Research insight · Issue 001
Sector downside risk and market concentration
Downside risk is unevenly distributed and changes across market regimes.
- Published
- Data through
- 28 July 2026
ContentsExecutive summary
Executive summary
Downside risk is unevenly distributed and changes across market regimes.
- 01Technology currently contributes the largest share of system Expected Shortfall.
- 02Downside leadership rotates through time.
- 03Deeper drawdowns can coincide with more concentrated leadership.
- Current largest contributor
- Technology
- 21.9%
- Current top-three share
- 55.4%
- Most frequent historical leader
- Energy
- 25.4%
- Sectors analysed
- 9
- 2001–2026
Why it matters
Sector volatility alone does not explain contribution to shared losses. Monitoring who drives downside risk, and how concentrated that leadership becomes, provides a clearer view of changing market structure.
Analysis 01
Sector contribution to downside risk
Technology contributes the largest share of system Expected Shortfall at 21.9%, followed by Consumer Discretionary and Materials. Together, the three sectors account for 55.4% of aggregate downside contribution. Utilities, Healthcare, Energy and Consumer Staples contribute substantially less, indicating that current downside risk is unevenly distributed across sectors.
A horizontal ranking of nine sector contributions. Technology is highest at 21.9%, followed by Consumer Discretionary at 18.9% and Materials at 14.6%; Consumer Staples is lowest at 2.9%.
Analysis 02
Downside leadership rotates over time
Downside leadership is not static. Technology dominates during some periods, while Financials, Energy and Materials become the largest contributors during others. Rather than a single sector driving every downturn, leadership rotates as market regimes evolve, reinforcing the importance of monitoring changing sources of downside contribution.
A heat map of nine sector contribution shares from 2001 to 2026. Darker periods move among Materials, Energy, Financials, Industrials, Technology and Consumer Discretionary, showing that downside leadership changes through time.
Analysis 03
Volatility does not tell the whole story
Higher volatility does not necessarily imply a larger contribution to shared market losses. While Technology combines elevated volatility with high downside importance, Energy exhibits relatively high volatility but a much smaller contribution to Expected Shortfall. Downside leadership therefore depends on more than standalone risk and reflects how sectors contribute to losses within the broader system.
A scatter plot of annualised volatility against share of system Expected Shortfall for nine sectors. Technology sits highest on both measures, while Energy has relatively high volatility but a much smaller downside contribution.
Analysis 04
Downside leadership becomes more concentrated during market stress
Deeper market drawdowns tend to coincide with a larger contribution from the leading sector. The relationship is modest, but it suggests that downside leadership can become more concentrated as market conditions deteriorate rather than rising evenly across all sectors.
A dual-axis time series from 2001 to 2026 comparing the largest sector contribution with the S&P 500 drawdown. Peaks in the leading contribution sometimes align with deeper market drawdowns.
Analysis 05
Deeper drawdowns can coincide with fewer effective contributors
The effective number of sector contributors tends to decline during some deeper market drawdowns, although the unconditional relationship is weak. This suggests that market stress can coincide with narrower downside participation, but concentration is not determined by drawdown depth alone.
A dual-axis time series from 2001 to 2026 comparing the effective number of sector contributors with the S&P 500 drawdown. The contributor count generally stays between seven and nine but falls during some periods of deeper stress.
Analysis 06
Downside importance is not fixed
Technology is currently the largest contributor to downside risk, but it is not the most frequent historical leader. Energy led in 25.4% of rolling windows, followed closely by Financials at 24.4% and Technology at 23.9%. Leadership therefore changes across market regimes rather than remaining permanently concentrated in one sector.
A horizontal ranking of the share of rolling windows led by each sector. Energy leads at 25.4%, Financials at 24.4% and Technology at 23.9%; the remaining sectors lead much less frequently.
Methodology
How the analysis is constructed
- Sample period
- 2001–2026
- Market benchmark
- S&P 500 Index
- Data source
- Yahoo Finance (via yfinance)
- Data through
- 28 July 2026
- Sector universe
- XLB · Materials
- XLE · Energy
- XLF · Financials
- XLI · Industrials
- XLK · Technology
- XLP · Consumer Staples
- XLU · Utilities
- XLV · Healthcare
- XLY · Consumer Discretionary
Expected Shortfall
Downside risk is measured using empirical Expected Shortfall at the 95% confidence level. Tail observations are defined as the worst 5% of equally weighted system returns within each rolling estimation window.
