Another important factor to build StatArb strategies that perform well consistently is understanding and building systems to adapt to changing portfolio compositions and relationships between different trading instruments. The drawback of having the StatArb trading strategy depend primarily on the short-term relationships between large number of trading instruments is that it is hard to understand and adapt to changing relationships between price moves in all the different instruments that constitute a portfolio. The portfolio weights themselves change over time. Principal component analysis, a statistical tool from dimensionality reduction techniques, can be used to construct, adapt, and monitor portfolio weights and significance that change over time.

The other important issue is dealing with relationships between the trading instrument and the leading instruments and also between the trading instrument and the portfolio of leading instruments. Sometimes, localized volatility and country-specific economic events cause the fundamental relationship needed to make StatArb profitable break down. For example, political or economic conditions in Brazil can start affecting the Brazilian real currency price moves to no longer be driven by major currencies around the world. Similarly, during periods of localized economic distress in Britain, say for Brexit, or in America, say due to trade wars against China, these portfolio relationships as well as the lead-lag relationships break down from historical expectations and kill the profitability of StatArb trading strategies. Trying to deal with such conditions can require a lot more statistical edges and sophistication beyond just StatArb techniques.