Calculation FAQ

For Indian equities, a Sharpe Ratio above 1.0 is generally acceptable, above 1.5 is good, and above 2.0 is excellent. The Nifty 50 itself historically runs around 0.5–0.9 over full market cycles. Compare your Sharpe against similar large-cap funds (typically 0.8–1.2) for meaningful context. Higher values mean you're earning more return per unit of total risk taken.

Sharpe is calculated using your portfolio's CAGR and volatility over the selected lookback window. A longer period smooths out short-term noise but includes older market conditions. A shorter period reflects recent performance more precisely. These diverge naturally — markets are not constant. Neither is 'correct'; they answer different questions about different time horizons.

Sharpe penalises ALL volatility — both upside and downside swings count equally in the denominator. Sortino only penalises downside volatility (days your portfolio fell below zero). If your portfolio has many large positive days mixed with few small losses, Sortino will be noticeably higher than Sharpe. Sortino is generally considered a fairer measure because investors don't object to positive surprises.

We use the current Indian risk-free rate (approximately 6.99%) as a proxy for the risk-free rate — the return you'd earn in a near-zero-risk instrument like an overnight repo fund or liquid fund. Any return above this is your 'excess return', which is what Sharpe, Treynor, and Jensen's Alpha all measure. If you can earn 6.99% risk-free, you need to earn meaningfully more to justify taking equity risk.

This happens for two mathematical reasons. First, Sortino's denominator only measures 'downside' volatility, which is always smaller than the total volatility used by Sharpe. Second, because we use MAR = 0, Sortino's numerator is your full portfolio return, whereas Sharpe subtracts the 6.99% risk-free rate. Because it divides a larger numerator by a smaller denominator, Sortino naturally produces a higher absolute number for profitable portfolios.

MAR stands for Minimum Acceptable Return. Setting it to 0 means any negative return is treated as a shortfall. Some formulas use the risk-free rate as MAR (only penalising returns below 6.99%). We use MAR = 0 per the original Sortino & Price (1994) paper to keep the measure stable and independent of changing interest rate assumptions.

Yes — above 2.0 is excellent for equity investing. Because we use MAR = 0, it means you're earning 2% of raw annualised return for every 1% of annualised downside deviation. For context, the Nifty 50 historically has a Sortino around 0.8–1.2 over bull markets. Consistently sustaining above 2.0 suggests the portfolio's losses are small and infrequent relative to its absolute returns.

Not necessarily. A high Sortino with low Sharpe means your portfolio has large upside swings (which inflate Sharpe's denominator but not Sortino's) alongside decent gains. This can happen in momentum-driven or concentrated portfolios. The risk is that the same large upside moves can reverse — a low Sharpe warns you that total volatility is high, even if the downside component looks controlled.

It means your portfolio earned 0.8% of annual return for every 1% of maximum drawdown endured. If your max drawdown was -30%, you earned roughly 24% annualised return to compensate. A ratio above 0.5 is generally considered good. Below 0.25 suggests the drawdown wasn't worth the return it produced — the pain wasn't justified.

Calmar uses both CAGR (over the lookback) and Max Drawdown (also over the lookback). If your worst drawdown happened 3+ years ago, it only appears in the 5-year Calmar, making shorter lookbacks look better. Conversely, a recent large drawdown makes short lookbacks worse. This sensitivity is actually useful — it helps identify whether risk is concentrated in a specific period.

Sharpe uses annualised volatility (average daily variation across all days) in the denominator. Calmar uses the worst historical peak-to-trough loss. Calmar is better suited for investors who can tolerate daily noise but are most concerned about large sustained losses — a common profile for long-term equity investors. An investment with low daily volatility but a history of infrequent sharp crashes would look great on Sharpe but poor on Calmar.

Sharpe divides excess return by total risk (standard deviation, both systematic and idiosyncratic). Treynor divides excess return only by systematic risk (beta, market-linked risk). Treynor is more relevant when your portfolio is one component of a broader diversified wealth — because idiosyncratic risk gets diversified away across other holdings. Sharpe is more relevant when this portfolio represents your entire investable wealth.

