Skip to main content
Track Hypothesis testing
19 / 20
Library

2017issue C1316-17

Partial correlation and the market-adjusted stock trend

A stock-versus-benchmark percent-change gap is an incomplete baseline because it leaves the market's linear pull inside the comparison. This article rebuilds that setup as a three-variable linear model and tests the remaining association with a t-statistic across nested lookbacks.

  • A raw stock-versus-benchmark percent-change gap is a weak baseline because many stocks share a strong linear association with the market.
  • Partial correlation measures the strength and direction of a linear relationship after one or more control series are held fixed.
  • The market-adjusted trend is the partial correlation of a sequential trend index with a stock price after controlling for a market index.
  • Nested lookback windows ending on the same as-of date can produce market-adjusted readings and t-statistics of different sign and size.
Entries in this reading2 entries

A weak percent-change baseline

Comparing a stock's percent change with a market benchmark's percent change is treated as a weak baseline because many stocks share a strong linear association with the market. The comparison therefore does not show how a change in market direction would affect the stock.

Partial correlation after a control variable

Partial correlation measures the strength and direction of a linear relationship between two continuous series after controlling for one or more other continuous series. The held-fixed series is a control variable: a third continuous series whose linear influence is held fixed when the remaining two series are related.

Three series in the linear model

The worked setup uses three series: a sequential trend index, the stock price, and a market index. The trend index is a sequential integer assigned to each observation so calendar order can enter the linear model as a variable.

The market-adjusted trend is the partial correlation of that trend index with the stock price series after controlling for the market index. The partial association of trend and stock price given the market isolates the stock's own linear path.

IBM nested lookbacks

The IBM illustration records ordered stock and market-index prices with a trend count of 1 on 17 November 2016, when the stock was 159.80 and the index was 2187.12. Six nested lookback windows of 5, 10, 15, 20, 25, and 30 sessions were computed to that as-of date.

The market-adjusted trend reading was 0.224 over 10 sessions and -0.380 over 5 sessions. IBM's 20-session market-adjusted reading was 0.881 with a t-statistic of 3.656, while the 5-session t-statistic was -0.642. The t-statistic is a hypothesis-test summary for whether the market-adjusted linear association differs from zero inside a given lookback.

Monsanto nested lookbacks

In the Monsanto example, the market-adjusted reading was negative at 15, 20, 25, and 30 sessions and positive only at 5 and 10 sessions. Monsanto's 30-session market-adjusted reading was -0.904 with a t-statistic of -4.714, compared with 0.726 and a t-statistic of 1.142 at 5 sessions.

Editorial reading of the nested windows

Editorial reading: the same as-of date can produce a market-adjusted trend of different sign and a t-statistic of different size, depending on how many ordered observations enter the lookback window. The archive records those historical IBM and Monsanto readings. It does not rank the windows, treat a larger t-statistic as a trading signal, or establish a present-day result.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
19 of 20 in the Hypothesis testing track
201744-44 pp.Next on Hypothesis testingConstructing market-adjusted stock trend scoresA market-adjusted trend score is the partial association between a stock and a trend variable after the concurrent market path is held constant.
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
All 26 readings tagged Hypothesis testing
Also on Hypothesis testing5 readings