<p>In this thesis, we conduct a case study by applying simple technical trading rules on Chinese stock market. The technical trading rules we tested are moving average rules and trading range breakout rules. The stock indices we tested are SSE A (Shanghai A) and SZSE (Shenzhen A) share, these shares are limited to the Chinese domestic traders. Our main trading rule frameworks are mainly from Brock, Lakonishok& Lebaron (1992), which including the most basic technical trading rules and covered various length of period, however we add the 25 days moving average to our frame work. We obtained our data from DataStream; the data are the daily closing prices of two indices we mentioned above.</p><p>We compared the mean return and Sharpe ratio with buy and hold. We further calculated breakeven transaction costs to test whether the technical trading rules can still add wealth to investors after adjusting the transaction costs. Our results showed that most technical trading rules perform better than buy and hold. VMA perform better than FMA and TRB, short period (25 and 50 days) performed better than longer period. On mean return, our data violated the assumption of parametric statistical test. We performed non-parametric tests, all the trading rules showed statistical significance at 95% level than buy and hold except FMA (1, 25,0), all the trading rules resulted higher Sharpe ratio than buy and hold. On transaction costs, 7 trading rules on SSE A are performed poorer than buy and hold, all the other rules provided positive breakeven transaction costs. Across the entire trading rule, both stock markets offered positive break-even transaction costs, 0.436% for SSE A and 1.369% for SZSE A. and they are both higher than the maximum transaction costs one bears.</p>
Identifer | oai:union.ndltd.org:UPSALLA/oai:DiVA.org:umu-35658 |
Date | January 2010 |
Creators | Geng, Haoming, Wang, Cheng |
Publisher | Umeå University, Umeå School of Business, Umeå University, Umeå School of Business |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, text |
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