應用串級式關聯神經網路於台指期貨 時間序列之型態辨識

Abstract

一般應用類神經網路之研究皆受限於倒傳遞類神經網路(Backprop NN)之效能不彰，必須花費大量時間等候其訓練之結果，尤其在樣本量龐大、隱藏節點繁多、無法累積學習等情況下，勢必錯失實際操作時的進場時點，且無法掌握後續市場之狀況。是故本研究之目的在於利用串級式關聯類神經網路(Cascade correlation neural network, CCNN)之特性來避免倒傳遞類神經網路之缺點；並利用型態辨識之資料轉化方式，進行台指期貨走勢之學習、預測。

本研究嘗試以串級式關聯類神經網路學習台指期貨之走勢模式，並結合四種相對應之多空樣板進行原始資料轉換之後，藉由SNNS特有之Batchman語言定義並完成類神經網路之整體驗證流程。實證結果顯示在各模擬過程中，隨著持有天期增加，可更完整掌握反轉型態而創造獲利，尤其是持有5天期之報酬最為顯著，經過統計檢定後證明無論在獲利能力或預測準確率上，串級式關聯類神經網路之表現均優於倒傳遞類神經網路。此外停損機制之導入也明顯控管損失幅度，提昇兩預測模型之獲利水準。

Previous researchs claimed that to forecast the index movement by using the neural network, massive amount of waiting time was normally mandatory. The situation gets worse if larger training sets and more hidden units come into the iteration. Thisphenomenon explains why Backprop neural network takes time in learning process. In this paper, we propose a modified version of Backprop neural network, a cascade-correlation neural network, to simplify the configuration of experiment and shorten the running time for deriving the forcast result. Experiment shows that under the same working environment, cascade-correlation neural network outperforms Backprop neural network. Profits for cascade correlationneural network, especially, exhibit a steady increase through the tesing period, while profits for Backprop neural network flucturates, despite the fact that they both earn positive profits.