Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/74888


Title: Stock Trend Analysis and Trading Strategy
Authors: Chen, Shu-heng;He, Hongxing;Chen, Jie;Jin, Huidong
陳樹衡
Contributors: 經濟系
Keywords: Data Mining;Clustering;k-means;Time Series;Stock Trading
Date: 2006
Issue Date: 2015-04-28 14:28:58 (UTC+8)
Abstract: This paper outlines a data mining approach to analysis and prediction of the trend of stock prices. The approach consists of three steps, namely parti- tioning, analysis and prediction. A modification of the commonly used k-means clustering algorithm is used to partition stock price time series data. After data partition, linear regression is used to analyse the trend within each cluster. The results of the linear regression are then used for trend prediction for windowed time series data. The approach is efficient and effective at predicting forward trends of stock prices. Using our trend prediction methodology, we propose a trading strategy TTP (Trading based on Trend Prediction). Some preliminary results of applying TTP to stock trading are reported.
Relation: Joint Conference on Information Sciences, Advances in Intelligent Systems Research
Data Type: article
ISBN: 10.2991/jcis.2006.135
DOI 連結: http://dx.doi.org/10.2991/jcis.2006.135
Appears in Collections:[經濟學系] 期刊論文

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