Shen, Ning. 2009. Institutional
Algorithmic Trading, Statistical Arbitrage And Technical Analysis. Doctoral
Dissertation, Cornell University.
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.
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