Alpha and Beta in PMS: Explained With Real Numbers

In the context of PMS investing, beta measures a portfolio's sensitivity to its benchmark  typically the Nifty 50 or Nifty 500 (or S&P BSE 500) TRI, depending on which benchmark the portfolio manager has selected. A beta of 1 means the PMS strategy is expected to move roughly in line with its benchmark. A beta below 1 suggests the strategy is structurally less volatile than the market; a beta above 1 suggests it amplifies market moves  a beta of 1.2, for instance, implies the strategy is expected to be roughly 20% more volatile than its benchmark. This baseline interpretation  below 1 defensive, equal to 1 neutral, above 1 aggressive  is consistent across virtually every major explainer on the subject, from Zerodha Varsity's mutual fund risk metrics chapter to fund-house knowledge bases.

Alpha measures something different: the return generated after accounting for that risk.

If a PMS strategy returned more than its beta and the benchmark's return would predict, it has positive alpha  evidence of manager skill, stock selection, or timing, rather than just riding a rising market. If it returned less than that risk-adjusted expectation, alpha is negative, even if the headline return looks respectable.

The distinction matters enormously in PMS, where strategies are concentrated (often 15–30 stocks), highly manager-dependent, and priced at 1.5–2.5% management fees plus, frequently, a performance fee. A high raw return with a beta of 1.4 and near-zero alpha simply means the manager took on 40% more market risk than the benchmark and delivered nothing extra for it  information a return figure alone will never tell you.

Beta's roots trace back to the Capital Asset Pricing Model (CAPM), developed by economist William F. Sharpe. Sharpe's core ideas began in his 1961 doctoral dissertation at UCLA  where he was formally supervised by Armen Alchian and informally mentored by Harry Markowitz  and matured into a paper submitted to the Journal of Finance in 1962. After revisions, it was published in 1964 as "Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk" (Journal of Finance, Vol. 19, No. 3). CAPM's central claim was elegantly simple: an asset's expected return equals the risk-free rate plus a premium proportional to its systematic risk  its beta. Sharpe shared the 1990 Nobel Memorial Prize in Economic Sciences with Harry Markowitz and Merton Miller for this body of work.

Alpha, as a formal performance-measurement concept, is generally credited to economist Michael C. Jensen, whose 1968 study on mutual fund performance introduced what is now called Jensen's alpha  the "excess" intercept term left over once a portfolio's CAPM-predicted return is subtracted from its actual return (Kerry Back, Rice University  "Jensen's Alpha"; formula corroborated by Wall Street Prep's Jensen's Measure explainer).

In India, alpha and beta only became genuinely comparable across PMS strategies quite recently. For years, portfolio managers could each pick their own benchmark  or none at all  making cross-strategy alpha comparisons close to meaningless. That changed with a SEBI circular dated December 16, 2022 (Circular No. SEBI/HO/IMD/IMD-PoD-2/P/CIR/2022/172) mandating standardized performance benchmarking and reporting by portfolio managers, effective April 1, 2023, with first reporting due in May 2023. Under this regime, every portfolio manager selects one benchmark per strategy from a shortlist maintained by the Association of Portfolio Managers in India (APMI)  for equity strategies, that shortlist is Nifty 50, S&P BSE 500, or MSEI SX 40, with separate lists for debt, hybrid, and multi-asset strategies . This single regulatory change is why alpha and beta in Indian PMS today are usable, benchmark-anchored numbers rather than manager-chosen marketing claims.

Once beta is known, alpha follows directly from the CAPM (Jensen's alpha) formula:

Alpha = Actual Portfolio Return − [Risk-Free Rate + Beta × (Benchmark Return − Risk-Free Rate)]

This exact structure is confirmed across multiple independent sources, including WallStreetMojo's Alpha Formula guide, which walks through the same six-step process: establish the risk-free rate, establish the benchmark return, find beta, compute the CAPM-expected return, compute actual return, then subtract.

Let's carry our illustrative beta of 0.84 forward into a full-year example:

• Actual PMS return (1 year): 22%
• Nifty 500 TRI benchmark return (1 year): 16%
• Beta: 0.84
• Risk-free rate: ~7% (commonly proxied in India by the 10-year G-Sec yield; see Wright Research's PMS performance metrics guide, which notes investors should "always verify the risk-free rate used")

Step 1  Calculate the CAPM-expected return:

Expected Return = Risk-Free Rate + Beta × (Benchmark Return − Risk-Free Rate) Expected Return = 7% + 0.84 × (16% − 7%) Expected Return = 7% + 0.84 × 9% Expected Return = 7% + 7.56% = 14.56%

Step 2  Subtract the expected return from the actual return:

Alpha = 22% − 14.56% = 7.44%

This illustrative PMS strategy generated 7.44% of alpha  return that cannot be explained by the market risk (beta) it took on. That is meaningfully different from a naive, non-risk-adjusted comparison, which would simply say "the PMS beat its benchmark by 6 percentage points" (22% − 16%). The naive gap and the true CAPM alpha diverge precisely because this strategy ran a lower-than-market beta (0.84)  taking less risk while still outperforming, which is a stronger result than the same 6-point gap achieved at a beta of 1.3 or 1.4 would be.

