In this thesis, we propose two genetic-programming-based models that improve the
trading strategies for mutual funds. These two models can help investors get returns
and reduce risks. The first model increases the return by selecting funds with high
Sortino ratios and allocates the capital equally, achieving the best annualized return.
The second model also selects funds with high Sortino ratios, but reduces the risk
by allocating the capital with the mean variance model.
Most importantly, our model utilizes the genetic programming to generate
feasible trading strategies to gain return, which is suitable for the market that
changes anytime. To verify our models, we simulate the investment for mutual
funds from January 1999 to December 2009 (11 years in total). The experimental
results show that our first model can gain return from 2004/1/1 to 2008/12/31,
achieving the best annualized return 9.11%, which is better than the annualized
return 6.89% of previous approaches. In addition, our second model with smaller
downside volatility can achieve almost the same return as previous results.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0824110-122030 |
| Date | 24 August 2010 |
| Creators | Chen, Hung-Hsin |
| Contributors | Chang-Biau Yang, Chia-Ping Chen, Jen-Chih Yao, Kuo-Si Huang, So-De Shyu |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0824110-122030 |
| Rights | off_campus_withheld, Copyright information available at source archive |
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