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Institutional Quant Differentiator

Rolling Returns & CAGR Dispersion Analyzer

Point-to-point trailing returns (e.g. 5-year CAGR) suffer from extreme start-date bias. Rolling returns calculate performance across every overlapping multi-year holding window in a monthly series (e.g. Jan 1995–Jan 2002, Feb 1995–Feb 2002) to reveal the shape of the outcome distribution rather than one arbitrary start date.

Real market history

Real month-end closing levels. This is a price index, so dividends are excluded — a dividend-reinvesting investor would have earned more.

380 month-end closes for S&P 500 (^GSPC), 1994-12 to 2026-07. Realised return 9.24% a year at 15.17% volatility. Source: Yahoo Finance (month-end closes), retrieved 2026-08-28.

1. Select Benchmark Index

Actual month-end closing levels. Coverage differs by index — each card shows its own range.

Active Series: S&P 500 (US Large Cap)Real month-end closes since Dec 1994, covering the dot-com crash, the 2008 financial crisis, the 2020 COVID drawdown and the 2022 rate-hike selloff. Price index — excludes dividends.

2. Rolling Horizon & Investment Capital

Configure investment size and rolling multi-year holding duration

$
Yrs

Median 7-Yr CAGR

6.15% / yr

Ending Wealth: $151,890

Probability of Positive Return

93.3%

277 of 297 windows profitable

Historical Range (Worst vs Best)

-6.2% to 15.4%

Worst: $64,066 | Best: $273,215

CAGR Dispersion (IQR)

±9.18%

Middle 50% outcome spread

Time-Diversification & Risk Verdict

93.3% Probability of Positive Gain

Across all 297 overlapping 7-year holding periods in S&P 500 (US Large Cap), your initial $100,000 produced a median terminal value of $151,890.

CAGR Probability Density Metrics

Quantifying probability of reaching specific annual return hurdles

Probability of Return > 10.0% CAGR:41.4%
Probability of Return > 15.0% CAGR:0.3%
Probability of Loss (Negative Return):6.7%
Annual Return Standard Deviation (σ):±4.98%

Historical Rolling Windows Breakdown

Latest overlapping 7-year investment windows (297 total)

Start PeriodEnd PeriodAnnualized CAGREnding Wealth
2019-012025-12+14.19% / yr$253,153
2019-022026-01+13.93% / yr$249,203
2019-032026-02+13.50% / yr$242,693
2019-042026-03+12.04% / yr$221,619
2019-052026-04+14.75% / yr$261,950
2019-062026-05+14.48% / yr$257,671
2019-072026-06+14.09% / yr$251,624
2019-082026-07+14.37% / yr$255,931
Quantitative Finance & Econometrics11 min readSanguine Straphanger

The Mathematics of Rolling Returns: Eliminating Start-Date Bias, Volatility Dampening, and True Capital Compounding

Standard mutual fund factsheets and portfolio performance presentations almost universally present point-to-point trailing returns (e.g. 1-year, 3-year, 5-year CAGR). Below is a quantitative dissection of why point-to-point metrics are inherently misleading, how rolling window distributions uncover true probability densities, and the mathematical mechanics of time-diversification.

1. The Fallacy of Point-to-Point Trailing Returns

Point-to-point Compound Annual Growth Rate (CAGR) measures the geometric rate of return from a single fixed calendar date t0 to an arbitrary terminal date tn. This creates severe endpoint dependency: if the initial date coincided with a market bottom or the terminal date coincided with an equity bubble peak, the reported CAGR will be drastically inflated.

Conversely, an exceptional fund whose terminal measurement falls immediately after a sudden 20% macroeconomic correction will appear artificially impaired. Rolling returns resolve this structural defect by sliding an identical investment window across every consecutive monthly period in history.

Formula 1: Rolling Multi-Year Geometric CAGRContinuous Slide
Rolling CAGRt(k) = [ Pt / Pt − 12k ](1/k) − 1

Pt = Asset Net Asset Value (NAV) or Index price at month t.

Pt − 12k = Asset NAV exactly k years (12k months) prior to month t.

k = Rolling horizon length in years (e.g. 3, 5, 7, 10).

2. Volatility Decay and the Law of Large Numbers

In short-term rolling horizons (k = 1 to k = 3), asset return distributions exhibit fat tails, high skewness, and extreme dispersion. As the investment tenure expands toward 7 to 10 years, the Central Limit Theorem and mean-reverting properties of economic production cause return distributions to converge tightly around the long-term earnings growth rate.

Rolling HorizonTypical Return DispersionNegative Window ProbabilityInvestor Takeaway
1-Year Rolling-38% to +65%24% – 28%Speculative noise dominates fundamentals
3-Year Rolling-8% to +32%10% – 14%Market cycle transitions begin filtering out
7-Year Rolling+4% to +21%< 1.5%Dispersion narrows substantially, though negative windows still occur
10-Year Rolling+7% to +18%0.0%Outcome dominated by earnings compounding rather than entry timing

Frequently Asked Questions & Quantitative Reference

Point-to-point returns calculate performance between two specific calendar dates, making the outcome highly vulnerable to the market conditions on those exact start and end days. Rolling returns calculate performance across every overlapping window of a fixed length (e.g. 5 or 7 years) throughout history, revealing the full probability distribution of returns.
Quantitative Analysis Disclaimer

Educational use only. Statistics here are computed from real month-end closing levels retrieved from Yahoo Finance, or from a series you upload. Two limits worth knowing: these are price indices, so dividends are excluded and a reinvesting investor would have done better; and coverage varies by index — the Nifty 50 feed begins in 2007, so it says nothing about earlier Indian market cycles. Past distributions are not forecasts. The blended 60/40 preset is modelled, not measured, and is labelled as such.