Portfolio Management Services (PMS) have positioned themselves as the sophisticated cousin of mutual funds. They offer bespoke, high-conviction equity strategies for affluent investors, with a minimum ticket size of ₹50 lakh. With assets under management (AUM) of ₹8.45 lakh crore (as of January 2026, per SEBI data) and over 500 active strategies, PMS equity approaches promise alpha through active stock selection across market caps.

Yet, performance narratives often hinge on a critical but overlooked detail: The benchmark. PMS houses routinely cite outperformance against APMI/SEBI-suggested broad indices such as the Nifty 50 TRI, S&P BSE 500 TRI and MSEI SX 40 (MSCI India 40 equivalent). However, when evaluated against relevant, style-specific benchmarks aligned with the PMS’ actual mandate (for example, