Inside the Crash Warning Index: What Goes Into an At-Risk Signal

Introduction

The Crash Warning Index (CWI) is a pivotal tool in MarketVibe's suite of indicators, designed to provide early warnings about potential market risks. The question we sought to answer was: "What do past At-Risk periods in CWI have in common?" Understanding these commonalities is crucial for traders who aim to avoid late exits and surprise drawdowns. By identifying the conditions that precede market downturns, traders can make more informed decisions about risk management and portfolio adjustments.

Data & Methodology

To explore this question, we examined a variety of data types, including index prices, breadth metrics such as % Above 50-DMA, A/D Net, and New High–New Low (NH–NL), as well as volatility measures like ATR%. Our analysis spanned multiple bull and bear cycles, including significant stress events, to capture a broad range of market conditions.

We focused on measuring forward returns, drawdown depth, and the duration of elevated risk periods. However, it's important to note that this research is exploratory. The sample size and regime differences mean that while we can identify tendencies, these are not foolproof predictions.

Key Patterns & Findings

Breadth Weakness and Index Highs

One key observation was that when market breadth weakened while indices made marginal new highs, future risk tended to rise. For instance, if the % Above 50-DMA fell below 40% while the index hit a new high, subsequent drawdowns were more likely.

Clusters of Elevated CWI Readings

We found that clusters of elevated CWI readings often preceded larger drawdowns, though not every cluster resulted in a downturn. For example, a CWI reading above 70 for three consecutive weeks often signaled increased market risk.

Volatility and Breadth Combinations

Certain combinations of high ATR% and weak breadth were more harmful than either condition alone. For example, when ATR% exceeded 2% and A/D Net turned negative, the market frequently experienced heightened volatility and risk.

Case Studies

Case Study 1: Pre-Crisis Build-Up

In a pre-crisis environment, the market was in a Bullish state, with indices climbing steadily. However, CWI began to flash warnings as breadth metrics like NH–NL flipped from expansion to contraction. Traders at the time felt complacent due to the rising indices, but the subsequent pullback validated the CWI's cautionary signals.

Case Study 2: Volatility Surge

During a period of elevated volatility, the market dashboard shifted to an At-Risk state. Despite initial confusion among traders, the combination of high ATR% and declining sector leadership in cyclicals signaled a regime shift. The market's eventual downturn aligned with the CWI's early warnings.

From Research to Product

Our research directly informed the design of MarketVibe's tools. Clusters of elevated risk readings guided the development of CWI threshold bands, creating clear visual cues for users. The interaction between breadth and volatility metrics encouraged us to combine these indicators, rather than relying on a single measure.

The Decision Edge Dashboard aggregates Climate, CWI, breadth, and leadership into a coherent snapshot, balancing sensitivity and robustness to avoid overfitting while providing actionable insights.

Practical Takeaways

• Treat sustained elevated CWI values as a warning about environment fragility, not a precise timing tool.
• Pay attention when breadth weakens while headline indices grind higher, as this may signal underlying market weakness.
• Use multi-metric views (Climate + CWI + breadth + volatility) to frame risk posture, rather than attempting to predict every market move.
• Monitor sector leadership shifts, particularly when defensive sectors outperform cyclicals, as this may indicate a risk-averse environment.
• Consider the duration of elevated risk readings, as prolonged periods often precede significant market changes.

Limitations & Responsible Use

While the CWI provides valuable insights, it is not without limitations. Changing market structures mean that what worked in one era may not apply in another. Data quality and survivorship bias can also affect results. Additionally, overfitting and look-ahead bias are risks that we strive to mitigate.

We encourage readers to use these insights as inputs to their own tested systems, avoiding over-reliance on any single pattern or metric. Always prioritize risk management and position sizing in your trading strategy.

If you want to monitor these risk conditions in real time, MarketVibe provides dashboards for CWI, breadth, and Climate at 1marketvibe.com.

Disclaimer: The information provided in this article is for educational purposes only and should not be considered as financial advice. Market conditions can change rapidly, and past performance is not indicative of future results. Always conduct your own research and consult with a financial advisor before making investment decisions.