每種預測模式皆有其數學背景，各模式之間並沒有絕對的好壞，而是取決於資料的類型，透過觀察資料的趨勢去選擇一個較合適的模型。預測股市相關數據需要龐大的歷史資料以及動態的預測模型，因此，本研究使用倒傳遞類神經網路(Backpropagation Neural Network)預測個股的五日帄均開盤價、最高價、最低價及收盤價，並推測每日的開盤、最高、最低及收盤價。利用模糊決策系統(Fuzzy Decision System)並考量成交量變數判斷其交易意願指標，進而計算出預定買價及預定獲利賣價，接著以月均線及季均線為判斷進場之時機，進行實際買賣操作。
在航空運輸產業股中選擇具代表性的長榮航及華航進行預測及操作，發現皆有不錯的獲利。推廣至運輸產業股的陽明及裕民，其趨勢皆是下跌的時段也不至於虧損。最後，再測詴操作非運輸產業股的中鋼也有不錯的表現。因此，本研究提出的方法可以決定個股的進出場價位及時機，進而獲得穩定的利潤並有效降低虧損之風險。

This study proposes a back-propagation neural network approach to estimate the 5-day MAs(moving averages) of Open, High, Low, and Close prices, and then Open, High, Low and Close prices of each day can be predicted. Considering the trading volume, a fuzzy control system which can simulate behavior of human decision making is used to determine the degree of trading intention. As a result, the bid and ask prices are able to be predicted according to predicted prices and trading intention. Moreover, the 20-day MA and 60-day MA are considered as restrictions to obtain an appropriate operating day, and a profitable stock is selected and operated to test the gain ability of the proposed predicting system. From the operated results, the proposed method can make stable gains continuously, and reduce the risk of loss efficiently.

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