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研究生:陳薏如
研究生(外文):Yi-Lu Chen
論文名稱:以遺傳演算法與展望理論為基礎探討雜訊交易行為與股票評價模式之影響
論文名稱(外文):Stock Evaluation Model with Noise Trader Behavior based on Genetic Algorithm and Prospect Theory Optimization
指導教授:柯博昌柯博昌引用關係林萍珍林萍珍引用關係
指導教授(外文):Po-Chang KoPing-Chen Lin
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:資訊管理研究所碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:54
中文關鍵詞:展望理論遺傳演算法雜訊交易
外文關鍵詞:Prospect TheoryGenetic AlgorithmNoise Trade
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傳統財務學假設市場為效率市場,但現實市場中存在的異常現象,像是超額波動性(Excess Volatility)等現象並無法被合理解釋,因此學者轉而從心理學的角度去探討投資行為。展望理論說明人們實際在面對相同金額的獲利和損失時,損失的痛苦遠大於相同金額獲利所獲得的滿足,由於人們在面對損失時,恐慌與害怕的想法往往令人失去理智,因此市場上充斥著雜訊交易者(Noise trader)。雜訊交易者的錯估(Misperceptions)造成套利無法發揮預期的力量,雜訊因素是影響股價的重要原因之一。De Long et al.學者由理論的觀點提出DSSW模型,並假設錯估呈現常態分配,但根據展望理論再對應到股市中,投資者情緒處於悲觀或樂觀時所產生錯估的變異程度應該不同。因此本研究加入展望理論的概念,提出PT-DSSW模型,並結合遺傳演算法分成GA-DSSW及GA-PT-DSSW兩個系統,代入實際股價資料做驗證。結果顯示因雜訊交易者投資策略較短暫,因此採用訓練期間較短的雜訊交易模型具備較佳的預測能力,而PT-DSSW模型,將錯估定義為左右變異程度不相同的常態分配,在對於不同走勢的市場特性來說,本研究所提出的模型較具有解釋的能力。
Traditional financial theory assumes the market is efficient, but there are many phenomenons can’t be explained with “Efficient Market Hypothesis” reasonably, such as “Excess Volatility”, so many scholars attempt to discuss the investing behavior with psychology. “Prospect Theory” explains that when individuals face the same amount of money of gaining or losing, the distressed feel of losing will not be able to cover the satisfied feel of gaining. When individuals face the losing, they will be influenced by negative thinking, so there are many “noise trader” filling in the market. Noise trader’s “Misperceptions” causes the inefficiency of arbitrage, and the noise element is one of the important factors affecting the stock price. De Long et al.’s DSSW model assuming the misestimate appears normal distribution, but according to prospect theory, the emotions of investors which are optimistic or pessimistic should cause different levels of misestimates. In this article, we propose PT-DSSW model which combines the concept of prospect theory and genetic algorithm divides into two systems which are GA-DSSW and GA-PT-DSSW. From our experiments, because the periods of the noise trader’s investing strategies are short, the noise trade model of the shorter training period could be better forecasting ability, and PT-DSSW model defining misestimates as a normal distribution which of different variable levels between right and left tail has powerful explaining ability in different kinds of markets.
摘 要 iii
Abstract iv
誌 謝 vi
圖目錄 viii
表目錄 x
第一章 緒論 1
第二章 文獻回顧 4
2.1遺傳演算法(Genetic Algorithm, GA) 4
2.2展望理論(Prospect Theory) 8
2.2雜訊交易行為 10
第三章 研究設計 15
3.1研究架構 15
3.2遺傳演算法設計 16
3.2.1染色體編碼 17
3.2.2適應函數 17
3.3 PT-DSSW模型 18
第四章 實驗設計 24
4.1實驗一:針對不同走勢的市場特性做探討 25
4.2實驗二:觀測較適的訓練期間長短 33
4.3實驗三:比較GA-DSSW與GA-PT-DSSW整體的預測能力 36
第五章 系統介面 39
第六章 結論與建議 40
附錄一 PT-DSSW模型的細部公式推導 42
附錄二 關於實驗三於各期移動視窗的細部資料 46
參考文獻 52
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