Treynor requires at least 60 days of aligned portfolio and benchmark returns to compute a reliable beta. If your portfolio is brand new, or you've selected a very short lookback, there may not be enough data. Re-run the analysis with a longer lookback period to enable this metric.

Yes, within the same asset class and benchmark. A higher Treynor means you're earning more excess return per unit of market risk taken. However, comparing Treynor across fundamentally different asset classes (equities vs. bonds vs. commodities) is not meaningful because they have incompatible beta characteristics.

For every 1% your returns deviate from the benchmark's path (tracking error), you earn 0.5% extra active return. An IR of 0.5 is considered 'good' in professional fund management — many actively managed Indian mutual funds struggle to sustain IR above 0.5 over 3+ year periods. Above 1.0 is exceptional.

A negative IR means your portfolio is underperforming the benchmark after accounting for the active risk taken. Your returns diverge from the benchmark (tracking error), but that divergence works against you — you're adding active risk without active return. The simplest diagnostic: identify the stocks pulling you below benchmark performance.

Sharpe measures absolute risk-adjusted performance versus the risk-free rate. IR measures relative performance against the benchmark. You can earn strong absolute returns (great Sharpe) while consistently lagging the Nifty 50 on a risk-adjusted basis (poor IR). This commonly happens in bull markets where beta > 1 portfolios do well in absolute terms but lose on relative IR.

Your portfolio tends to move 1.2× the Nifty 50. If Nifty rises 10%, your portfolio typically rises 12%; if Nifty falls 10%, your portfolio typically falls 12%. Beta above 1 amplifies market moves — beneficial in bull markets, painful in bear markets. A beta of 0.8 means you absorb only 80% of the market's move in either direction.

Not necessarily. Beta captures only systematic (market-linked) risk, not total risk. A portfolio of highly volatile mid-caps with low correlation to Nifty can have a low beta while still being very risky on an absolute basis. Use beta alongside annualised volatility and VaR for a complete risk picture. Low beta primarily means lower Nifty sensitivity, not lower overall volatility.

Yes. Negative beta means the portfolio tends to move opposite to the market — gold, inverse ETFs, and some hedged strategies can exhibit this. For a standard long equity portfolio, negative beta is unusual and typically signals a very short data history or atypical stock selection. In portfolio construction, a small negative-beta allocation can dampen overall portfolio loss during market crashes.

Beta is estimated from the regression of portfolio returns against benchmark returns over the lookback window. Market regimes change — a stock that was highly correlated with Nifty during the 2020 crash may behave more independently in calm markets. Different lookback periods capture different market conditions, so beta estimates naturally shift.

The performance table Alpha is simple outperformance: portfolio return minus benchmark return for a period. Jensen's Alpha is risk-adjusted — it subtracts what your beta 'predicted' you should earn given the market's actual return and the risk-free rate. For example, if the risk-free rate is 6.99% and Nifty returns 16.99% (a 10% excess return), a portfolio with beta 1.5 is expected to earn a 15% excess return (21.99% total). Jensen's Alpha is whatever you earned above that 21.99%.

If your beta is below 1, you weren't expected to keep up with Nifty in a rising market — CAPM predicted a lower return for your risk level. Beating that expectation produces positive Jensen's Alpha even without beating Nifty outright. It means you added value beyond what your market exposure dictated.

Zero Alpha means you earned exactly what the CAPM model predicted based on your beta and the market's return. No manager skill above market exposure is implied. Positive Alpha suggests return beyond market-beta compensation; negative Alpha suggests you underperformed what your level of market risk should have delivered.

Index funds targeting the Nifty 50 aim for tracking error below 0.5% annualised. Actively managed large-cap funds typically run 3–6% TE. A portfolio with significant mid-cap or sector concentration can easily reach 10–15%+ TE. Higher tracking error is not inherently bad — it's the price of active management — but it must be compensated by a strong Information Ratio.

