Evaluating performance capacity of high frequency trading strategies, based on comparative ratios and market inefficiency at Helsinki Stock Exchange
1University of Oulu, Oulu Business School, Department of Finance, Finance
|Online Access:||PDF Full Text (PDF, 3.5 MB)|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201412042083
|Publish Date:|| 2014-12-08
|Thesis type:||Master's thesis
High frequency trading is not only about the speed but also about the effective trading strategies it uses to perform the trade. Performance capacity evaluation of high frequency trading strategies is done using different comparative ratios. Studies find, due to tight spread, it is difficult for high frequency traders to generate significant alpha by trading the highly liquid stocks using market making strategy. But they can still generate positive return with Sharpe ratio almost equal to market. They act more like market makers following this strategy. The capacity of other high frequency trading strategies lies in between (58–75) %. Statistical arbitrage strategy is the best among all the high frequency trading strategies. Sharpe ratio as a main tool of comparison between high frequency and non-high frequency traders, shows multiple times higher Sharpe for high frequency traders in comparison to non-high frequency traders. Value at Risk (VaR) suggests the probability of generating positive return for all the strategies having long and short positions.
This thesis takes one month high frequency limit order and tick data from NASDAQ OMX Nordic and select six mostly traded Finnish stocks based on their limit order book activities. Basic limit order book activities of all the selected stocks is analyzed including and excluding non-high frequency activates to make sure all the selected stocks are influenced by high frequency activities, so that the result is more accurate. This thesis follows the high frequency trading strategies and respective holding periods suggested by Aldridge (2009). Sometimes strategies work not because strategies are efficient but due to market inefficiency. This thesis crosschecks the market inefficiency with auto-regressive based test. Due to tick data and a very short time interval between the two observations, it finds strong influence of past returns and past price movements in the current return suggesting inefficiency in the market.
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