使用決策樹演算法結合個股技術及籌碼指標建立股價漲跌預測模型

A Stock Investment Model Using Decision Tree with Technical and Counter Indexes

劉敬之(Ching-Chih Liu)

指導教授 ： 李維平

中原大學/商學院/資訊管理研究所/碩士(2011年)

https://doi.org/10.6840/cycu201100039

摘要

股票市場的走勢一直是許多民眾所關注的話題，而對於股票市場上股價因素的漲跌，有許多投資人希望對於未來股票價格進行準確預測，但股價受到很多因素；包含人為因素、政治因素、總體經濟環境因素、消息面或是其他未知因素的影響，使得許多相關研究的模型準確率無法令人滿意。

Data mining技術應用於預測的延革已經很久，而過去研究使用的方法包含基因演算法(Genetic Algorithms)、類神經網路(Artificial Neural Network，ANN)、決策樹演算法(Decision Tree Algorithms)、支援向量機(Support Vector Machine)等等均有學者應用於建立股價預測模型，近年來決策樹演算法成為在股價預測的議題中受歡迎的一種，研究顯示也有不錯的效果。

本研究應用決策樹演算法於台灣集中及櫃檯市場之類股股價的預測，研究目標為台灣50類股，研究期間為西元2002年5月2日起至2010年3月31日止。

實驗結果發現，以決策樹演算法結合個股技術指標及籌碼指標變數建立的股價漲跌預測模型之投資報酬率，可與在同期間之投信基金報酬率之排名獲得不錯的成效，同時領先相同期間之大盤指數漲幅。

關鍵字

籌碼指標 ； 股價預測 ； 資料探勘 ； 決策樹 ； 技術指標

並列摘要

The trend of stock market is a subject to be concerned by most people. Many investors expect to predict the stock price, which is affected by lot of factors, such as human interference, political issue, macro-economic or other unknown factors. Therefore the accuracy of some related models cannot be satisfied by people who concern.

Some methods applied to establish the stock price forecasting model by scholars, including Genetic Algorithms(GA), Artificial Neural Network(ANN), decision tree algorithms, SVM(Support Vector Machine) and so on. However, data mining technology has been used to forecast for a long time. Recently, decision tree algorithms become a popular one in the study of the stock price prediction and the study has shown good results.

This research applies decision tree algorithm to forecast the stock price of listed and OTC market in Taiwan. The target of research is to study TSEC Taiwan 50 sectors during the period from 2nd May 2002 to 31st March 2010. Experimental results show that the ROI (return on investment) of the stock price index prediction model established by combining the decision tree algorithm with the technical and counter indexes variables of stock can get a good result comparing to the ranking of the return on ROI of Trust Fund in the same period; and also ahead to the increase of market index during the same time period.

並列關鍵字

Data Mining ； Decition Tree ； Counter Indexes ； Stock Price Predict ； Techenical Indexes

參考文獻

1. 白炳豐、林國平、王正賢，橢圓形模糊系統於台灣股市股價預測之應用，Journal of the Chinese Institute of Industrial Engineers, Vol. 21, No. 2, pp. 146-155，2004。

5. 陳俊達、王台平、劉昭麟，以文件分類技術預測股價趨勢，第十九屆自然語言與語音處理研討會論文集 (ROCLING XIX)，P.347‒361，民國96年。

1. Kimoto, T. & Asakawa, K.(1990), Stock market prediction system with modular neural networks, IJCNN, Vol.1, P.1-6

2. Brownstone, D. (1995), Using percentage accuracy to measure neural network predictions in Stock Market movement, Neurocomputing 10 , P.237-250

3. Myoung-Jong Kim , Sung-Hwan Min and Ingoo Han(2006), An evolutionary approach to the combination of multiple classifiers to predict a stock price index, Expert Systems with Applications 31, P.241–247