An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option
碩士 === 國立交通大學 === 管理學院碩士在職專班資訊管理組 === 93 === This study applied learning classifier system on Index Option intraday trading analysis. Many researches have showed that Taiwan stock market is a highly systematic risky economy. Investors have to adequately avoid the highly systematic risk to accumulate...
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ndltd-TW-093NCTU53960422016-06-06T04:10:45Z http://ndltd.ncl.edu.tw/handle/84333228424424390962 An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option 應用分類元系統於台指選擇權日內價格分析之研究 Weh-Chuan Tseng 曾文娟 碩士 國立交通大學 管理學院碩士在職專班資訊管理組 93 This study applied learning classifier system on Index Option intraday trading analysis. Many researches have showed that Taiwan stock market is a highly systematic risky economy. Investors have to adequately avoid the highly systematic risk to accumulate the potential profit in this kind of economic environment. By the way, the short term investment, such as intraday trading, is compared to the long term investment, is a well-chosen strategy to investors accumulating the profit. Owing to the Index Option is a kind of hedging financial commodity and the related higher range of price fluctuation, investors could bring it up and have the opportunity to obtain the higher profit with lower risk. Therefore, this study tried to build up an Index Option intraday trading model that gives the investments by holding short term position to avoid systematic risk. As for the model kernel, this study applied learning classifier system, an integrated artificial intelligence approach that incorporates reinforcement machine learning method to Genetic Algorithm with the dynamic learning ability. And the output of this model would be a suggestion of the trend prediction of short term price fluctuation to investors. Furthermore, this study utilizes Wilson’s XCS as the system kernel of this model. The input and output formation of this model is drawn up according to B-S pricing model. By the way, the input factors of this model also are concerned with the moving of the Future price, volume, the Index Option volume and its implied volatility. In the Experiments, this study selects Taiwan Index Option as the commodity to the prediction system with two parts, call-option and put-option. To evaluate the option model performance, this study is respectively compared the accuracy with the Random Walk Model and compared the accumulative profit with Buy-and-Hold strategy as well. Finally, the empirical result shows that the system could be successfully generalized the Index Option intraday trading rules of short term prediction with 10-minute and 20-minute, and all of them have been exhibited remarkable outcome. An-Pin Chen 陳安斌 2005 學位論文 ; thesis 69 zh-TW |
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碩士 === 國立交通大學 === 管理學院碩士在職專班資訊管理組 === 93 === This study applied learning classifier system on Index Option intraday trading analysis. Many researches have showed that Taiwan stock market is a highly systematic risky economy. Investors have to adequately avoid the highly systematic risk to accumulate the potential profit in this kind of economic environment. By the way, the short term investment, such as intraday trading, is compared to the long term investment, is a well-chosen strategy to investors accumulating the profit. Owing to the Index Option is a kind of hedging financial commodity and the related higher range of price fluctuation, investors could bring it up and have the opportunity to obtain the higher profit with lower risk. Therefore, this study tried to build up an Index Option intraday trading model that gives the investments by holding short term position to avoid systematic risk. As for the model kernel, this study applied learning classifier system, an integrated artificial intelligence approach that incorporates reinforcement machine learning method to Genetic Algorithm with the dynamic learning ability. And the output of this model would be a suggestion of the trend prediction of short term price fluctuation to investors.
Furthermore, this study utilizes Wilson’s XCS as the system kernel of this model. The input and output formation of this model is drawn up according to B-S pricing model. By the way, the input factors of this model also are concerned with the moving of the Future price, volume, the Index Option volume and its implied volatility. In the Experiments, this study selects Taiwan Index Option as the commodity to the prediction system with two parts, call-option and put-option. To evaluate the option model performance, this study is respectively compared the accuracy with the Random Walk Model and compared the accumulative profit with Buy-and-Hold strategy as well. Finally, the empirical result shows that the system could be successfully generalized the Index Option intraday trading rules of short term prediction with 10-minute and 20-minute, and all of them have been exhibited remarkable outcome.
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author2 |
An-Pin Chen |
author_facet |
An-Pin Chen Weh-Chuan Tseng 曾文娟 |
author |
Weh-Chuan Tseng 曾文娟 |
spellingShingle |
Weh-Chuan Tseng 曾文娟 An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option |
author_sort |
Weh-Chuan Tseng |
title |
An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option |
title_short |
An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option |
title_full |
An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option |
title_fullStr |
An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option |
title_full_unstemmed |
An Application of Learning Classifier System on Option Intraday Trading Analysis – an Example of Taiwan Index Option |
title_sort |
application of learning classifier system on option intraday trading analysis – an example of taiwan index option |
publishDate |
2005 |
url |
http://ndltd.ncl.edu.tw/handle/84333228424424390962 |
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