隨著世界各國經濟的快速發展,經濟現象的預測已經是政經與企業擬定未來政策的重大考量因素,其中又以股價指數預測之相關研究較為重要,包含技術分析、基本分析、產業分析、政策分析和心理分析等等,都是要試圖找出可依循的市場規則,進而獲取更高的報酬。近年來案例式推理也逐漸成為資料探勘預測及分類時一個重要的工具,藉由結合前人的經驗及資料發展一案例庫,並藉此行為發展經驗法則推估其之後的動作,此種模式將能幫助我們更進一步了解未來,並且對此進行預測。故本研究嘗試結合案例式推理之分群權重法與歸納學習法決策樹工具(ID3)搭配模糊理論及演化法則作為主要研究工具–「案例似推理演化式模糊決策樹」。期望藉由此一學習性的工具,準確預測出股票之趨勢性,提供買家較為有利的資訊平台。本研究可細分為三個階段,第一階段是透過案例式推理將龐大的資料庫轉換為小型的資料庫;第二階段是針對各個小型的資料庫運用演化式模糊決策樹進行案例的測試;第三階段則是利用模擬退火法進行參數最佳化。經過實驗結果證實,透過此一系統之幫助,不論是在上漲、下跌甚至是持平型的股市案例中,均可準確預測股價走勢,準確率甚至較現有之預測系統進步10%以上。證明本系統具有較佳之優越性。
Stock price predictions suffer from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper establishes a novel financial time series-forecasting model by a case based fuzzy decision tree induction for stock price movement predictions in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The model is major based on the idea that the historic price data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately react to the current tendency of the stock price movement from these smaller case based fuzzy decision tree inductions. Hit rate is applied as a performance measure and the effectiveness of our proposed CBFDT model is demonstrated by experimentally compared with other approaches on various stocks from TSEC. The average hit rate of CBFDT model is 91% the highest among others.