Low TE means you're closely hugging the benchmark. That's ideal if you intend to track an index cheaply. But if you're hand-picking stocks expecting to outperform, very low TE suggests your portfolio isn't meaningfully different from the benchmark — you're paying active management effort for what is effectively an index fund.

We take the daily difference between your portfolio return and the benchmark return for each trading day in the lookback period, compute the standard deviation of those differences, and annualise by multiplying by √252. Days where you outperformed and days where you underperformed both contribute to tracking error — it measures the consistency of deviation, not its direction.

R-squared (shown as a decimal from 0 to 1) measures how much of your portfolio's daily return variation is explained by the benchmark's movements. R² = 0.85 means 85% of your day-to-day swings are driven by Nifty; only 15% comes from your specific stock picks. A lower R² means your portfolio behaves more independently from the market.

It depends on your goal. If you want efficient market exposure at low cost, high R² (above 0.9, index-like) is appropriate. If you're seeking genuine active management with independent alpha, you want lower R² — below 0.7 suggests your picks are meaningfully differentiated from the index. Above 0.9 with high management costs signals an expensive closet index fund.

Beta tells you the magnitude of market sensitivity. R² tells you how reliable that beta estimate is. A portfolio with Beta = 1.2 but R² = 0.3 means the 1.2× relationship is noisy — the portfolio inconsistently tracks Nifty at that multiplier, sometimes more, sometimes completely differently. High R² makes beta a trustworthy and actionable risk measure.

On days when Nifty rises, your portfolio rises 1.2× as much on average. If Nifty gains 1%, you typically gain 1.2%. This is desirable when paired with low downside capture — you want to amplify gains and cushion losses relative to the benchmark.

Aim for Upside Capture > 100% and Downside Capture < 100%. A portfolio with 120% upside capture and 80% downside capture is considered excellent active management — it amplifies Nifty gains and absorbs less of its losses. The ratio (Upside ÷ Downside) being above 1.3 is a robust indicator of consistent quality over time.

It depends on your downside capture. If your downside capture is also low (say 70%), you're running a defensive strategy — you participate less in rallies but also lose less in downturns. That can be a valid risk-conscious approach. The concern arises when upside capture < 100% and downside capture ≥ 100% — meaning you're getting the worst of both worlds.

On days when Nifty falls, your portfolio falls 1.1× as much on average. You're amplifying market losses. Ideally this should be below 100%, meaning you absorb less than the benchmark's downside. Values above 100% suggest the portfolio is concentrated in high-beta stocks or sectors that sell off more sharply than the broader index.

Yes, but only for portfolios containing instruments that move opposite to the benchmark (short positions, inverse ETFs, gold, hedges). For a standard long-only equity portfolio, downside capture is almost always positive. A negative value in a long-only portfolio typically means very short data history with sampling noise.

We filter all trading days where the benchmark had a negative return, then compare the average portfolio return on those days to the average benchmark return on those days. The ratio, expressed as a percentage, is the downside capture. A minimum of 10 benchmark-down days is required for a reliable estimate.

No. VaR 95% means that on 95% of trading days historically, your loss was less than this threshold. On the remaining 5% of days (~13 trading days per year), your actual loss exceeded VaR. It is a statistical threshold, not a guarantee. CVaR (Expected Shortfall) shown separately tells you the average loss on those worst-5% days.

We compute the 5th percentile of your historical daily portfolio returns (in percentage), then multiply by your current portfolio value in rupees. If your worst-5% return days averaged -1.5% and your portfolio is ₹10L, your VaR is approximately ₹15,000. The percentage VaR is a historical observation; the rupee amount scales it to your actual position size.

Stock returns are not normally distributed — they have 'fat tails,' meaning extreme moves happen more often than a bell curve predicts. Indian equities especially can have sharp overnight gaps due to budget announcements, global events, or corporate news. Historical VaR uses actual observed returns without any distributional assumption, making it more accurate and conservative in practice.

