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論文中文名稱:蟻群最佳化系統在台灣股票市場投資決策之應用 [以論文名稱查詢館藏系統]
論文英文名稱:The Application of Ant Colony Optimization System on the Investment Strategies at Taiwan Stock Market [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:工商管理研究所
中文姓名:陳明琪
英文姓名:Ming-Chi Chen
研究生學號:94749002
學位類別:博士
語文別:中文
口試日期:2008-05-01
論文頁數:91
指導教授中文名:林逾先
指導教授英文名:Edward Yu-Hsien Lin
口試委員中文名:陳穆臻;陳家祥;林鳳儀;鄭雅穗
口試委員英文名:Mu-Chen Chen;Ja-Shen Chen;Fengyi Lin;Hilary Cheng
中文關鍵詞:蟻群最佳化KD指標成交量基因演算法模擬退火法
英文關鍵詞:Ant colony optimizationKD technical indicatorsStocks trading volumeGenetic algorithmsSimulated annealing
論文中文摘要:本研究運用一種改進的蟻群最佳化演算法,針對台灣股票市場建構一個理性的投資決策系統,同時與大盤系統、基因演算法及模擬退火法在股市投資決策上之投資績效作比較。我們並探討了系統參數變動對投資績效之影響。模型中以股價、20日移動平均線、KD線與成交量等技術分析指標為判斷因子,根據蟻群最佳化之路徑選擇機率進行投資。設定成交量為費洛蒙濃度訊息、KD值為能見度資訊。研究結果發現ACO系統之投資績效優於大盤系統、基因演算法與模擬退火法之投資績效,且ACO系統在研究期間之投資報酬率高達390.39%,年平均報酬率為78.08%,遠超過銀行定存之獲利。不論是在股市上漲或下跌期間,ACO系統之投資績效皆明顯優於大盤系統、基因演算法與模擬退火法之投資績效。
ACO系統的參數測定顯示費洛蒙殘留係數ρ值的改變,會影響ACO系統之投資績效,ρ值越高,ACO系統搜尋最佳投資組合之能力越好。且費洛蒙刺激係數α值與能見度刺激係數β值的變動,會影響ACO系統之投資績效,當α=1、β=2 時,ACO系統之投資績效最高;當α=1、β=1 時,ACO系統之投資績效最差。
論文英文摘要:This research develops a modified ACO (Ant Colony Optimization) algorithm towards the construction of a rational investment decision-making system for Taiwan stock market. The performance of this developed algorithm is compared with genetic algorithm and simulated annealing approach through the comparison of their generated investment return rates from the stock market. This study also investigates the effect of system parameter changes on the performance of investment return. Our model takes into account the technical indicators that include stock price, 20-day moving average, KD line and trading volume as the determining factors. Specifically, the trading volume is the phenomenon, the data of stochastic line KD is the visibility. The results of our research reveals that our modified ACO algorithm has better investment return performance than those from the actual stock market, the genetic algorithm, and the simulated annealing approach. The overall 390.39% investment return rate, or the 78.08% annual investment return rate, generated from this research is far better than the profit obtained from the return of fixed term deposit during the same period. The superiority of our modified ACO algorithm performs equally well for both the upward and downward trend in the stock market.
In addition, our research results indicate that the change of pheromone trail rate ρ will affect the optimal return of the portfolio under ACO system. Specifically higher ρ value leads to stronger capability of searching the optimal investment portfolio. The variation of pheromone trail weighting parameter α and visibility weighting parameter β will affect the portfolio performance. It is found that ACO algorithm yields the best investment performance when α=1 and β=2. On the other hand, it generates the least performance when α=1 and β=1.
論文目次:摘 要 i
ABSTRACT ii
誌 謝 iii
目 錄 iv
表目錄 v
圖目錄 vi
圖目錄 vi
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 3
第二章 文獻探討 5
2.1 蟻群最佳化 5
2.2 基因演算法 10
2.3 模擬退火法 15
2.4 股價分析方法 18
2.5 技術分析有效性 24
2.6 投資決策理論 32
第三章 研究方法 36
3.1 研究模型與設計 36
3.2 資料來源與說明 54
3.3 投資績效比較基準 56
3.4 不同參數值對ACO系統之影響 56
3.5 研究範圍與限制 57
第四章 系統模擬結果與討論 58
4.1 投資績效比較 58
4.2 ACO系統在股市急遽變化時之投資績效 66
4.3 ACO系統參數對投資績效之影響 72
第五章 結論及建議 75
5.1 結論 75
5.2 建議 76
參考文獻 78
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