Applying Technical Analysis of Stock Trends to Trading Strategy of Dynamic Portfolio Insurance
Jung-Bin Li 0, Sheng-Hsiu Wu 1, Mu-Yen Chen 2, An-Pin Chen 3
0Institute of Information Management, National Chiao Tung University
1Institute of Information Management, National Chiao Tung University
2Department of Accounting, National Changhua University of Education.
3Institute of Information Management, National Chiao Tung University
Available Online October 2006.
- https://doi.org/10.2991/jnmp....1How to use a DOI?
- neural network, genetic algorithm, portfolio insurance
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jung-Bin Li AU - Sheng-Hsiu Wu AU - Mu-Yen Chen AU - An-Pin Chen PY - 2006/10 DA - 2006/10 TI - Applying Technical Analysis of Stock Trends to Trading Strategy of Dynamic Portfolio Insurance PB - Atlantis Press SP - 1 EP - 4 SN - 1951-6851 UR - https://doi.org/10.2991/jnmp....1 DO - https://doi.org/10.2991/jnmp....1 ID - Li2006/10 ER -