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2017issue C1344

Constructing market-adjusted stock trend scores

A market-adjusted trend score keeps the leftover link between a stock and a trend variable after the concurrent market path is held constant. Nested lookbacks and a t-test then show whether that residual link is continuous, breaking down, or indistinguishable from no association.

  • A market-adjusted trend score is the partial association between a stock and a trend variable after the concurrent market path is held constant.
  • The residual score can be assembled from the three pairwise correlations among the stock, the trend variable, and the market, and it is bounded between -1 and +1.
  • A t-test on the residual measure asks whether the market-adjusted trend is statistically distinguishable from no association.
  • Repeating the construction on nested lookbacks that end on the same date shows whether the residual trend is continuous or breaking down.
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What the score isolates

A market-adjusted trend score can be built as the partial association between a stock and a trend variable after the concurrent market path is held constant. That leftover link is a partial correlation: the linear association between the stock series and the trend series after the shared linear effect of the market has been removed.

The point of the control is simple. A raw stock-to-trend reading can be a market ride. Holding the concurrent market path constant keeps only the name-specific remainder.

Assemble the residual from three pairwise correlations

The residual association can be assembled from the pairwise-correlation triplet. The three raw associations among the stock, the trend variable, and the market are sufficient to build the residual score.

The finished measure is bounded between -1 and +1. Values nearer those extremes indicate a stronger residual trend over the chosen window.

What market control can change

Once the market effect is removed through that linear control, the residual stock-trend link can shrink or change sign relative to the raw stock-market association. A name that looked aligned with trend on an unadjusted reading may look weaker, stronger, or reversed after the market path is held constant.

Compare nested lookbacks

A nested lookback is a set of sampling windows that all end on the same date and lengthen backward. Repeating the construction from the last five sessions through the last ten and out to the last thirty shows whether the residual trend is continuous or breaking down.

The shared end date is what makes the comparison fair. Each window answers the same question on a longer or shorter stretch of the same history.

A trend-significance test applies a t-test to the residual measure. The question is whether the market-adjusted trend is statistically distinguishable from no association over the chosen window.

The test does not replace the score. It only asks whether the leftover stock-to-trend link differs from no relationship on that sample.

When two names diverge

Two names observed over the same calendar windows can diverge. One remains tightly tied to the market, while the other shows little market association in its price change. The residual score is what makes that split visible after the common path is controlled for.

The same residual score can feed a diversification screen by preferring names whose residual trend is not merely a copy of the market path. In that construction use, the screen flags names whose path is largely a copy of the market rather than stock-specific.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
20 of 20 in the Hypothesis testing track
1998Track finished · Next track: Kalman filterT3 adaptive smoothing from regression benchmarks10 readings
All readings on this track · 20 readings
  1. 1988Serial dependence, trade excursions, and belief revision
  2. 1989Score an indicator before anyone writes a buy or sell rule
  3. 1990Evaluating safety labels in a crash window
  4. 1991Keep a decade-year only when it differs from the overall-mean
  5. 1991Audit indicator forecasts with chi-square hit rates
  6. 1992A lag-regression audit of stochastic forecast skill
  7. 1992Constructing detrended tests for calendar seasonality
  8. 1992Building a shared workbook for calendar seasonality tests
  9. 1995T-test evaluation of simulated trade returns
  10. 1995Constructing a two-proportion Z-test for hit rates
  11. 1996Treat a volume-price surge as a contingency table
  12. 1997Testing bond and stock seasonality before you trade
  13. 1997Walk-forward tests after system optimization
  14. 1997Testing breakout rate versus rally speed
  15. 2000Score the misses before a pattern is a system
  16. 2002Leave a moving-average crossover unfinished until chance cannot explain the ranges
  17. 2013Keep or drop an RSI threshold with a complementary-sample test
  18. 2013Constructing a null and a t-test for sample returns
  19. 2017Partial correlation and the market-adjusted stock trend
  20. 2017Constructing market-adjusted stock trend scores
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