政大學術集成


Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/68727


Title: Trading Strategies Based on K-Means Clustering and Regression Model
Authors: 陳樹衡
Chen,Shu-Heng
Contributors: 經濟系
Date: 2007
Issue Date: 2014-08-14 12:05:45 (UTC+8)
Abstract: This paper outlines a data mining approach to the analysis and prediction of the trend of stock prices. The approach consists of three steps, namely, partitioning, analysis and prediction. A 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. Using our trend prediction methodology, we propose a trading strategy TTP (Trading based on Trend Prediction). Some results of applying TTP to stock trading are reported. The trading performance is compared with some practical trading strategies and other machine learning methods. Given the volatility nature of stock prices the methodology achieved limited success for a few countries and time periods. Further analysis of the results may lead to further improvement in the methodology. Although the proposed approach is designed for stock trading, it can be applied to the trend analysis of any time series, such as the time series of economic indicators.
Relation: Computational Intelligence in Economics and Finance 2007, pp 123-134
Data Type: book/chapter
Appears in Collections:[Department of Economics] Books & Chapters in Books

Files in This Item:

There are no files associated with this item.



All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing