作為時間序列的股票價格是非線性且非雜亂由於股票市場受到各種因素的影響。預測股票價格或指數與嘈雜的資料直接通常是有大的誤差。也因此，本論文是採用是倒傳遞小波類神經(Back Propagation Wavelet Neural Networks，簡稱BWNN)來當作預測方法，因為股價是處於一種非線性的方式呈現，以至於以灰預測進行股價預測會有極大的誤差，所以本論文把灰預測與小波轉換當作萃取特徵的種方法，倒傳遞類神經網路具備前饋和迴授兩種的網路特性，使得倒傳遞類神經網路的輸出包含以前所輸出的資料，因此倒傳遞類神經網路具有動態行為以及資料貯存的特性。本文提出六種預測策略分別為IWNN、SWNN、GWNN(S) 、GWNN(H) 、GWNN(L) 、GWNN(E)。利用以上不同的策略進行預測，來使投資行為能夠獲得較佳的利潤，且能夠降低投資者風險承擔。

As the time series of stock prices is non-linear and non-messy since the stock market is affected by a variety of factors influence. Prediction of stock price or index and noisy data directly is usually has a large margin of error. Also therefore, this papers is used BWNN (Back Propagation Wavelet Neural Networks) to as forecast method, because shares is is a nonlinear of way rendering, that to grey forecast for shares forecast will has great of errors, so this papers put grey forecast and Wavelet conversion as extraction features of species method, pour passed class neural network with Feed-forward and Feed-back award two species of network characteristics, Pass class information output by the neural network output contains a previously, so passing class characteristics of neural network with dynamic behavior and data storage. Presented six forecast policy namely, IWNN, SWNN, GWNN (s), GWNN (h), GWNN (l), GWNN (e). Use of these different strategies for forecast, to enable investment behavior to better profits, and to reduce the risk investors assume.

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