For comparison, it's worth showing how sensitive this figure is to beta. Using the same actual and benchmark returns but a higher beta of 1.3 (a more aggressive, higher-risk strategy):

Expected Return = 7% + 1.3 × 9% = 7% + 11.7% = 18.7% Alpha = 22% − 18.7% = 3.3%

Same 22% return, same 16% benchmark  but alpha falls from 7.44% to 3.3% purely because more market risk was used to get there. This is exactly why comparing PMS strategies on raw return alone, without their beta, can be misleading.

There is no universal "good" alpha or beta  both depend on strategy mandate, market cap focus, and time horizon. That said, a few broadly accepted reference points recur across PMS-focused research:

The more important qualifier, echoed across PMS-specific research, is consistency over time. A single strong year of alpha may simply reflect a manager being in the right sector at the right moment; genuinely skill-driven alpha shows up across multiple 3–5 year periods and more than one market cycle  a bull run, a correction, and a sideways phase. A PMS strategy with modest but consistently positive alpha across cycles is generally a stronger signal than one with a single spectacular year followed by underperformance.

Illustrative examples are useful for learning the mechanics, but real, disclosed industry data shows what alpha generation actually looks like in the Indian PMS space once SEBI's benchmarking regime is applied at scale.

According to analysis of Association of Portfolio Managers in India (APMI) data covering 543 discretionary equity PMS schemes over a five-year lookback (as of mid-2026), 57.6% of schemes  roughly six in ten  outperformed their selected benchmark, while 42.4% underperformed (3% by more than 10%). Within that outperforming group, a handful of strategies stood out for materially large alpha: Varanium Capital's G2G India Portfolio led with 59.27% alpha on 69.14% returns, Aequitas Investment Consultancy generated 28.29% alpha on 38.16% returns, and Green Lantern Capital delivered 22.57% alpha on 34.86% returns. Notably, only 7.5% of the industry achieved double-digit alpha exceeding 10% over this window  a reminder that large, sustained alpha is the exception, not the norm, even in a market where a majority of strategies beat their benchmark. Because different PMS providers can select different approved benchmarks (Nifty 50 TRI, BSE 500 TRI, or MSEI SX40 TRI), direct alpha comparisons across managers using different benchmarks still carry a caveat.

An earlier, independent data pull from PMSBazaar, covering 254 PMS schemes over a 12-month period (as of April 2022), found that nearly 70% of schemes outperformed the Nifty 50, with the average PMS return at 26.6% versus 18.8% for the Nifty 50 over that window. Top performers that year included Green Portfolio's Super 30 strategy (136% return) and Counter Cyclical Investment's Long Term Value strategy (121% return), while the weakest strategies in the same sample still posted small positive returns rather than losses (Business Standard, citing PMSBazaar data). Independent PMS-ranking platforms such as PMS AIF World's Best PMS in India rankings similarly track since-inception and rolling multi-year CAGR figures against category benchmarks for hundreds of SEBI-registered strategies, reinforcing the same pattern: outperformance is common industry-wide, but it is unevenly distributed, and single-year figures (whether 12-month or 5-year) tell very different stories depending on the window chosen.

The lesson for an investor reading a PMS factsheet: treat any single-period alpha number as one data point, not the full picture, and always check what benchmark and time window it was measured against.

Alpha and beta have moved well beyond the analyst's spreadsheet. PMS research platforms  including Wright Research and PMS AIF World  now publish rolling alpha, Sharpe ratio, and drawdown figures across hundreds of SEBI-registered strategies, sourced directly from the standardized, APMI-mandated disclosures created by the 2022 SEBI circular. At the association level, APMI itself now publishes industry-wide alpha statistics  such as what share of discretionary equity strategies beat their benchmark over five years  a form of aggregated, cross-manager reporting that essentially didn't exist before that regulatory change.

At the same time, AI-powered comparison and advisory platforms have started ingesting this factsheet-level data at scale, turning what used to be a manual, spreadsheet-heavy due-diligence exercise into something closer to instant, side-by-side comparison. More sophisticated managers and analytics platforms are also pushing beyond simple alpha toward factor attribution  separating out how much of a strategy's "alpha" is genuine stock-selection skill versus exposure to known return factors like size, value, momentum, or quality, a distinction several PMS-metrics guides now flag explicitly as important to check before crediting a manager with pure skill.

