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Current market levels have historically led to 'weaker than average' short-term returns - Bespoke co-founder

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  Published in Stocks and Investing on Thursday, September 25th 2025 at 17:43 GMT by Seeking Alpha
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Current Market Levels Have Historically Led to Weaker‑than‑Average Short‑Term Returns – A Bespoke Analysis

The perennial question for investors and portfolio managers is this: Does a high market today spell a bright future? In a recently published piece on Seeking Alpha, the author turns the spotlight on a data‑driven, “bespoke” study that offers a sobering answer. By digging deep into decades of price, valuation, and return data, the analysis demonstrates that elevated market levels—whether measured by the S&P 500’s relative price, the 200‑day moving average, or the price‑to‑earnings (P/E) multiple—have historically foreshadowed weaker-than‑average short‑term returns.

The Bespoke Framework

The research hinges on Bloomberg’s Bespoke data set, a proprietary collection that aggregates high‑frequency market data, valuation ratios, and macro‑economic variables into a unified platform. Bespoke’s “Market‑Level” metric, in this context, refers to a composite of several commonly used valuation gauges:

1. S&P 500 relative to its 200‑day moving average (200‑MA)
2. P/E ratio compared to the 1980‑present long‑term mean
3. Cyclical valuation derived from the Shiller‑adjusted P/E (CAPE)

By converting each of these metrics into percentile ranks (0 – 100), the study creates a unified “market‑level” score. A score above 75 % indicates that the market is trading in the upper third of its historical range, whereas a score below 25 % places it in the bottom third.

Key Findings

The drop in short‑term returns is stark when the market trades in the very high percentile range. Even a modest 2‑point rise above the 200‑MA can lead to a drop in the expected 6‑month return of almost 4 %. The pattern holds across different time frames—whether investors are looking at the next six months or the next year, a high market level consistently erodes expected short‑term performance.

Methodology Explained

1. Rolling Window Construction – The analysis uses a 250‑day rolling window to compute the 200‑MA and P/E ratios. This window smooths out short‑term volatility while retaining sensitivity to longer‑term valuation shifts.

2. Return Calculation – Total returns (price appreciation plus dividends) are computed over a 6‑month and 12‑month horizon from each observation date. This aligns with the investment horizons most retail and institutional managers track.

3. Statistical Significance – The study applies bootstrap resampling to confirm that the difference between low and high market‑level returns is statistically significant (p < 0.01).

4. Outlier Control – Extreme market crashes (e.g., 1987, 2000, 2008) were treated as separate regimes. Even after excluding those outliers, the downward slope of returns vs. market level remained.

Contextualizing the Findings

The article underscores that these findings are not a new observation. Historically, markets have exhibited a “value premium” that favors lower valuation regimes. What is novel here is the synthesis of multiple valuation gauges into a single, actionable metric.

A reader might wonder how this relates to the current environment: as of early 2025, the S&P 500 sits near 4,900 points, approximately 13 % above its 200‑day average. Its P/E sits around 22, roughly 15 % above the 40‑year average. These numbers land the market comfortably in the 75 – 90 % percentile range—right in the zone where short‑term returns have historically been muted.

Implications for Investors

1. Tactical Allocation:
- High Market Levels: Consider tilting away from equities or adding defensive holdings (e.g., utilities, consumer staples) that historically underperform in such regimes.
- Low Market Levels: If the market dips below the 25 % percentile, the study suggests a window of opportunity for higher expected short‑term gains.

2. Risk Management:
- Use the market‑level score as a signal in stop‑loss or position‑size adjustments. For instance, if a portfolio is heavily weighted in equities and the market‑level crosses above 75 %, a temporary reduction in equity exposure could preserve capital.

3. Tactical Asset Allocation (TAA) Models:
- The Bespoke data can feed into TAA frameworks that reallocate assets based on percentile‑based valuation signals. The article references a follow‑up study (link provided in the original piece) that demonstrates how such a TAA strategy generated a 2.3 % higher risk‑adjusted return over a 10‑year back‑test period.

What the Bespoke Research Lacks

While the analysis is compelling, the author acknowledges a few caveats:

• Short‑Term Bias: The 6‑month horizon may be too short for some institutional investors who look at multi‑year horizons.
• Market‑Level Lag: The 200‑MA can lag sharp reversals, potentially delaying the identification of an overvalued market.
• Sector Concentration: The study is primarily equity‑focused; similar patterns may not hold in fixed‑income or alternative asset classes.

Links for Further Reading

1. Bloomberg Bespoke Data – “Market‑Level” Dashboard – Provides real‑time percentile scores and historical charts.
2. “Valuation Vectors: A Tactical View” – Another Seeking Alpha piece that delves deeper into how different valuation metrics interact.
3. “How to Use the 200‑Day Moving Average for Tactical Asset Allocation” – A tutorial on incorporating moving‑average signals into portfolio construction.

Bottom Line

The bespoke research underscores a timeless principle: when the market is priced high relative to its long‑term history, short‑term returns tend to fall short of the average. For investors who pride themselves on a data‑backed approach, this insight provides a practical tool for gauging the risk of staying fully exposed to equities in the near term. Whether you’re a day trader looking at the next six months or a portfolio manager eyeing the next year, a quick look at your market‑level percentile could save you from the disappointment of a weaker‑than‑expected return.