The website analyses mutual fund performance relative to their benchmarks. It focuses on two main questions:
- How often mutual funds outperform their benchmarks?
- By how much do they outperform their benchmarks?
No. The analysis does not provide investment recommendations. It only evaluates how funds have historically performed relative to their benchmarks.
The website focuses on selected categories of actively managed mutual funds in India: large cap funds, large and mid cap funds, mid cap funds, small cap funds, multi cap funds, flexi cap funds, focused funds, aggressive hybrid funds, value funds, and contra funds. These categories have been selected because they represent some of the major actively managed equity oriented mutual fund segments in India.
Only Direct Growth plans are included.
- The NAV (net asset value) data of mutual fund schemes has been collected from the official website of the Association of Mutual Funds in India (AMFI).
- The benchmark index data has been collected from the official website of NSE Indices.
Benchmark index data is obtained from the official websites of the National Stock Exchange of India (NSE).
Indices can be calculated in two ways:
- Price Return Index (PRI) — reflects only price changes.
- Total Return Index (TRI) — reflects both price changes and dividends.
Mutual funds are required to compare themselves against TRI benchmarks, because TRI reflects the full return investors receive. Therefore, the analysis uses TRI data for all benchmarks.
Performance is evaluated using rolling returns rather than discrete or trailing returns. For each rolling period:
- The fund's return is calculated.
- The benchmark's return is calculated.
- The two returns are compared.
If the fund return exceeds the benchmark return, it is counted as an instance of outperformance.
Rolling returns measure performance across overlapping time windows. For example, a three-year rolling return calculates returns for every possible three-year period in the dataset rather than relying on a single start and end date.
This approach reduces dependence on arbitrary start and end dates and captures performance across many market conditions.
Rolling returns provide a more comprehensive picture because they:
- Reduce date-selection bias.
- Include many more observations.
- Reflect performance across multiple market environments.
Discrete returns depend heavily on the specific start and end dates chosen. Trailing returns depend on a single endpoint. Rolling returns mitigate these limitations.
Yes. Rolling return observations overlap, meaning many periods share the same underlying data. This means the observations are not completely independent.
Despite this limitation, rolling returns are used because they provide far more observations than non-overlapping methods.
Using non-overlapping returns would produce very few observations. Direct plans began in 2013, so between 2013 and 2026:
- Only about four non-overlapping 3-year periods.
- Only about two non-overlapping 5-year periods.
This sample size is too small for meaningful analysis.
The analysis calculates rolling returns for four horizons: 3 years, 5 years, 7 years, and 10 years. These periods reflect medium- and long-term investment horizons.
Rolling returns are calculated using daily data. This allows the analysis to include the maximum number of observations.
Financial markets are closed on weekends and holidays. Instead of explicitly identifying every holiday, the analysis keeps only the dates that appear in both datasets (fund NAV data and benchmark index data).
This ensures that comparisons are made only on common trading days.
For each rolling period, if the fund return exceeds the benchmark return it is counted as outperformance; otherwise, underperformance. The Outperformance Frequency is the percentage of rolling periods where the fund beats its benchmark.
Example: if a fund outperforms in 60 out of 100 periods, the outperformance frequency is 60%.
For each rolling window, the difference between fund return and benchmark return is calculated. This difference is called the spread.
To summarise this across all observations, the median spread is used. The median is preferred because it is less affected by extreme values.
Results are presented across three analysis windows: Since inception, Last 10 years, and Last 5 years. These allow both long-term and recent performance to be examined.
No. Rolling horizons cannot exceed the analysis window. For example, a 10-year rolling return cannot exist inside a 5-year analysis window. Only logically valid combinations are included.
No. The methodology does not attempt to forecast future returns or determine whether past outperformance will persist.
The analysis does not evaluate risk metrics such as volatility, drawdowns, Sharpe ratio, or downside risk.
The analysis compares each fund only with its benchmark, not with other funds in the same category.
The analysis uses NAV data and benchmark indices but does not explicitly model investor taxes, exit loads, or transaction costs.
The methodology does not explicitly address whether funds that were merged or closed are included or excluded.
The page explains how benchmarks are identified but does not discuss how benchmark changes (if any) are handled historically.
Questions, feedback, or suggestions — we'd love to hear from you.