Publication: Modeling asset prices for algorithmic and high frequency trading
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2011-04-26
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Abstract
Algorithmic Trading (AT) and High Frequency (HF) trading, which are responsible for over
70% of US stocks trading volume, have greatly changed the microstructure dynamics of tick-by-tick stock data. In this paper we employ a hidden Markov model to examine how the intra-day dynamics of the stock market have changed, and how to use this information to develop trading
strategies at ultra-high frequencies. In particular, we show how to employ our model to submit limit-orders to profit from the bid-ask spread and we also provide evidence of how HF traders
may profit from liquidity incentives (liquidity rebates). We use data from February 2001 and February 2008 to show that while in 2001 the intra-day states with shortest average durations were also the ones with very few trades, in 2008 the vast majority of trades took place in the
states with shortest average durations. Moreover, in 2008 the fastest states have the smallest price impact as measured by the volatility of price innovations
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High frequency traders, Algorithmic trading, Durations, Hidden Markov model