On the other hand, such methods rely heavily on market effect, since they do not require an in-depth understanding of market microstructure. High-frequency trading differs fundamentally from other types of trading by the capability of complex algorithms to analyze huge quantities of data. Identifying signals (alpha) and optimizing execution at the trading schedule level comprise the dynamic method of portfolio management.
To construct the list of S&P 500 companies that have been included in an index since at least 2008, it used closing price and trading volume data. Although medium frequency statistical arbitrage approaches outperform their paper benchmarks, they are highly dependent on the quality of the trading engine and optimization software.