Combination of Ensemble Algorithms with technical indicators to forecast stock market
碩士 === 國立彰化師範大學 === 企業管理學系 === 101 === In the recent years, many financial event impact the global stock market deeply. For instance, subprime mortgage crisis in USA, European sovereign debt crisis, the critical issue of the stock income tax in Taiwan etc., they make the global economic still heavil...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2013
|
Online Access: | http://ndltd.ncl.edu.tw/handle/39980944653314097713 |
id |
ndltd-TW-101NCUE5121009 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-101NCUE51210092016-03-16T04:15:02Z http://ndltd.ncl.edu.tw/handle/39980944653314097713 Combination of Ensemble Algorithms with technical indicators to forecast stock market 以集成式演算法結合技術指標進行股市預測分析 Chun-Teng Ko 柯鈞騰 碩士 國立彰化師範大學 企業管理學系 101 In the recent years, many financial event impact the global stock market deeply. For instance, subprime mortgage crisis in USA, European sovereign debt crisis, the critical issue of the stock income tax in Taiwan etc., they make the global economic still heavily fluctuate. In empirical research, we can find the technical analysis is the useful tool to predict the movement of the stock price. Investigating the relations between volume and price variability in stock markets is the advantage of the technical analysis to forecast and control the trading rule. This paper adopted several technical analysis indicators, and use data mining approaches to sift out the results for trading signal. This paper not only experimented with single classifier such as Logistic, Support Vector Machine, and Neural Network, but also tested Ensemble Algorithms such as Adabooost.M1 and Bagging. This paper collected the daily data of the six countries which are USA, UK, Germany, Japan, Canada, and Taiwan, and examined the performance of the three single classifier and two ensemble algorithms. In conclusion, this paper found Logistic combined with AdaboostM1 have the best predictive. Shian-Chang Huang 黃憲彰 2013 學位論文 ; thesis 36 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立彰化師範大學 === 企業管理學系 === 101 === In the recent years, many financial event impact the global stock market deeply. For instance, subprime mortgage crisis in USA, European sovereign debt crisis, the critical issue of the stock income tax in Taiwan etc., they make the global economic still heavily fluctuate.
In empirical research, we can find the technical analysis is the useful tool to predict the movement of the stock price. Investigating the relations between volume and price variability in stock markets is the advantage of the technical analysis to forecast and control the trading rule. This paper adopted several technical analysis indicators, and use data mining approaches to sift out the results for trading signal. This paper not only experimented with single classifier such as Logistic, Support Vector Machine, and Neural Network, but also tested Ensemble Algorithms such as Adabooost.M1 and Bagging.
This paper collected the daily data of the six countries which are USA, UK, Germany, Japan, Canada, and Taiwan, and examined the performance of the three single classifier and two ensemble algorithms. In conclusion, this paper found Logistic combined with AdaboostM1 have the best predictive.
|
author2 |
Shian-Chang Huang |
author_facet |
Shian-Chang Huang Chun-Teng Ko 柯鈞騰 |
author |
Chun-Teng Ko 柯鈞騰 |
spellingShingle |
Chun-Teng Ko 柯鈞騰 Combination of Ensemble Algorithms with technical indicators to forecast stock market |
author_sort |
Chun-Teng Ko |
title |
Combination of Ensemble Algorithms with technical indicators to forecast stock market |
title_short |
Combination of Ensemble Algorithms with technical indicators to forecast stock market |
title_full |
Combination of Ensemble Algorithms with technical indicators to forecast stock market |
title_fullStr |
Combination of Ensemble Algorithms with technical indicators to forecast stock market |
title_full_unstemmed |
Combination of Ensemble Algorithms with technical indicators to forecast stock market |
title_sort |
combination of ensemble algorithms with technical indicators to forecast stock market |
publishDate |
2013 |
url |
http://ndltd.ncl.edu.tw/handle/39980944653314097713 |
work_keys_str_mv |
AT chuntengko combinationofensemblealgorithmswithtechnicalindicatorstoforecaststockmarket AT kējūnténg combinationofensemblealgorithmswithtechnicalindicatorstoforecaststockmarket AT chuntengko yǐjíchéngshìyǎnsuànfǎjiéhéjìshùzhǐbiāojìnxínggǔshìyùcèfēnxī AT kējūnténg yǐjíchéngshìyǎnsuànfǎjiéhéjìshùzhǐbiāojìnxínggǔshìyùcèfēnxī |
_version_ |
1718205968826761216 |