Historical Context

Rolling Returns: What They Measure and How to Read the Chart

Rolling returns provide a powerful way to analyze investment performance over different time periods without being anchored to a specific start or end date. Instead of looking at returns from a fixed point in time (like "returns since 2000"), rolling returns calculate performance over a moving window (e.g., 5 years) at each point in the historical timeline. This approach eliminates the bias that can come from cherry-picking favorable or unfavorable start dates, giving a more complete picture of how an investment strategy has performed across different market conditions.

For value spreads, rolling returns reveal whether value stocks have consistently outperformed growth stocks over extended periods, or if the outperformance has been concentrated in specific episodes. When you see a rolling return chart showing positive values, it indicates that value stocks delivered higher returns than growth stocks over that particular window. Negative values show the opposite—growth stocks outperformed. The magnitude of these returns, combined with their persistence, helps investors understand the reliability of the value premium and whether it's a consistent feature of markets or one that appears only during certain regimes.

The chart on this page displays rolling annualized returns calculated over windows of 1, 3, 5, or 10 years, depending on your selection. Longer windows (5-10 years) smooth out short-term volatility and reveal longer-term trends, while shorter windows (1-3 years) capture more recent dynamics. When rolling returns remain consistently positive over long periods, it suggests a robust value premium. When they fluctuate between positive and negative, it indicates that the value-growth relationship is more cyclical and regime-dependent.

Drawdowns: What They Tell You About Value vs Growth

Drawdowns measure the peak-to-trough decline in an investment's value during a specific period. For value spreads, drawdowns occur when growth stocks significantly outperform value stocks, causing the spread to decline from its previous peak. Understanding drawdowns is crucial because they reveal the pain periods that value investors must endure, even when the long-term premium is positive. Historical analysis shows that value strategies can experience severe drawdowns lasting years or even decades, testing investor conviction and potentially causing them to abandon the strategy at the worst possible time.

The distribution statistics on this page help quantify these drawdowns. The minimum value shows the most extreme negative spread in the historical record, while percentiles reveal how often such extremes occur. For example, if the current value is at the 10th percentile, it means that 90% of historical observations showed higher spreads, indicating that value stocks are currently very cheap relative to growth stocks. This could represent either a buying opportunity or a sign that structural changes have permanently altered the value-growth relationship.

Skewness and kurtosis provide additional insights into the distribution of returns. Positive skewness indicates that extreme positive returns (value outperformance) are more common than extreme negative returns, which would be favorable for value investors. High kurtosis (fat tails) suggests that extreme events occur more frequently than a normal distribution would predict, meaning that value spreads can experience sudden, dramatic shifts. Understanding these statistical properties helps investors set realistic expectations about the volatility and potential for extreme outcomes in value investing.

Historical Regimes: What High and Low Spreads Have Meant Historically

Financial markets operate in distinct regimes characterized by different economic conditions, investor sentiment, and structural factors. Value spreads have historically been influenced by these regimes, with certain periods favoring value stocks and others favoring growth stocks. Understanding these historical patterns helps contextualize current market conditions and provides insight into potential future developments.

The 1970s and Early 1980s: This period was characterized by high inflation, rising interest rates, and economic uncertainty. Value stocks, particularly those in energy, materials, and financial sectors, significantly outperformed growth stocks. The value spread reached historically wide levels as investors favored companies with tangible assets and current earnings over growth prospects. This regime demonstrated that value investing can thrive during inflationary periods when cash flows today are valued more highly than uncertain future growth.

The 1990s Dot-Com Bubble: The late 1990s saw an extreme growth regime where technology and internet stocks reached unprecedented valuations. Value stocks dramatically underperformed, and the value spread became extremely negative. This period tested value investors' patience, as traditional valuation metrics seemed irrelevant in the face of "new economy" narratives. However, the bubble's collapse from 2000 to 2002 led to one of the strongest value outperformance periods in history, as overvalued growth stocks crashed while value stocks held relatively steady.

The 2010s Growth Regime: Following the 2008 financial crisis, a prolonged period of low interest rates, quantitative easing, and technological disruption created an environment highly favorable to growth stocks. Large-cap technology companies, particularly in the FAANG group (Facebook, Apple, Amazon, Netflix, Google), drove market returns while value stocks languished. This decade-long underperformance led to historically wide value spreads by 2020, suggesting that value stocks were extremely cheap relative to growth stocks.

The 2020-2022 Value Rotation: The COVID-19 pandemic and subsequent recovery, combined with rising inflation expectations and interest rate increases, triggered a significant rotation toward value stocks in late 2020 and early 2021. This period demonstrated how quickly market regimes can shift when economic conditions change. However, the rotation was not sustained uniformly, as growth stocks recovered in 2021 before value again outperformed in 2022 amid rising rates and inflation concerns.

These historical regimes illustrate that value spreads are not static but evolve with economic conditions, monetary policy, technological change, and investor psychology. Current spread levels must be interpreted in the context of these historical patterns, recognizing that what seems like an extreme valuation today may persist longer than expected, or reverse more quickly than anticipated, depending on the underlying regime drivers.

Using Distribution Statistics for Investment Research

The distribution statistics presented on this page provide a comprehensive view of how value spreads have behaved historically. The mean and median offer measures of central tendency, though they may differ significantly if the distribution is skewed. The standard deviation quantifies the typical variability of spreads, helping investors understand the normal range of fluctuations. When current values fall outside of ±2 standard deviations from the mean, they represent statistically unusual conditions that may warrant attention.

Percentile rankings are particularly useful for understanding where current conditions stand relative to history. A current percentile of 95% means that the value spread is higher than 95% of all historical observations, suggesting value stocks are expensive relative to their historical relationship with growth stocks. Conversely, a percentile of 5% indicates that value stocks are extremely cheap. However, investors should be cautious about assuming mean reversion will occur quickly—extreme percentiles can persist for extended periods, as demonstrated by the 2010s growth regime.

The z-score provides a standardized measure of how many standard deviations the current value is from the historical mean. This metric is useful for comparing across different value definitions, as it normalizes for the different scales and volatilities of various metrics. A z-score beyond ±2 is typically considered statistically significant, while values beyond ±3 are extremely rare and may indicate fundamental shifts in market structure or temporary market dislocations that could present opportunities or risks.