Executive Summary

MarketThermometer aggregates multiple valuation indicators—including the Buffett Indicator, Shiller CAPE, Q ratio, and others—into a single composite score using a proprietary normalization and weighting methodology. Rather than interpreting a dozen separate metrics, the framework produces one aggregate "temperature" reading that reflects where markets stand relative to historical norms.

The current aggregate reading sits at 74 out of 100, indicating overvalued conditions. The blended z-score of 1.87 standard deviations above the long-term mean suggests markets are stretched, though momentum remains intact.

The Aggregation Problem

While numerous consolidated market indexes exist, they often suffer from methodological limitations. Some overweight recent data, others fail to account for structural changes in the economy, and most don't provide clear context for what the readings actually mean in historical terms.

MarketThermometer addresses this by normalizing each indicator to its own historical distribution (converting to z-scores), then applying a weighted aggregation formula that combines these normalized readings into a single composite score. The result is a thermometer-like visualization where the reading reflects how far current conditions deviate from long-term averages.

The Methodology

Each valuation indicator is processed through the following steps:

1. Historical Normalization: Current readings are compared against long-term historical means and standard deviations. For example, the Buffett Indicator (Market Cap/GDP) currently reads 1.92, which is 2.47 standard deviations above its historical mean of 1.05.

2. Z-Score Conversion: Each metric is converted to a z-score, allowing apples-to-apples comparison across different indicators with different scales and units.

3. Weighted Aggregation: Z-scores are combined using a proprietary weighting scheme that accounts for each indicator's historical predictive power and correlation with forward returns.

4. Temperature Mapping: The aggregate z-score is mapped to a 0-100 scale, where 50 represents fair value (zero standard deviations from mean), and readings above 67 indicate overvaluation.

The current aggregate z-score of 1.87 maps to a temperature reading of 74, placing markets in the "overvalued" range. Historically, readings above 1.5 standard deviations have been associated with compressed forward returns over subsequent 5-10 year periods.

Component Indicators

The framework incorporates several well-established valuation metrics:

Buffett Indicator (Market Cap/GDP): Currently at 1.92, or 2.47 standard deviations above the long-term mean of 1.05. This metric, popularized by Warren Buffett, compares total market capitalization to GDP. Readings above 1.5 have historically preceded periods of below-average returns.

Shiller CAPE (Cyclically Adjusted P/E): At 32.4, this is 2.48 standard deviations above the post-war mean of 22.1. The CAPE ratio smooths earnings over a 10-year period to account for business cycle fluctuations. Current levels suggest elevated multiples relative to normalized earnings.

Q Ratio (Tobin's Q): Measures market value relative to replacement cost of assets. This indicator helps assess whether markets are pricing assets above or below their fundamental replacement value.

Additional indicators include price-to-sales ratios, yield curve dynamics, and sentiment gauges, each normalized and weighted according to their historical relationship with forward returns.

Interpreting the Reading

The thermometer visualization maps the composite score to six distinct ranges:

• 0-17: Extremely Undervalued - Rare capitulation levels, historically associated with strong forward returns
• 17-33: Strongly Undervalued - Attractive entry points for long-term investors
• 33-50: Discounted - Modest discount to fair value
• 50-67: Fair Value Range - Markets trading within typical bands around long-term trends
• 67-83: Overvalued - Valuations stretched relative to history, mean reversion risk elevated
• 83-100: Extremely Overvalued - Late-cycle euphoria, forward returns typically compress

At the current reading of 74, markets fall in the "overvalued" category. The blended macro and valuation ensemble suggests defensive positioning is warranted while momentum remains intact.

Data Sources and Updates

The framework pulls data from multiple sources:

• Yahoo Finance: Real-time equity and commodity pricing
• CoinGecko: Cryptocurrency market data
• FRED (Federal Reserve Economic Data): Economic indicators and GDP data

Data syncs automatically on a regular schedule, ensuring readings reflect current market conditions. Historical data extends back several decades for most indicators, providing the statistical foundation for normalization.

Limitations and Considerations

Like any valuation framework, MarketThermometer has limitations. The aggregation methodology assumes historical relationships will persist, which may not hold during structural economic shifts. Additionally, valuation metrics are poor timing tools—markets can remain overvalued or undervalued for extended periods.

The framework is most useful for:

• Assessing long-term risk/reward dynamics
• Understanding where current conditions sit relative to history
• Identifying periods of extreme valuation that have historically preceded regime shifts

It is less useful for:

• Short-term trading signals
• Predicting exact timing of market turns
• Accounting for structural changes in the economy

Conclusion

MarketThermometer provides a framework for understanding market conditions through the lens of historical valuation norms. By aggregating multiple indicators into a single composite reading, it offers clarity on where markets stand relative to long-term averages.

The current reading of 74 suggests overvalued conditions, though this should be viewed in context of broader macro trends, monetary policy, and structural economic factors. Valuation metrics are one tool in the analytical toolkit, not a standalone investment strategy.

The dashboard is available here, showing real-time readings, historical trends, and AI-powered analysis of what the aggregate temperature means in the current market context.