教授(外文):Chien-Feng Huang
學位類別:碩士
校院名稱:國立高雄大學
系所名稱:資訊工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:48
中文關鍵詞:選股問題模糊理論支援向量機遺傳演算法
外文關鍵詞:stock selectionfuzzy theorysupport vector machinesgenetic algorithms
相關次數:
  • 被引用被引用:9
  • 點閱點閱:869
  • 評分評分:
  • 下載下載:266
  • 收藏至我的研究室書目清單書目收藏:0
本論文提出一個植基於模糊理論之支援向量機選股模型並經由遺傳演算法實現模型之最佳化。選股問題在財務研究與投資領域中向來是極具挑戰性的關鍵問題,在人工智慧及軟式計算的輔助下這類問題得到有效的解決。因此,本論文運用模糊理論與支援向量機對股市進行分析,篩選具有成長潛力的公司做為投資標的。並且以遺傳演算法同時對選股模型作特徵選取與參數最佳化,建構可精確分析的選股模型。藉由統計測試證實本選股策略在獲利表現上遠超越大盤所能帶來的資產成長,實驗結果顯示在實際投資環境下選股模型可提供具備穩健性與可行性的投資策略。
In this thesis I will present a study of a hybrid AI-based methodology for stock selection, which has long been a challenging task in investment and finance. Recent advances in artificial intelligence and soft computing have led to significant opportunities to solve these problems more effectively. Therefore, in this study, the fuzzy theory and support vector machines are employed to rank a set of stocks; and top-ranked stocks are then selected to construct a portfolio. In addition, genetic algorithms were used to optimize the model parameters and perform feature selection simultaneously. Based on several statistical tests, I will show that the portfolios constructed using the proposed method shall outperform the benchmark significantly. The results thus show that the proposed investment approach is effective and robust for stock selection in practice.
中文摘要 ……………………………………………………………… I
英文摘要 ……………………………………………………………… II
誌謝 …………………………………………………………………… III
目錄 …………………………………………………………………… IV
表目錄 ………………………………………………………………… V
圖目錄 ………………………………………………………………… VI
第一章 : 緒論
1. 研究背景 …………………………………………………… 1
2. 研究目的 …………………………………………………… 2
第二章 : 文獻探討 ………………………………………………… 3
第三章 : 研究方法
1. 支援向量機(Support Vector Machine, SVM) …………… 7
2. 模糊理論(Fuzzy Theory) ………………………………… 11
3. 遺傳演算法(Genetic Algorithm, GA) …………………… 14
第四章 : 實驗方法
1. 實驗對象 …………………………………………………… 17
2. 實驗架構 …………………………………………………… 19
3. 模型最佳化 ………………………………………………… 21
第五章 : 實驗結果 ………………………………………………… 24
第六章 : 結論 ……………………………………………………… 36
參考文獻 ………………………………………………………………… 37
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