@inproceedings{d047582d40b0463da21a8cb36f2c0c6a,
title = "A Bayesian-adaboost model for stock trading rule discovery",
abstract = "Detecting the trading patterns with different technical indicators from the historical financial data is an efficient way to forecast the trading decisions in the financial market. In most cases, the trading patterns which consist of some specific combinations of technical indicators are significant in predicting the efficient trading decisions. However, discovering those combinations is a rather challenge assignment. In this paper, we propose a novel method to detect the trading patterns and later the Naive bayes with Adaboost method was employed to determine the trading decisions. The proposed method has been implemented on two historical stock datasets, the experimental results demonstrate that the proposed algorithm outperforms the other three algorithms and could provide a worthwhile reference for the financial investments.",
keywords = "Adaboost, Naive Bayeians method, biclusters, stock prediction",
author = "Zhoufan Kong and Jie Yang and Qinghua Huang and Xuelong Li",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 ; Conference date: 14-10-2017 Through 16-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CISP-BMEI.2017.8302138",
language = "英语",
series = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Qingli Li and Lipo Wang and Mei Zhou and Li Sun and Song Qiu and Hongying Liu",
booktitle = "Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017",
}