智慧型多值企業價值評估模型: 以模糊遺傳程式規劃為基礎

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林萍珍(Ping-Chen Lin) 查詢紙本館藏

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論文名稱

智慧型多值企業價值評估模型: 以模糊遺傳程式規劃為基礎
(Multi-Valued Stock Valuation Based on FuzzyGP)

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摘要(中)

企業價值評估是評估企業的真實價值, 有效的價值評估機制可提昇企業經營績效進而創造投資人的財富。
一般評估模型如資產價值法、淨值折現法、市場乘數法、選擇權定價法, 因缺乏堅實的理論基礎,
至今仍存在一些爭議。此外, 財務資訊含有較多的雜訊與不確定性,
傳統數學與財務模型存在許多的假設前題與限制, 因此,
引進新的金融創新技術是不可或缺的, 人工智慧中的柔性計算技術於複雜求解問題的優越性正以改善此一缺點。
企業價值不易評估, 極可能是它並非是明確值, 而是一個模糊區間值,
而模糊理論的特性是在資訊充滿不確定性時, 可依其隸屬程度辨識模糊事物的輕重多寡;
另一原因可能是財務評估模型假設有一固定形式函數存在, 這些函數通常是變數的線性組合,
遺傳程式規劃依其非線性的最佳化能力, 能在廣大的求解空間搜尋出最適的組合解。
因此, 本研究的目的是提出『模糊遺傳程式規劃』, 作為建構企業價值評估模型新技術,
期望能有系統性與自動化建構新的評估模型, 以解決資訊非結構性與不確定性的問題。換言之,
新模型可以評估出企業真實價值的合理區間而非單點股價。
另外, 本研究以模糊可能分佈 (Fuzzy Possibility Distribution)為基礎解決投資與資產組合的交易策略。
而且此評估模型將是從大量的非結構性財務資料中發掘出一個收斂到最適的評估模型與最佳的資產配置交易策略。
為要驗證新理論模型的可行性與合理性, 本研究研究方法分成研究模型運作流程與研究設計兩部份描述。
模型運作流程部份分為過濾流程、抽樣流程以及模模糊遺傳程式規劃之評估流程。
過濾流程要從資料庫中處理缺值與空值; 抽樣流程是從母體中抽出符合實驗條件的變數與樣本。
模模糊遺傳程式規劃之評估流程是以遺傳程式規劃技術為基礎, 將染色體模糊化協助發掘企業真實價值。
此流程主要目標搜尋企業價值的評估規則, 藉遺傳程式規劃做選擇,
交配與突變機制不斷演化出新族群, 最後搜尋出最佳的規則組合, 此即為智慧型的評估模型,
做為評估企業真實價值的準則。本研究提出模糊樹混合交配法以改善個體不易收斂的問題。
遺傳程式規劃兩項重要工作是個別染色體節點編碼與評估函數設計,
模糊化的節點是評估的組成元素, 即輸入變數。評估函數可說是個體遺傳演化的環境,
若一演化世代愈能符合評估函數的要求, 便表示此個體愈能適應環境,
本研究之評估函數設計以報酬率最大與風險最小並重演化出更優良的子代。
本研究貢獻包括: 第一, 建構新的智慧型企業價值評估「理論模型」, 釐清部份既有模型的困境與盲點。
第二, 提出企業價值合理區間的觀念, 突破現有評估模型所解釋的企業價值均為單一股價的思維,
企業的真實價值或許是模糊多值 (Multi-Valued)而非單值 (Single-Valued),
合理解釋資本市場充滿不確定性的模糊現象。
第三, 提出模糊樹混合交配的方法, 以改善遺傳程式規劃不易收斂的問題;
交配的另一問題是交配舊基因, 模糊樹混合交配法交配後會產生新的基因。
第四, 梯形模糊數適合多值的企業價值評估, 並導引出自然的投資組合之資產配置交易策略,
協助證券分析師或投資人在做決策一套兼具擇股與擇時的智慧型證券決策支援系統。
第五, 提供金融機構一套有用的授信決策支援系統。本研究建構之智慧型企業價值評估模型,
將以資訊科技的透明化, 部份解決授信人員操守問題; 且模型將有效幫助授信者過濾雜訊,
能儘早發現危機訊號以謀對策, 因而避免誤判放款對象與額度, 而發生鉅額呆帳損失;
亦可應用於其他財務領域如企業購併、上市上櫃承銷價訂定、銀行團接管等課題。

