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.

Medium frequency trading (MFT) is a trading strategy that falls between high-frequency trading (HFT) and low-frequency trading (LFT). It typically involves holding positions for a few hours to several days. MFT strategies leverage sophisticated algorithms and technology to analyze market trends and execute trades based on statistical patterns and market signals. Unlike HFT, which relies on executing a large number of trades within milliseconds, MFT focuses on optimizing trade timing and execution over a longer horizon, aiming to capitalize on short-term market inefficiencies. This approach balances the need for speed with the ability to conduct more in-depth market analysis, reducing the reliance on ultra-fast execution and infrastructure costs associated with HFT.