應用倒傳遞類神經網路於開盤行為研究分析以台灣加權指數為例

Abstract

由於資訊的迅速傳遞，台灣股市又容易受到突發事件的影響，有不少投資人的財產經過一夜股市的變動而大幅縮水，導致股市投資者追求低風險的投資機會，以降低不可掌握的風險則顯得更為迫切，而面對隔夜的風險通常使得投資人長期的獲利於一夕之間即回吐，因此長期性投資人則需要一套避險的模式來保全自己的財產。 本研究應用人工智慧領域中的倒傳遞類神經網路，對台灣加權指數之開盤後十五分鐘的歷史資料，以時間09:05、09:10、09:15收盤價之漲跌型態做分群，產生八個模型的群組，再將各群中的資料輸入至倒傳遞類神經網路，預測相對於當日內台灣加權指數收盤價的漲幅程度，並以台灣期貨指數為投資交易對象來實驗。實驗結果證實，實驗模型經由分群後結合倒傳遞類神經網路，來預測準確率顯著優於對照組只有使用倒傳遞類神經網路及隨機漫步的模型，而且模型中以M2(漲、漲、跌)、M7(跌、跌、漲)的投資交易準確度及獲利力為最佳之績效模型。因此，以上實驗藉由分群技術輔助更能掌握環境的變化去作動態學習，進而提供投資人更明確的買賣交易資訊，以輔助決策者做正確抉擇的依據。

Because the rapid change of information transmission, Taiwan stock market is also easy to influence by unexpected events. Many investors’ property changes in the stock market overnight sometimes shrink substantially. Therefore, it is urgent that the stock market investors seeking low risk investment opportunities to reduce the unpredictable risk. In order to prevent the dramatic overnight losses, the long-term investors need to build a hedging model to save their own property. In this study, we apply the theory of artificial intelligence in the field of back-propagation neural network to clustering the historical data of the behavior of opening patterns after 15 minutes in Taiwan weighted index price by the time 09:05, 09:10, 09:15 of the closing price. Produce eight types of groups, then each group of data entry to the back-propagation neural networks to predict relative to the same day's closing price of the Taiwan stock price index, and tests the investors in Taiwan's futures index as trading partners.The experimental result confirmed that after the experimental model through the combination of clustering propagation neural network to predict the exact rate was significantly better than the control group which only using back propagation neural network and the random walk model. In addition, the model with M2 (up, up, down), M7 (down, down, up) of the investment transaction accuracy and the profitability are the best profit performance model. Therefore, these experiments assisted by clustering has better grasp of the changes in the environment to make dynamic learning. Thus provide investors with more specific transactions information to assist decision-makers to make the right choice.