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2004issue C051-5

Rebuild every industry as a share of one rank scoreboard

A published sector ratio inherits weighting noise and can hide leadership inside the group. Ranked relative strength puts every liquid name on one daily scoreboard, converts each industry into a share of that board, and leaves rank-rotation and an MACD overlay to act only on the reconstituted lines.

  • A diversified equity market can host several business cycles at once, so one broad index can hide an industry that is not moving with the headline trend.
  • Ranked relative strength ranks the full universe on one-day closing change, averages those ranks inside each industry, normalizes the averages so they sum to one, and accumulates each gap from the equal-share baseline.
  • Rank-rotation and industry-rotation then read the order, slope, and ten-way share of the reconstituted sector indexes, while MACD only marks turns on those rebuilt lines.
  • On the information-technology window the ratio and the ranked series diverged, and the same ranking on a price-level cohort showed a sub-$10 leadership turn that an MACD overlay later confirmed.
Entries in this reading3 entries

One headline trend can hide several cycles

A diversified equity market can host several business cycles at once, so one broad index can hide an industry that is not moving with the headline trend.

Dividing a published sector index by a base index inherits outlier price noise and capitalization or price weighting, so large names can mask small-cap leadership inside the same group. That conventional quotient is a relative-strength ratio.

When names inside one industry do not move tightly together, a capitalization-weighted ratio plot becomes noisy while a ranked rebuild stays cleaner. A sector is any basket assumed to share positive price co-movement, whether an industrial family or a non-industry split such as value versus growth, large versus small capitalization, or a price-level cohort.

Rank the universe, then share it out

Ranked relative strength rebuilds each industry by ranking the full universe on one-day closing change, averaging those ranks inside the group, normalizing the averages so they sum to one, and accumulating each day's gap from an equal-share baseline. The cumulative series is the reconstituted sector index that later rules compare, rotate, and overlay.

The construction uses daily closes on the 1,500 names of the S&P Super Composite, bucketed into ten GICS industries, because liquidity in that universe supports complete, continuous data.

Logarithmic and percentage one-day changes produce the same rank order. With 1,500 names the median sits at the 750th position, and a sector scores above 0.50 when more than half its names beat that median.

If all ten industries had identical returns, each would hold a 0.1 share. Persistent gaps from that equal-share baseline are treated as strength or weakness and graph as a steadily rising or falling cumulative line.

A hyperbolic-tangent map with scaling factor 3 assigns each rank a score from near 0 to near 1, with 0.5 at the median, before the same average, normalize, and accumulate steps.

One-day GICS shares of the rank scoreboard

Energy takes 16.3 percent of this day's combined rank score while information technology is left with 4.9 percent. Anything above the 10 percent equal-share line is leading the 1,500-name board that session. The percentages are the spreadsheet's normalized sector averages from the calculation table, not estimates from a plotted curve.
Energy takes 16.3 percent of this day's combined rank score while information technology is left with 4.9 percent. Anything above the 10 percent equal-share line is leading the 1,500-name board that session. The percentages are the spreadsheet's normalized sector averages from the calculation table, not estimates from a plotted curve.S&P Super Composite 1500 · 1-day

Each name is scored with a hyperbolic tangent of its one-day rate-of-change rank (scaling factor 3) inside the S&P Super Composite 1,500, then the ten GICS averages are rescaled so they sum to 100 percent. This is a single-day worked example, not the reconstituted daily index.

Read order, slope, and the ten-way share

Rank-rotation selects, avoids, or stands aside from industries by the order and slope of their reconstituted rank series rather than by a single broad-market average.

Industry-rotation reads the ten-way share of ranked strength as the market regime that tells a single name whether its group is leading, lagging, or mixed.

When the ratio and the ranks disagree

On the information-technology window, the standard ratio and the ranked series diverged. The ratio did not take out its earlier peak while the ranked series made a higher high, and from mid-April 2003 into early July that industry led the ranked panel.

From that divergence through the next labeled high, nearly 20% of information-technology names sat in the top 10% of the cross-section, ahead of every industry except health care. That is the breadth a rank-rotation rule would have read instead of the ratio's implied downturn.

Apply the same ranking to a price-level cohort

The same ranking applied to two price-level cohorts split by a 200-day average and a $10 cutoff showed a sub-$10 leadership turn in early April.

An MACD overlay on that reconstituted series confirmed the turn by month-end, and the episode lasted into early July. MACD here marks turning points and zero-line crosses on the reconstituted ranked series. It is not a substitute for the ranking construction.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
8 of 23 in the Industry rotation track
20041-6 pp.Next on Industry rotationRate-hike regimes and sector rotation as a case studyThe three linked methods share one use: put a single trade into a diversified or regime-aware context.
All readings on this track · 23 readings
  1. 1985Industry leadership carryover as a bull-regime test
  2. 1988Constructing industry-group breadth and rotation measures
  3. 1992Trendline holds, trailing stops, and industry rotation
  4. 1994Inflation-deflation regimes inside the stock cycle
  5. 1996Sector rotation across economic cycle phases
  6. 2001Rebased relative performance charts for sector rotation
  7. 2001Place a small-cap growth idea inside a regime map
  8. 2004Rebuild every industry as a share of one rank scoreboard
  9. 2004Rate-hike regimes and sector rotation as a case study
  10. 2005A two-name style-index sleeve makes rank rotation one procedure
  11. 2006Consumer staples after a smokestack cycle
  12. 2007An intra-sector regime split between builders and equity REITs
  13. 2008Country and sector weights in an Africa regional-sleeve
  14. 2011Trend permission, priced entries, and sector rotation
  15. 2012Construct a regime-aware context from sector rotation
  16. 2012Regime overlays versus rank rotation
  17. 2014Rank-based sector rotation as a portfolio test
  18. 2017Real estate as a ranked industry sleeve
  19. 2017Theme sleeves: liquidity and commission filters before industry rotation
  20. 2018Retail sleeve construction through channel rotation and daily leverage
  21. 2020Water sleeve construction: satellite size, industry mix, and liquidity
  22. 2020A ranked research terminal as a three-layer watchlist procedure
  23. 2020Regression channels for sector rotation context
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