基於 EEMD 之類神經網路預測方法進行台指選擇權交易策略

• Main Authors: 李恩慈, Li, En Tzu
• Language: 英文
• Published: 國立政治大學
• Subjects: EEMD; ANN; 交易策略; FK 指標; Forecasting; FK Indicator
• Online Access: http://thesis.lib.nccu.edu.tw/cgi-bin/cdrfb3/gsweb.cgi?o=dstdcdr&i=sid=%22G0098755003%22.

金融市場瞬息萬變，幾乎所有商品價格都是非線性的動態過程，如何預測價格一直都是倍受討論和研究的議題。隨著電腦科技的不斷進步,許多財務學者以市場上的歷史交易資料作為研究對象，希望能夠預測出有效的結果。本研究利用 EEMD 法拆解原始加權指數訊號，建立類神經網路模型，並預測出未來市場之價格後，利用 FK 值當作交易門檻，帶回台指選擇權做交易測試並計算報酬。由於不同神經元個數會配適出不同的預測結果，本研究希望能夠找到較適合使用在指數預測的網路架構。 === The financial market forecasting is characterized by data intensity, noise, non-stationary, high degree of uncertainty, and hidden relationships. Investors are concerned about the forecasting market price. Throughout the development of computational technology, researchers have been involved in data mining on historical trading enabling them to have a more accurate data. This research uses Ensemble Empirical Mode Decomposition-based Artificial Neural Networks (ANNs) learning paradigm to provide different ways to analyze the stock market. In our research, we used the ANN method to obtain our prediction of the stock price. First, the previous day’s stock price needs to be decomposed in order to see the various variables, that is, the numerous IMFs seen on the graphs. Acquiring the information, it is inserted into the ANN method to get a prediction. Following that, the prediction can then be transformed into a simpler result via the Forward Calculator % K indicator. As a result, the FK value can display a signal if to buy or sell, and confirm trading time, and make buy or sell Call-Put decisions on TAIEX options. In summary,we found different neuron numbers in the hidden layers that may affect the result of prediction.