20-year rolling return data from CRISP and PhonePe shows small-cap funds beat large caps less than half the time. Entry timing, not risk appetite, determines returns.
Alpha Score of 28 reflects poor overall profile with poor momentum, weak value, weak quality. Based on 3 of 4 signals — score is capped at 90 until remaining data ingests.
A widely held belief in Indian investing is that small-cap mutual funds consistently beat large-cap funds over long horizons. The logic seems straightforward: higher risk should yield higher returns. Retail investors with a high risk appetite therefore tilt their portfolios toward small-caps, expecting automatic outperformance.
That assumption is not supported by two decades of data. A recent CRISP Mutual Fund Scorecard analysis by PhonePe, examining 20 years of rolling returns for the Nifty Small Cap 250 TR Index versus the Nifty 100 TR Index, reveals a more complex picture. Small-cap funds do not deliver consistent outperformance. Their edge depends on entry timing and market cycles.
The conventional narrative treats small-cap allocation as a simple risk-reward trade-off. If you can stomach volatility, the logic goes, small caps will reward you over the long run. The CRISP data challenges that directly.
Most investors look at absolute returns over arbitrary start and end dates. A fund that returned 15% CAGR over 10 years looks like a clear winner. That number depends entirely on the entry point and the exit date. Rolling performance analysis–calculating returns at each month-end over a sliding window–removes that selection bias.
According to the Scorecard, the small-cap index outperformed the large-cap index in only a fraction of the rolling periods. The data does not show a persistent small-cap advantage. Instead, it shows cycles of outperformance followed by periods of significant underperformance.
The small-cap premium–the extra return small stocks theoretically deliver–is not a smooth payout. It is episodic. When small caps rally hard, valuations stretch, and the subsequent correction tends to be sharper than in large caps. The rolling return data captures these reversals.
For an investor holding through a full cycle, the net benefit may be negligible or even negative if the entry was near a peak. The data implies that small-cap funds are not a "set and forget" allocation. They require active management of the allocation size based on market conditions.
The CRISP analysis uses the Nifty Small Cap 250 TRI and Nifty 100 TRI as proxies. The TRI (Total Return Index) includes dividends, so the comparison is clean.
The study computed rolling returns at each month-end over 20 years. The percentage of months where small caps beat large caps varies depending on the rolling window length. For long windows (5 years or more), small caps still did not outperform in a majority of periods. The data suggests that the probability of small-cap outperformance over any 5-year stretch is below 50%.
That is a critical number. If an investor cannot systematically time the entry, the odds of achieving the expected premium are roughly coin-flip–hardly a reward for taking higher volatility.
Small caps exhibit higher drawdowns and longer recovery times. The data shows that during bear markets, small-cap indices fall more than large caps. The subsequent recovery may bring them back to the same level as large caps, the investor experiences deeper interim losses and higher emotional risk–often leading to panic selling at the bottom.
This volatility penalty is not captured in point-to-point CAGR numbers. The rolling return analysis reveals the path dependency of small-cap returns.
Most fund comparison tools show a 5-year or 10-year CAGR. Those numbers are misleading for asset allocation decisions.
A 10-year CAGR can look impressive if the starting point was a market bottom (e.g., March 2009 or March 2020). That same CAGR would look much weaker if the starting point was a peak (e.g., December 2007 or January 2022). Single-point returns are heavily influenced by the luck of the start date.
Rolling returns average out that luck. They show what a typical investor–who enters at a random time–can expect. The CRISP methodology is superior for setting realistic expectations.
As of the data snapshot (April 2026), small caps had recently undergone a strong rally. Rolling returns were likely elevated. The historical pattern suggests that after such stretches, the forward-looking premium shrinks. The data does not predict an immediate reversal, it argues for reducing small-cap exposure when valuations are high.
Retail investors in India have been pouring money into small-cap schemes. The SIP momentum and the direct plan flows are concentrated in this segment. The CRISP data carries a clear warning.
The CRISP data is historical. Forward-looking confirmation or rejection depends on observable signals.
Investors can use the rolling return framework to make concrete decisions.
| Condition | Action |
|---|---|
| Small-cap rolling 5-year return > 20% CAGR | Reduce allocation to underweight |
| Small-cap rolling 5-year return < 10% CAGR | Consider increasing allocation |
| Small-cap vs large-cap rolling 3-year premium > 30% | Trim small-cap exposure |
| Small-cap vs large-cap rolling 3-year premium < -10% | Add to small-cap weight |
These thresholds are illustrative. The principle is to buy when the premium is compressed, not after it has expanded.
Institutional investors and fund managers who outperform over long periods do not hold static allocations. They adjust based on valuation spreads and momentum signals. The rolling return methodology is one tool among many, it provides a clear, data-backed anchor.
This analysis is not just about small-cap funds. It is about the danger of assuming that past returns from a favorable starting point will repeat. The 20-year rolling data strips out the start-date bias and shows how random the small-cap premium actually is.
Investors who rely on fund brochures or SIP calculators that use peak-to-peak returns are making decisions based on flawed data. The CRISP Scorecard is a step toward more honest performance reporting.
For those building a long-term portfolio, the takeaway is clear: small-cap funds belong in a carefully sized slice, not as the core holding. The size of that slice should vary with market conditions. The data gives no support to the idea that higher risk appetite alone justifies a permanent overweight to small caps.
The next step for an investor using this framework is to check the current rolling 5-year returns for the Nifty Small Cap 250 TRI against the Nifty 100 TRI. If the premium is above average, the allocation should be below neutral. If it is compressed, the opportunity may be building.
This is not a forecast of a crash or a rally. It is a probabilistic framework for managing cycle risk. The 20-year data is the evidence. The investor's job is to act on it.
The original stock market analysis by Sanchari Ghosh at Mint provides the full CRISP Scorecard findings. The rolling return methodology should become a standard reference for any mutual fund allocation decision.
Prepared with AlphaScala research tooling and grounded in primary market data: live prices, fundamentals, SEC filings, hedge-fund holdings, and insider activity. Each story is checked against AlphaScala publishing rules before release. Educational coverage, not personalized advice.