--- title: "Sector Rotation Scorecard Tool" description: "Sector rotation scorecard: score all 11 GICS sectors against economic cycle signals and relative strength to get overweight and underweight recommendations." canonical: https://www.getswoopr.com/sector-analysis/sector-rotation-scorecard/ source: "Swoopr Investment: https://www.getswoopr.com" --- ## Direct Answer > The Sector Rotation Scorecard combines five macro economic-cycle signals with each of the 11 GICS sectors' trailing relative-strength percentile to rank sectors and recommend overweight, market-weight, or underweight positioning. It is a top-down allocation overlay based on historical cycle patterns, not a guarantee of future sector performance. # Sector Rotation Scorecard Enter 5 macro cycle signals and each sector's trailing relative strength percentile. The scorecard combines both inputs to rank all 11 GICS sectors and recommend overweight, market-weight, and underweight designations for a top-down allocation overlay. ## Step 1, Economic Cycle Signals Current readings of key macro indicators. These determine which sector characteristics the cycle currently favors. ISM Manufacturing PMI Above 55, strong expansion 50-55, moderate expansion 45-50, mild contraction Below 45, deep contraction Yield Curve (2yr, 10yr) Steep positive (>1%) Flat to slightly positive (0-1%) Mildly inverted (−1% to 0%) Deeply inverted (<−1%) Fed Policy Stance Cutting aggressively Cutting gradually On hold / pausing Hiking gradually Hiking aggressively Inflation Trend (CPI/PCE) Falling sharply (disinflation) Falling gradually Stable Rising gradually Rising sharply Credit Spreads (HY, IG) Tight / tightening (risk-on) Normal range (3-4%) Widening (caution) Wide / elevated (>5%) ## Step 2, Sector Relative Strength Percentile For each sector, enter its current trailing 26-week RS percentile rank versus all 11 sectors (0 = worst, 100 = best). If you track the ratio (e.g. XLK/SPY), translate by ranking from weakest to strongest across all 11. Compute scorecard Reset ## Scorecard Results ### Allocation Summary | Rank | Sector | RS Score (40%) | Macro Score (60%) | Composite | Signal | | --- | --- | --- | --- | --- | --- | **How scores are calculated:** Composite score = 60% macro score + 40% RS percentile (normalized 0-10). Macro score reflects which sector characteristics the current cycle environment favors, e.g., early cycle/steep curve boosts Financials and Consumer Discretionary; late cycle boosts Energy; recession/deep contraction boosts Health Care, Utilities, Consumer Staples. Top 3 composite scores = Overweight; bottom 3 = Underweight; middle 5 = Market Weight. This tool provides a structured starting framework, not investment advice. Validate outputs against your own research before making any allocation decisions. ## How to Use This Tool 1. **Set macro signals (Step 1):** Select the current reading for each of the five economic cycle indicators. ISM PMI and yield curve carry the most weight in determining cycle phase; credit spreads and inflation trend provide additional signal. 2. **Enter RS percentiles (Step 2):** Calculate the 26-week ratio of each sector ETF to SPY, then rank all 11 sectors from weakest to strongest. Convert to percentile: the lowest-RS sector gets 0, the highest gets 100. Intermediate sectors get evenly spaced percentiles. Enter these values in the grid. 3. **Compute the scorecard:** Click "Compute scorecard" to see each sector's composite score, ranking, and overweight/underweight designation. 4. **Apply as an overlay:** Use the output to tilt sector allocations by 5-10% above benchmark weight for overweights and 3-5% below for underweights. The remaining sectors hold at market weight. 5. **Review monthly:** Recalculate when macro indicator readings change materially or when RS rankings shift. The output should change the allocation only when signals are consistent across multiple indicators, not on single-period noise. ## Methodology Each of the 11 GICS sectors carries a fixed set of favorability scores (0-10) across five macro dimensions: economic cycle phase, yield curve shape, Fed policy stance, inflation trend, and credit spreads. These scores were hand-assigned per sector, reflecting each sector's typical historical sensitivity to that dimension (for example, Financials score highest under a steep positive yield curve; Consumer Staples and Utilities score highest in deep contraction). A sector's **macro score** is the weighted average of its five dimension scores for your selected macro readings: cycle 25%, yield curve 25%, Fed stance 20%, inflation trend 15%, credit spreads 15%. Your entered relative-strength (RS) percentile (0-100) for each sector is normalized to a 0-10 **RS score** by dividing by 10. The **composite score** is 60% macro score + 40% RS score. Sectors are ranked by composite score: the top 3 are labeled Overweight, the bottom 3 Underweight, and the middle 5 Market Weight. You compute the RS percentile yourself, outside this tool, typically by ranking each sector ETF's 26-week performance ratio against SPY from weakest (0) to strongest (100). ### Assumptions and limitations - **Macro favorability scores are static, hand-assigned judgments** based on typical historical sector behavior, they are not derived from a backtest, regression, or live data feed, and they do not adapt if historical sector-macro relationships shift. - **The tool does not verify your RS percentile inputs.