This analysis examines how different market sectors—Technology, Defence & Aerospace, and Energy—have behaved since 2020, a period defined by significant macroeconomic disruption and recovery.

The COVID-19 shock in 2020 created a sharp market contraction followed by an unusually strong recovery phase driven by fiscal stimulus and changing consumption patterns. During this period, technology stocks exhibited strong growth characteristics, benefiting from increased digital adoption, but also showed sensitivity to later macroeconomic tightening.

In contrast, defence and aerospace stocks demonstrated more stable behaviour, particularly during periods of geopolitical tension. Events such as the war in Ukraine contributed to increased government spending in this sector, supporting more consistent performance and relatively resilient risk profiles.

Energy stocks, on the other hand, displayed highly cyclical behaviour. Their performance was closely tied to supply shocks and inflationary pressures, particularly during 2022, where energy prices surged. This resulted in periods of strong returns but also elevated volatility. During the inflation tightening regime, tech cluster returns show reduced magnitude and higher oscillation, consistent with a risk-off environment affecting growth-sensitive equities.

Clustering analysis reveals that stock behaviour does not strictly follow sector boundaries. Instead, stocks are grouped based on similarities in return patterns, volatility, and drawdown characteristics. This highlights that under different market regimes, assets from different sectors can exhibit comparable risk-return profiles.

The time-series analysis further shows that market regimes play a critical role in shaping cluster behaviour. Periods of crisis, recovery, and tightening cycles lead to clear divergence between clusters, particularly in terms of stability, recovery speed, and downside risk.

Overall, this analysis demonstrates that while sector identity provides a useful starting point, understanding market behaviour requires examining how assets respond dynamically to macroeconomic conditions. Clustering offers a structured way to uncover these patterns and support more informed decision-making.