VaR is highly sensitive to whether extreme crash events fall within the lookback window. A 5-year lookback may include the 2020 COVID crash; a 2-year lookback may not. Including a crash inflates VaR significantly — which is actually more conservative and appropriate for risk management purposes. Shorter lookback VaR can give a false sense of security if calm recent markets are masking historical volatility.

VaR 99% captures the worst 1% of days (roughly 2–3 days per year) rather than the worst 5% (~13 days per year). It's a more extreme tail measure and will always be a larger number than VaR 95% for the same portfolio. Use VaR 95% for day-to-day risk management thinking; use VaR 99% for stress scenario and regulatory capital planning.

The 'worst 1 in 100 day' loss is by definition extreme and uncommon. Put it in context: a VaR 99% of ₹50,000 on a ₹10L portfolio (5%) means your portfolio historically fell more than 5% in a single day roughly 2–3 times per year. For equity portfolios, this is within normal range during volatile periods. What matters is whether you can absorb such losses without being forced to sell.

The two metrics serve different audiences and decisions. VaR 95% is widely used by retail investors and fund managers for day-to-day risk monitoring. VaR 99% is more commonly used in institutional risk management, regulatory frameworks (like RBI stress tests for banks), and scenario analysis. Showing both gives a fuller picture of the tail risk profile.

CVaR (Conditional Value at Risk, also called Expected Shortfall) is the average loss on your worst 5% of days — the mean of all observations that exceeded VaR. VaR says 'you won't lose more than X on 95% of days.' CVaR says 'on the 5% of days when losses exceed VaR, you typically lose Y.' CVaR captures the severity of tail losses, not just their threshold.

Yes, always. CVaR is the average of the observations that already exceeded VaR. If VaR 95% is ₹15,000, CVaR will be higher — perhaps ₹22,000 — meaning the average loss on those bad days was ₹22,000, not just the minimum cutoff of ₹15,000. The gap between CVaR and VaR reflects how fat-tailed your return distribution is.

VaR has a blind spot: it tells you the threshold but nothing about what happens beyond it. Two portfolios can have identical VaR but vastly different CVaR — one might lose 1.2× VaR on bad days, the other 3× VaR. CVaR is also 'coherent' in the mathematical sense (it satisfies properties like subadditivity), making it more reliable for portfolio risk aggregation and regulatory use.

Not necessarily. Max Drawdown measures the largest peak-to-trough decline during the analysis period — but the portfolio may have fully recovered since. It's the worst paper loss experienced, not a realised loss unless you sold at the trough. However, it is a realistic worst-case benchmark for planning: could you have stayed invested through that decline without panic-selling?

This happens when your portfolio's worst historical drawdown occurred within the last 2 years, so it is captured by all lookback windows. It's actually informative — the worst period is recent, not buried in older history. To see different values across lookbacks, the worst drawdown must predate your shorter lookback window (e.g., occurred 3+ years ago).

Use it as a psychological stress test before investing. Ask yourself: 'Can I stay invested through a -40% portfolio decline without selling?' If not, a portfolio with -40% historical MDD may not match your actual risk tolerance — even if the long-term CAGR looks attractive. The Calmar Ratio (CAGR ÷ |Max Drawdown|) helps evaluate whether the historical drawdown was 'worth' the return it generated.

Max Drawdown is the worst historical decline over the entire analysis period — a backward-looking record. Current Drawdown shows how far your portfolio currently sits below its most recent peak — the drawdown you're experiencing right now, if any. If your portfolio is at an all-time high, current drawdown is 0%. Current drawdown updates with every new data point.

For Indian large-cap equity portfolios, 15–22% annualised volatility is typical. The Nifty 50 itself runs around 14–18% over most market cycles. Mid-cap or concentrated portfolios can easily exceed 25–30%. Higher volatility isn't inherently bad — what matters is whether you're compensated for it. Check your Sharpe Ratio to see if the returns justify the volatility.

We take the standard deviation of your portfolio's daily returns over the lookback period, then multiply by √252 (the square root of trading days in a year) to annualise. The √252 factor converts daily standard deviation to an annual figure under the assumption that returns are approximately independent across days.