** Relative strength must be calculated off-tool from real price data; an incorrect or stale RS input produces a misleading composite score with no warning. - **The top-3/bottom-3/middle-5 split is fixed** regardless of how close or far apart the composite scores are, a sector ranked 3rd and one ranked 4th can have nearly identical scores yet receive different signals. - This tool provides a structured allocation-tilt framework, not investment advice, validate outputs against your own research before acting on them. ## FAQ ### Does the scorecard indicate when to change an allocation, or only what the current reading is? It reports a reading for the inputs entered at the moment they are entered. It has no memory of previous runs, applies no threshold for how much a score must move before it means something, and produces no timing signal. A sector moving from fourth to third place changes its label without necessarily reflecting a meaningful change in conditions. Comparing successive runs and recording what changed is left to the user, and it is where most of the interpretive work sits. ### What does it mean when the macro signals and the relative strength rankings disagree? It is a common and informative outcome. Macro readings describe the environment the economy is in, while relative strength describes what the market has already been pricing, and markets frequently move ahead of economic data. A sector scoring well on macro and poorly on relative strength may be early or may be wrong. The composite blends the two into one number, which hides the disagreement, so looking at the two component scores separately preserves information the ranking discards. ### Can the scorecard be applied to industries rather than sectors? Not as built. The favorability scores are assigned to the eleven sector-level groupings, and there is no equivalent set for the narrower industry tier. The general approach transfers, but it would require judgements about how each industry responds to yield curve shape, policy stance, inflation, and credit conditions, which is exactly the hand-assigned work the sector version already contains. Running industry relative strength separately and using the sector output as context is the more practical combination. ### What would cause the fixed macro favorability scores to stop reflecting reality? They encode typical historical sensitivities, so they weaken whenever a sector composition or economic exposure changes materially. Classification changes that move large companies between sectors, a sector becoming dominated by businesses with different rate sensitivity than its historical members, or a policy environment without a close historical analogue would each degrade them. Because the scores are static and not refitted, that drift is invisible inside the tool and has to be checked against how sectors are actually behaving. ### Does the scorecard account for how large each sector is in the benchmark? No. Every sector is scored on the same scale regardless of its weight in a broad index, so a top-ranked sector representing a small share of the market and one representing a large share receive the same label. That matters when translating ranks into positions, because the same percentage tilt applied to a small sector and a large one produces very different effects on a portfolio. The benchmark weights have to be brought in from outside the tool. ### How should the tilt ranges mentioned alongside the output be interpreted? As an illustration of how a ranking overlay is commonly expressed, not as a recommendation. Appropriate position sizing depends on an individual situation, objectives, time horizon, tax position, and risk tolerance, none of which the tool knows anything about. This is educational material rather than investment advice, and the ranges exist to show the mechanics of translating a rank into a weight rather than to prescribe one. ### Which public sources publish the five macro indicators the first step asks for? Purchasing manager survey readings are published by the organizations that conduct them. Treasury yield data at each maturity, from which the curve shape is read, is published by the US Treasury and mirrored by central bank data services. Policy stance is set out in central bank statements and meeting minutes. Consumer and personal consumption price measures come from national statistical agencies. Credit spread series are published by several central bank data portals. All five are available without a paid subscription. ### Why does the scorecard ask for relative strength over a 26-week window? A window of roughly six months is long enough to filter short-term noise while remaining responsive to a genuine change in leadership, and it sits inside the horizon over which sector momentum has been most studied. It is a convention rather than an optimum. A shorter window produces more frequent rank changes and more reversals, a longer one lags turns. Whichever is used, applying it consistently across all eleven sectors matters more than the specific length. ### How does this scorecard relate to the leading, weakening, lagging, and improving states? The state framework classifies a sector by relative strength and the momentum of that relative strength, using no macro input at all. This scorecard blends a single relative strength percentile with macro favorability into one ranked number. They can be used together: the state describes where a sector sits in its relative performance path, while the macro component describes whether the environment supports that path continuing. Neither replaces the other. ## References - [CFA Institute Research and Policy Center](https://rpc.cfainstitute.org/) - [U.S. Bureau of Economic Analysis: industry and GDP-by-sector data](https://www.bea.gov/) ## Related Reading - [Sector & Industry Analysis](https://www.getswoopr.com/sector-analysis/) the section this guide belongs to - [Airline RASM and CASM Explained](https://www.getswoopr.com/sector-analysis/airline-rasm-and-casm/) a companion guide in this cluster - [Bank Analysis: NIM, CET1, and Credit Quality](https://www.getswoopr.com/sector-analysis/bank-analysis-nim-cet1-credit-quality/) a companion guide in this cluster - [Bank CET1 Ratio](https://www.getswoopr.com/sector-analysis/bank-cet1-ratio/) a companion guide in this cluster - [Bank Efficiency Ratio Explained](https://www.getswoopr.com/sector-analysis/bank-efficiency-ratio/) a companion guide in this cluster - [Bank Net Interest Margin (NIM) Explained](https://www.getswoopr.com/sector-analysis/bank-net-interest-margin/) a companion guide in this cluster - [Biotech Cash Runway](https://www.getswoopr.com/sector-analysis/biotech-cash-runway/) a companion guide in this cluster - [Biotech Clinical Stages Explained](https://www.getswoopr.com/sector-analysis/biotech-clinical-stages/) a companion guide in this cluster - [Biotech Pipeline Analysis](https://www.getswoopr.com/sector-analysis/biotech-pipeline-analysis/) a companion guide in this cluster - [Biotech Regulatory Milestones Explained](https://www.getswoopr.com/sector-analysis/biotech-regulatory-milestones/) a companion guide in this cluster