摘要(英)

Stock valuation plays an important role in stock selection for fundamental investors. The Efficient Market Hypothesis (EMH) emphasizes that the intrinsic value of a stock will be reflected by its market price. Previous studies on stock valuation estimate a stock’’s value as a single-valued number. Different models generate different estimates on the same stock. This may imply that the value of a stock should be multi-valued rather than single-valued. This study develops an intelligent stock valuation model to produce a multi-valued price for a stock by generalizing genetic programming to Fuzzy genetic programming. Since the stock value is estimated by a Fuzzy expression tree which calculates to a trapezoidal Fuzzy number, the stock value becomes multi-valued. In addition, the resulting trapezoidal Fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated.

關鍵字(中)

★ 遺傳程式規劃
★ 模糊數
★ 多值
★ 企業價值評估

關鍵字(英)

★ Genetic Programming
★ Fuzzy Number
★ Multi-Value
★ Intrinsic Value
★ Stock Valuation

論文目次

1 緒論 1
1.1 研究背景 2
1.2 研究動機 3
1.3 研究目的 5
1.4 研究重要性 6
1.5 研究步驟 7
1.6 論文章節架構 8
2 企業價值評估理論 11
2.1 企業價值的意義 11
2.2 企業價值理論模型 12
2.3 企業價值評估模型理論 15
2.3.1 資產價值法 15
2.3.2 淨值折現法 16
2.3.3 價格乘數法 17
2.3.4 選擇權定價法 19
2.4 績效評估 19
2.5 企業價值相關研究 20
2.6 傳統評估方法的問題 23
3 模糊理論與遺傳程式規劃 25
3.1 模糊理論 25
3.1.1 模糊集合 25
3.1.2 模糊數 27
3.1.3 模糊決策 29
3.1.4 模糊理論於財務相關研究 31
3.2 遺傳程式規劃 33
3.2.1 遺傳演算法的基本概念 34
3.2.2 遺傳程式規劃的運作流程 36
3.2.3 遺傳程式規劃技術上面臨的困難 38
3.2.4 遺傳程式規劃於財務相關研究 40
3.3 模糊遺傳程式規劃相關研究 44
4 研究模型 45
4.1 理論模型 45
4.2 研究模型 47
4.2.1 過濾流程 49
4.2.2 抽樣流程 49
4.2.3 模糊遺傳程式規劃為基礎的評估流程 49
4.2.4 模糊遺傳程式規劃樹的表示 50
4.3 細步實作 50
4.3.1 編碼 51
4.3.2 模糊數的型態 53
4.3.3 模糊數的運算 53
4.3.4 模糊樹混合交配法 55
4.3.5 評估函數 60
4.3.6 交易策略 60
4.3.7 系統參數 64
5 實驗分析與討論 66
5.1 樣本資料選取與資料來源 66
5.2 變數的操作性定義 67
5.3 實驗環境與工具 70
5.4 實驗設計 70
5.4.1 實驗期間 70
5.4.2 實驗假設 71
5.4.3 實驗設計 72
5.5 實驗結果與分析 72
5.5.1 訓練期間長短 76
5.5.2 移動視窗 80
5.5.3 效能分析 84
5.5.4 投資報酬率評比 89
6 結論與建議 94
6.1 研究發現 95
6.2 研究貢獻 97
6.3 研究限制 98
6.4 未來研究方向 99
參考文獻 101

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指導教授

陳彥良、陳稼興
(Y. L. Chen、J. S. Chen)

審核日期

2004-1-15

推文