Reading your own returns honestly | the right benchmark, attribution analysis, how long you need to evaluate a strategy, and why most performance measurement misleads more than it informs.
TK
Tanmay Kurtkoti
Founder & CEO, RupeeCase
⏱ 14 min read⟳ Updated 6 May 2026◆ Advanced
Your momentum strategy returned 32% last year. Was that good? It depends entirely on what the benchmark returned, how much risk you took, and whether the performance was driven by your factor signal or just by the market going up.
Performance measurement is the process of answering those questions honestly. It's harder than it sounds | the financial industry has a long history of making performance look better than it is, through selective benchmarks, cherry-picked time periods, and gross-of-cost returns. This module teaches you to cut through that and evaluate performance clearly.
Why most "alpha" in India is a benchmarking illusion
500
Stocks in Nifty 500
correct factor benchmark
60
Months to evaluate
5Y minimum, 1 cycle
0.8
Excellent IR bar
above this is rare
60
Healthy win rate pct
not 90, not 50
NSE IndicesSEBI MF RegulationsAMFI Benchmarks
The right benchmark
Every return number is meaningless without a benchmark. The benchmark should represent: what would you have earned if you hadn't run this strategy?
For systematic factor strategies on Nifty 500, the correct benchmark is the Nifty 500 TRI (Total Return Index) | not the Nifty 50, not the price index. Reasons:
TRI vs Price Index: The price index ignores dividends. The TRI includes dividends reinvested. For a fair comparison, your strategy's returns should also include dividends | and so should the benchmark. Using the TRI as benchmark prevents a spurious "alpha" from simply holding dividend-paying stocks.
Nifty 500 not Nifty 50: Your strategy selects from Nifty 500, which includes mid and small-cap stocks that systematically outperform large-caps over long periods. Comparing a Nifty 500 factor strategy to the Nifty 50 would show artificial alpha | you'd be taking more risk (mid-cap exposure) and comparing to a lower-risk benchmark.
The most fundamental measure | how much more did you earn than the benchmark?
Excess Return = Strategy CAGR − Benchmark CAGR
Simple and honest. A strategy that earned 28% when Nifty 500 TRI earned 20% has 8% excess return. Use CAGR over 5+ years for meaningful measurement.
2. Information Ratio (IR)
How consistently did you generate excess return? High IR = alpha was consistent, not concentrated in a few lucky years.
IR = Annualised Excess Return / Tracking Error
Tracking error = standard deviation of monthly excess returns. IR above 0.5 is good. Above 0.8 is excellent. A strategy with 8% excess return but 20% tracking error (IR = 0.4) is much riskier than one with 6% excess return and 6% tracking error (IR = 1.0).
3. Maximum Drawdown vs Benchmark Drawdown
Did your strategy fall further than the benchmark in bad periods?
Relative Drawdown = Strategy MDD − Benchmark MDD
If Nifty 500 fell 35% in 2020 and your strategy fell 45%, you had a relative drawdown of -10%. This matters | it means you took more risk than the market and got punished for it in downturns. Ideally, your strategy's MDD should be close to or below the benchmark MDD.
4. Rolling Alpha (3-year windows)
Did alpha persist, or was it concentrated in a specific period?
If rolling alpha is positive for 80% of all 3-year windows in the backtest, the strategy is consistently adding value. If alpha is only positive in 2 to 3 exceptional years, the "CAGR alpha" may be misleading.
5. Win Rate (monthly / annual)
What fraction of months / years did the strategy beat the benchmark?
Win Rate = Months outperforming / Total months
Good factor strategies typically beat the benchmark in 55 to 65% of months | not 90%. Expecting the strategy to beat every month sets unrealistic expectations. Understanding the win rate helps you calibrate how often you should expect underperformance and avoid abandoning the strategy at the wrong time.
Return attribution: where does your alpha really come from
Market beta (Nifty 500 TRI)62%
Factor exposure (momentum / value)22%
Stock selection (true alpha)10%
Timing / rebalance effect6%
Illustrative attribution for a typical India large and mid cap factor portfolio. Most of what retail investors call alpha is actually market beta. True stock selection alpha above Nifty 500 TRI is usually below 3 percent net of costs.