Sector contribution
Component Expected Shortfall for each sector is calculated as its portfolio weight multiplied by its average loss conditional on the equally weighted system return falling within its worst 5% of observations. Component contributions are then normalised so that sector shares sum to 100%.
Largest contributor
For each rolling window, the sector with the highest contribution to aggregate downside risk is identified.
Effective contributors
The effective number of contributors is calculated using the inverse Herfindahl concentration index of sector Expected Shortfall (ES) contribution shares.
- N_eff is the effective number of sector contributors.
- w_i is sector i’s share of aggregate system Expected Shortfall.
- n is the number of sectors included in the analysis.
This measure estimates how broadly downside risk is distributed across sectors. Higher values indicate that downside risk is shared more evenly across multiple sectors, while lower values indicate that downside risk is increasingly concentrated within fewer dominant contributors.
Rolling estimation
Statistics are estimated using a 252-trading-day rolling window, recalculated daily. This captures changes in sector downside contribution through time rather than assuming fixed relationships.
System construction
The system return is constructed as an equally weighted average of the nine sector ETF returns. Each sector therefore receives a weight of 1/9.
Benchmark
Market drawdowns are calculated using the SPDR S&P 500 ETF Trust (SPY), based on auto-adjusted closing prices obtained from Yahoo Finance via yfinance.
Returns and frequency
Daily logarithmic returns are calculated from auto-adjusted closing prices. Rolling measures are estimated daily, while selected figures are resampled to month-end observations for presentation.
Reproducibility
All analysis was performed using Python. Charts and summary statistics were generated directly from the underlying analysis pipeline without manual adjustment.
Research workflow
- Data
- Daily auto-adjusted closing prices
- Returns
- Daily logarithmic returns
- System construction
- Equally weighted across nine sectors
- Risk measure
- Empirical Expected Shortfall, 95% confidence level
- Tail definition
- Worst 5% of system-return observations
- Rolling window
- 252 trading days, recalculated daily
- Benchmark
- SPY
- Aggregation
- Component ES shares normalised to 100%
- Presentation frequency
- Daily estimates; selected charts shown monthly
- Output
- Figures and summary statistics
Lower values indicate that downside contribution is concentrated among fewer sectors, while higher values indicate that contribution is distributed more broadly across the sector universe.
Research implications
What the evidence shows
The evidence presented in this report shows several characteristics of downside risk that are not immediately visible through traditional measures of market volatility.
- 01Downside risk is unevenly distributed across sectors.
- 02Downside leadership rotates through time rather than remaining permanently concentrated in any single sector.
- 03Higher volatility alone does not determine downside importance.
- 04Periods of deeper market stress can coincide with more concentrated downside leadership.
- 05As concentration rises, the breadth of sector participation in downside risk narrows.
- 06Monitoring changes in downside leadership may provide additional insight into evolving market structure and vulnerability.
Together, these findings suggest that understanding how downside risk is distributed across markets may be as important as measuring the size of market declines themselves. Monitoring changes in concentration and leadership provides additional context for assessing evolving market conditions and the resilience of diversification.
Publication record
Sources and disclosures
Data
- Yahoo Finance, accessed through yfinance.
- SPDR sector ETFs: XLB, XLE, XLF, XLI, XLK, XLP, XLU, XLV and XLY.
- Market benchmark: SPDR S&P 500 ETF Trust (SPY).
- Data through 28 July 2026.
Methodology reference
- Acerbi, C. and Tasche, D. (2002). “On the coherence of expected shortfall.” Journal of Banking & Finance, 26(7), 1487–1503.
Software
Python, pandas, NumPy, matplotlib and yfinance.
This publication has been prepared by LB Research for informational and educational purposes only. It does not constitute investment advice, investment research as defined under applicable regulation, a recommendation, or an offer to buy or sell any financial instrument.
The analysis is based on publicly available market data and the methodologies described within this publication. While reasonable care has been taken in preparing this report, no representation or warranty is made regarding the accuracy, completeness or timeliness of the information presented.
Any opinions expressed reflect the author's judgement at the publication date and may change without notice. Past performance is not indicative of future results.
Readers remain solely responsible for their own investment decisions.
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