1990issue C101-4
Evaluating safety labels in a crash window
A 1987 crash-window comparison split listed names into a resilient cohort and a decliner cohort with a 5 percent price-change cutoff. Mean yield, price-to-earnings, and return on equity did not differ significantly. Editorial view: treat the break as a construction stress test and keep only traits that stayed inside that loss bound.
- A 5 percent cutoff between a pre-shock close and a later close defines a resilient cohort at or above 95 percent of the earlier close and a decliner cohort below that line.
- Mean dividend yield, price-to-earnings, and return on equity did not differ significantly, and index membership, high payout, larger market value, beta, and return on equity did not mark the resilient cohort.
- The resilient cohort had a lower average share price, a smaller change from the prior January 1, lighter pre-shock trading, smaller capitalization, and lower return on assets.
- Editorial view: read the crash window as a safety contrast, not a forecast, and retire quality labels that fail the bounded-loss check.
A classroom stress test for construction
Editorial view: a market-wide break is a classroom stress test for portfolio construction. Keep only the traits that stayed inside a defined loss bound, and retire popular quality labels that fail that crash-regime check.
A 1987 crash-window comparison split listed names into two cohorts using a 5 percent price-change cutoff between a pre-shock close and a later close. The resilient cohort is the set of names whose later close stayed at or above 95 percent of the pre-shock close. The decliner cohort is the set of names whose later close sat below 95 percent of the pre-shock close.
A fixed shock window and a loss bound
Editorial framing: scenario risk analysis here means fixing a shock window and a price-loss threshold before asking which holdings stayed inside a bounded drawdown. The 5 percent cutoff is that threshold.
Selection was quasi-random with industry balancing. After that step the decliner cohort had 240 names and the resilient cohort had 85. Incomplete fields were omitted from calculations.
Compare the same statistic across both cohorts
Editorial framing: hypothesis testing here means comparing two crash-window cohorts on the same statistic with a two-sided mean test, instead of ranking names by a favorite fundamental. Mean differences were tested with a two-tailed Student t-test.
Mean dividend yield, price-to-earnings ratio, and return on equity did not differ significantly between the two cohorts. Price-to-earnings means were 19.5 with a 13.2 spread versus 16.0 with a 13.1 spread, and the difference was not significant at p greater than 0.3.
Yield means were not significantly different at p greater than 0.2 because the resilient cohort’s 3.9 percent mean had an 8.4 standard deviation, versus 2.3 percent with a 2.9 spread in the decliner cohort.
Quality labels that failed the crash-window check
Large-index membership, high dividend payout, larger market value, beta, and return on equity did not mark the more resilient cohort in this sample.
Those familiar proxies are quality labels. Editorial view: a quality label that fails a crash-window test should be retired from that use, even if it is widely treated as a safety marker.
Traits that accompanied limited loss
The more resilient cohort had a lower average share price, a smaller price change from the prior January 1, lighter pre-shock trading in shares and as a share of shares outstanding, and smaller capitalization.
Return on assets was lower in the more resilient cohort, which the analysis linked to a possibly higher mix of tangible assets.
No single fundamental or technical statistic stood out as a predictor of subsequent price action. The comparison was framed as a safety contrast rather than a forecast.
A distinct crash regime
Editorial framing: market regime classification here means treating an abrupt cross-market break as a distinct regime and asking which construction traits clustered in names that did not follow the break.
Smaller later market declines produced a similar contrast that was less marked than the 1987 window.
All readings on this track · 20 readings
- 1988Serial dependence, trade excursions, and belief revision
- 1989Score an indicator before anyone writes a buy or sell rule
- 1990Evaluating safety labels in a crash window
- 1991Keep a decade-year only when it differs from the overall-mean
- 1991Audit indicator forecasts with chi-square hit rates
- 1992A lag-regression audit of stochastic forecast skill
- 1992Constructing detrended tests for calendar seasonality
- 1992Building a shared workbook for calendar seasonality tests
- 1995T-test evaluation of simulated trade returns
- 1995Constructing a two-proportion Z-test for hit rates
- 1996Treat a volume-price surge as a contingency table
- 1997Testing bond and stock seasonality before you trade
- 1997Walk-forward tests after system optimization
- 1997Testing breakout rate versus rally speed
- 2000Score the misses before a pattern is a system
- 2002Leave a moving-average crossover unfinished until chance cannot explain the ranges
- 2013Keep or drop an RSI threshold with a complementary-sample test
- 2013Constructing a null and a t-test for sample returns
- 2017Partial correlation and the market-adjusted stock trend
- 2017Constructing market-adjusted stock trend scores