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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 = A HYB...

國立高雄大學資訊工程學系碩士班

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 = A HYBRID STOCK SELECTION MODEL USING GENETIC ALGORITHMS, FUZZY THEORY AND SUPPORT VECTOR REGRESSION

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	A HYBRID STOCK SELECTION MODEL USING GENETIC ALGORITHMS, FUZZY THEORY AND SUPPORT VECTOR REGRESSION
作者:	鄭敦維,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	民100
面頁冊數:	48葉圖，表格 : 30公分;
標題:	選股問題
標題:	stock selection
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/32834320712066345782
附註:	參考書目：葉37-41
摘要註:	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.

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 = A HYBRID STOCK SELECTION MODEL USING GENETIC ALGORITHMS, FUZZY THEORY AND SUPPORT VECTOR REGRESSION
鄭, 敦維

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 = A HYBRID STOCK SELECTION MODEL USING GENETIC ALGORITHMS, FUZZY THEORY AND SUPPORT VECTOR REGRESSION / 鄭敦維撰 - [高雄市] : 撰者, 民100. - 48葉 ; 圖，表格 ; 30公分.
參考書目：葉37-41.
選股問題stock selection

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 = A HYBRID STOCK SELECTION MODEL USING GENETIC ALGORITHMS, FUZZY THEORY AND SUPPORT VECTOR REGRESSION
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328 $a 碩士論文--國立高雄大學資訊工程學系碩士班
330 $a 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.
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