迴歸模式與類神經網路在台股指數期貨預測之研究

The Application of Regression Model and Artficial Neural Network for Studying the Taiwan Stock Index Future

李惠妍(Hui-Yen Li) ； 吳宗正(Chung-Cheng Wu) ； 溫敏杰(Miin-Jye Wen)

《經營管理論叢》 2卷1期 (2006/06) Pp. 83-99

https://doi.org/10.6872/OMR.200606_2(1).0005

摘要

臺灣證券金融市場於民國87年7月21日正式推出加權股價指數期貨契約，為台灣的金融業自由化及國際化，建立一新里程碑。除提供投資者投資商品和避險工具外，也提供投機客及套利者，以少量資金賺取較大利潤的機會，尤其近幾年來期貨交易熱度愈來愈沸騰，期貨市場的發展潛力愈來愈不可忽視。有鑑於類神經網路是近年來快速竄起的資訊處理技術，尤其是運用在金融財務方面，績效卓著。所以本研究嘗試運用類神經網路及統計方法中的迴歸分析，來預測台股指數期貨的隔日收盤指數，以尋求適宜的預測模式。本研究結果發現：在倒傳遞類神經網路方面，發現有隱藏層的模式較無隱藏層的模式預測績效稍佳，但差異不大。在迴歸分析方面，去除三筆偏離值後，再經由逐步迴歸分析篩選出當日收盤指數、基差、漲跌、10日威廉氏指標、5日乖離率等5種變數的模式最為適合。在改良式類神經網路方面，以逐步迴歸分析篩選後之5種變數，做為輸入變數，發現無隱藏層的模式似乎較有隱藏層的模式預測績效稍佳，但差異也是不大。在三種不同模式的預測績效比較發現，迴歸分析績效最好，其次是改良式倒傳遞類神經網路，而預測績效最差的是倒傳遞類神經網路。

關鍵字

臺灣加權股價指數期貨 ； 倒傳遞類神經網路 ； 迴歸分析

並列摘要

Taiwan stock market formally established Taiwan Stock Index Future Contract on July 21, 1998, it could be said a milestone for Taiwan finance career's liberalization and internationalization. It offers investors the new investment product and hedging risk tools, and also offers speculators and arbitragers the opportunity of making more profit at less fund. Especially, the higher futures transactions, the more potential of futures market in these few years. Artificial Neural Network is a tool of information technique that rapidly rises in these few years. Especially using in finance field, the performance is very outstanding. So this study tries to use Artificial Neural Network and Regression Analysis of statistical methods in order to predict the next day closing index of FITX, and then find the suitable prediction model to create the better rate of gaining profit. The results of the study are: At the aspect of the Back-Propagation network, it could be found that the model with hidden layer is not big difference than the model without hidden layer for the prediction performance. At the aspect of Regression Analysis, By ruling out three outliers and by stepwise regression analysis to select the five variables of the closing index, basis difference, up and down, 10 days W%R and 5 days BIAS into the model. At the aspect of improving Artificial Neural Network, the five variables selected by using stepwise regression analysis method are regard as input variables. It could be found that the model without hidden layer is better than the model with hidden layer for the prediction performance, but the difference is small. At the prediction performance of three models comparison aspect, It could be found the performance of improving model of Artificial Neural Network is the better choice, Regression Analysis is the best one, but Artificial Neural Network is the worst one. So it is not certainly better when variables are more but create too much complex, on the contrary, decrease some effects between some variables. Improving model of Artificial Neural Network is to simplify variables in order to decrease effects and get better prediction performance.

並列關鍵字

Taiwan stock index future ； Artificial neural network ； Regression analysis