Realistic Information Ratios across Indian equity styles
Closet indexer large cap fund
0.18
Average active large cap fund
0.32
Nifty Momentum 150 TRI backtest
0.58
RupeeCase Nifty 10, 10 stocks, live
0.74
Top quartile hedge fund India (PMS)
0.88
Information Ratios above 0.5 are good, above 0.8 is rare. Source: AMFI scheme data, NSE Indices backtests, RupeeCase live NAV series.
How long before you can evaluate a strategy?
This is the most important | and most ignored | question in performance evaluation. Short-period returns are dominated by luck, not skill.
Evaluation period
Confidence in results
Why
1 month
Near zero
Monthly returns are almost pure noise. Even random strategies show 1-month "alpha."
1 year
Very low
1-year results are dominated by market regime. Anything can look good or bad in 12 months.
3 years
Moderate
Starts to show consistent patterns, but still doesn't cover a full market cycle.
5 years
Meaningful
Covers at least one significant correction. Results start to be statistically significant.
10+ years
High
Covers multiple market cycles. Alpha this persistent is likely real, not luck.
The practical implication: Do not change your strategy because of 6-month or 12-month underperformance. The expected win rate for most factor strategies is 55 to 65% of months | meaning you should expect to underperform the benchmark in 35 to 45% of all months. A 6-month stretch of underperformance is completely within normal expectations. Evaluating performance and making strategy decisions only at 3 to 5 year intervals is not laziness | it's statistical discipline.
TK | The performance meeting I will never forget
In 2019 I sat in a PMS review meeting in Mumbai where the fund manager showed a 17 percent CAGR over 3 years and everyone applauded. I asked one question. What was Nifty 500 TRI over the same period. Nobody had checked. It was 14.5 percent. So 2.5 percent of gross alpha, minus 2.5 percent expense ratio, minus taxes. A zero alpha fund dressed up as a star performer. Since then I carry one rule. Before I congratulate any strategy, I pull up Nifty 500 TRI for the exact same window, net of every cost. If it does not beat the index after fees, it is not a strategy. It is a story.
Performance analytics on RupeeCase
Every strategy on RupeeCase shows a rolling 12-month and rolling 36-month alpha chart, monthly win rate vs benchmark, information ratio, and the full performance attribution (how much return came from factor exposure vs stock selection vs market beta). The benchmark is always the Nifty 500 TRI. Net-of-cost returns throughout. Available at invest.rupeecase.com.
Honest performance analytics
Rolling alpha, information ratio, monthly win rate | vs Nifty 500 TRI, always net of costs
The Total Return Index version of Nifty 500 | includes dividends reinvested. The correct benchmark for any strategy selecting from the Nifty 500 universe.
Information Ratio
Annualised excess return divided by tracking error. Measures consistency of alpha generation. Above 0.5 is good; above 0.8 is excellent.
Tracking error
The standard deviation of the difference between strategy returns and benchmark returns. High tracking error means performance diverges significantly from the benchmark in any given period.
Rolling alpha
Alpha calculated over a rolling window (e.g., 36 months), computed at each monthly interval. Shows whether alpha was persistent or concentrated in specific periods.
Win rate
The fraction of periods (months or years) in which the strategy outperformed the benchmark. Good systematic strategies typically win 55 to 65% of months.
TK
A note from the author
Why this matters
You cannot improve what you do not measure correctly. Too many Indian investors judge their portfolios by absolute returns alone, ignoring risk-adjusted metrics, drawdown analysis, and proper benchmarking. After 17 years of building attribution and performance systems, I can tell you that rigorous measurement is what separates serious systematic investors from those who are just guessing with spreadsheets.
Want to put this into practice? RupeeCase is the systematic investing terminal built around everything you're learning here, factor scores, strategy backtests, portfolio construction for Indian markets.
→ Grinold, R. & Kahn, R. (2000). Active Portfolio Management. McGraw-Hill. (Information Ratio and performance measurement)
→ Sharpe, W.F. (1994). The Sharpe Ratio. Journal of Portfolio Management.
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