Applying technical analysis of stock trends to trading strategy of dynamic portfolio insurance

Jung Bin Li, Sheng Hsiu Wu, Mu Yen Chen, An Pin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the trading operation of dynamic portfolio insurance, TIPP (Time Invariant Portfolio Protection), when adjusting active assets, only considers the scale of asset of that time regardless of how market trend proceeds. In other words, TIPP is clumsy in evading loss and pursuing profits. This study makes use of the predictability of artificial neural network, via market trend analysis and the learning of historical data, to find out the most optimized Multiplier of TIPP in various situations so as to optimize dynamic portfolio insurance. This study utilizes two kinds of artificial neural networks. One is to employ the price, quantity, and tendency technical index as the input item to predict the future rise or drop as the output item. The other is to employ the various technical indexes when MACD crossed on that day to serve as the input item, and the output items are the future range and days of rise and drop. The statistics show that the profitability of the prediction module of crossed MACD is better than the artificial neural networks; both are better than the traditional strategy operation of TIPP.

Original languageEnglish
Title of host publicationProceedings of the 9th Joint Conference on Information Sciences, JCIS 2006
DOIs
Publication statusPublished - 2006
Event9th Joint Conference on Information Sciences, JCIS 2006 - Taiwan, ROC, Taiwan
Duration: 2006 Oct 82006 Oct 11

Publication series

NameProceedings of the 9th Joint Conference on Information Sciences, JCIS 2006
Volume2006

Other

Other9th Joint Conference on Information Sciences, JCIS 2006
CountryTaiwan
CityTaiwan, ROC
Period06-10-0806-10-11

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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