In this context, they're the same thing — 'volatility' is the finance term for standard deviation of returns. Annualised volatility is simply daily standard deviation × √252. When analysts describe a stock as 'volatile,' they typically mean its return standard deviation is high relative to its peers or the broader market.

Introduced by Peter Martin in 1987, the Ulcer Index measures the depth and duration of drawdowns throughout the analysis period. It's computed as the RMS (root mean square) of all daily drawdown values. It's named 'Ulcer' because sustained periods below a portfolio's peak cause investor stress — and this metric captures both how deep and how long the portfolio stayed underwater, not just the worst single moment.

Max Drawdown captures the single worst point. Ulcer Index captures the full drawdown path — a portfolio that fell 20% and recovered in a week has a much lower Ulcer than one that fell 15% and stayed below its peak for a year. Ulcer penalises prolonged periods in the red more heavily, which aligns better with real investor psychological stress.

Lower is better. For a Nifty 50 tracking portfolio over a 5-year window that includes 2020, expect Ulcer around 8–12%. A well-managed diversified active portfolio might sit at 5–8%. Values above 20% suggest the portfolio spent significant time substantially below its peak — a red flag for investor experience even if the eventual returns look acceptable.

No. Expected Return is a statistical forward-looking estimate — the mean of your portfolio's historical log-returns compounded to an annual rate over the lookback period. It represents what the model estimates you might earn annually going forward, assuming historical patterns hold. Actual past returns (CAGR) are shown separately and are backward-looking facts, not estimates.

Expected Return averages across your entire lookback (e.g., 3 years of daily returns), while 1-Year Return only reflects the last 12 months. If last year was unusually strong or weak, the 1-year figure will diverge significantly. Expected Return is a longer-run baseline, more stable but potentially less reflective of recent momentum.

We use CAPM (Capital Asset Pricing Model) for stocks with fewer than 252 trading days of price data. We estimate their beta against Nifty 50 and compute: Expected Return = Risk-Free Rate + Beta × (Nifty Return − Risk-Free Rate). This prevents very new stocks from distorting the portfolio expected return with a thin, potentially unrepresentative data sample.

It is a simple cumulative return — (portfolio value today) ÷ (portfolio value 252 trading days ago) − 1. It is NOT separately annualised for periods shorter than a year. Because the window is approximately one year, the cumulative and annualised values are effectively equivalent. If fewer than 252 trading days of data are available, the actual available days are used.

1-Year Return is a single-period simple return. CAGR (3-Year) is annualised — it takes the cumulative return over 3 years and expresses it as the equivalent constant annual rate. For a single year, cumulative and CAGR are identical. Over multiple years, CAGR compresses the full multi-year result into a per-year rate, which is more comparable across different holding periods.

The 1-year horizon is the most commonly used performance evaluation period for individual investors and market commentators. It aligns with tax year reporting and annual review cycles. CAGR over 3+ years is a better indicator of consistent performance, but 1-year gives the clearest picture of recent momentum and is the easiest to verify against headline news.

CAGR (Compound Annual Growth Rate) is the constant annual rate that would take your portfolio from its 3-year-ago value to its current value through compounding. We use 3 years (approximately 756 trading days) because it's the industry-standard minimum for evaluating portfolio skill with some statistical significance. 1-year returns are too noisy; 5-year windows may include market regimes no longer relevant to the current portfolio.

We use the maximum available history and annualise it. If your portfolio has 18 months of data, the calculation uses those 18 months: CAGR = (1 + cumulative_return)^(252 ÷ actual_days) − 1. The metric label still says '3Y' but the step-by-step trace shows the actual number of days used, so you can see exactly what period was analysed.

CAGR is the historical endpoint result — it captures the actual gain from 3 years ago to today, including any strong recent run-up. Expected Return is a smoothed mean of daily returns across all 3 years. If the portfolio surged sharply in recent months, CAGR captures that final value, while Expected Return averages over the whole period. Both are valid; CAGR is verifiable history, Expected Return is a long-run model estimate.