For an investor evaluating a ₹50-lakh-minimum PMS commitment (the SEBI-mandated minimum for PMS in India), alpha and beta collapse a noisy, multi-year performance history into two directly comparable numbers. Understanding and using them well offers several concrete advantages:

• They separate skill from market movement. Alpha isolates what a manager actually added; beta shows how much market risk was taken to produce it  two very different questions a headline CAGR figure conflates into one number.
• They enable genuine apples-to-apples comparison. With SEBI/APMI-mandated common benchmarks now in place, investors can finally compare strategies that previously used inconsistent, self-selected (or no) benchmarks.
• They match a portfolio to real risk tolerance. Beta indicates how a strategy is likely to behave during a market fall  essential for an investor who cannot stomach large drawdowns, versus one comfortable riding out volatility for higher long-run returns.
• They flag risk-taking that isn't paying off. A high beta paired with low or negative alpha is a warning sign: the manager is taking on extra market risk without generating value for it.
• They support sharper fee conversations. PMS management fees of 1.5–2.5%, often plus a performance fee, are far easier to evaluate once multi-year, benchmark-verified alpha is on the table rather than a marketed return figure alone.
• They discourage return-chasing. Reviewing alpha and beta across 3–5 years and multiple market cycles  rather than one standout year  helps investors avoid mistaking a lucky run for durable manager skill.

No performance metric is free of blind spots, and alpha and beta have several worth knowing before you lean on them too heavily:

Benchmark selection changes the answer.

Because PMS managers themselves choose their benchmark from an APMI-approved shortlist, the same portfolio can show meaningfully different alpha depending on whether it's measured against the Nifty 50, S&P BSE 500, or another eligible index. Direct alpha comparisons across managers using different benchmarks should be treated cautiously.

Both metrics are backward-looking by construction.

Alpha and beta are calculated entirely from historical return data  they describe what already happened, not what will happen next. A strategy with excellent 5-year alpha can still underperform going forward; past risk-adjusted outperformance is informative, not predictive.

Beta is not fixed  it drifts.

As a PMS manager's sector weights, cash levels, or concentration change over time, or as market regimes shift, beta calculated over one period can differ meaningfully from beta calculated over another. A single beta figure on a factsheet is a snapshot, not a permanent property of the strategy.

Alpha persistence is real but uneven.

Industry data shows outperformance is common in aggregate  roughly six in ten discretionary equity PMS strategies have beaten their benchmark over five years, per APMI-based analysis  but large, sustained double-digit alpha is rare (only about 7.5% of the industry, per the same data). Past alpha, in other words, does not reliably predict future alpha, and chasing last year's biggest alpha number is a common and costly investor mistake.

Short lookback windows distort both numbers.

A single unusually strong or weak year  or even a single volatile quarter  can produce a misleadingly high or low alpha and beta reading. Multi-year, multi-cycle data is required before either figure is genuinely meaningful.

Reading alpha and beta correctly  matching benchmarks, checking time windows, and separating one great year from genuine multi-cycle skill  is exactly the kind of work that used to require a dedicated research analyst. This is the gap PMS Sahi Hai, an APMI-registered wealth advisory platform, and its AI engine Nyra are built to close.

Rather than starting from "which PMS products does an advisor happen to sell," PMS Sahi Hai's approach starts from the investor's own risk profile and works outward across the market  tracking and scoring 900+ SEBI-registered PMS and AIF strategies on a consistent, comparable basis. Where a raw factsheet gives you one manager's chosen benchmark and one time window, Nyra's portfolio health check evaluates a strategy (or an investor's existing PMS/AIF holdings) across standardized pillars that go beyond headline returns  including real CAGR measured against the right benchmark, net of costs, and risk factors like maximum drawdown and recovery, rather than the return number alone. That is the same discipline this article has walked through manually: don't just look at the return, look at what risk was taken and what benchmark it's being measured against.

For an investor trying to work out whether a shortlisted PMS strategy's alpha is durable or a one-year outlier, or whether its beta actually fits their own risk tolerance, this kind of standardized, source-cited comparison  built for HNI and NRI investors evaluating ₹50-lakh-plus commitments  turns a genuinely hard comparison problem into a much faster, evidence-based one. You can learn more about how PMS itself works on PMS Sahi Hai's What is PMS" guide, which lays out the ₹50-lakh minimum, taxation differences, and how PMS compares structurally to mutual funds.

The core takeaway is simple: don't evaluate a PMS strategy on its return number alone. Ask what beta it carried to generate that return, and whether its alpha  return above and beyond what that risk level would predict  has held up consistently across at least one full market cycle, ideally three to five years, against a benchmark that's actually appropriate for the strategy's mandate. A strategy with a modest but consistently positive alpha and a beta that matches your own risk appetite is, in most cases, a stronger long-term choice than one chasing the highest single-year return with an unclear risk profile behind it.

Used this way  as a screening discipline rather than a marketing statistic  alpha and beta turn a PMS factsheet from a wall of numbers into a genuinely useful decision tool.

PMS Sahi Hai's AI engine, Nyra, was built to do exactly the kind of benchmark-matched, risk-adjusted comparison this article has walked through  across 900+ SEBI-registered PMS and AIF strategies, scored on a consistent basis rather than each manager's own marketing numbers. If you already hold a PMS or AIF portfolio and want to see how its real, risk-adjusted alpha stacks up, start with a free PMS portfolio health check  and if you're comparing strategies before you invest, explore how PMS Sahi Hai's marketplace works and start with its guide to PMS investing.