Institutional Algorithmic Trading, Statistical Arbitrage And Technical Analysis
| dc.contributor.author | Shen, Ning | en_US |
| dc.date.accessioned | 2009-10-13T13:52:49Z | |
| dc.date.available | 2014-10-13T06:27:56Z | |
| dc.date.issued | 2009-10-13T13:52:49Z | |
| dc.description.abstract | Technical analysis tools are widely used by short term investors in the financial market to identify trading opportunities and generate abnormal profit. Two of the most popular ones, Moving Average Convergence - Divergence and Bollinger Bands, are adopted in this study for algorithmic traders and statistical arbitragers (intraday trading) to reveal their effectiveness in terms of realizing sizeable profit before and after transaction cost. The simple oscillator signals derived from MACD and BB fail to efficiently recognize optimal trading timing and negative profit before and after transaction cost are realized under both strategies. Numerical analysis describes the sensitivity of profit with and without transaction fee to the strategies parameters. The results disclose that the selection of relevant parameters is not able to improve the performance of the strategies. A Long Only Filter Strategy (LOFS) is created to further investigate the possible strategies employed by institutional investors. Successfully generating considerable profit after transaction cost with a significant lower level risk, LOFS outperforms the buy-and-hold benchmark strategy as well as MACD and Bollinger Bands. LOFS is a promising strategy for statistical arbitragers who aim to profit from trading after accounting for transaction costs. | en_US |
| dc.identifier.other | bibid: 6714242 | |
| dc.identifier.uri | https://hdl.handle.net/1813/13821 | |
| dc.language.iso | en_US | en_US |
| dc.title | Institutional Algorithmic Trading, Statistical Arbitrage And Technical Analysis | en_US |
| dc.type | dissertation or thesis | en_US |
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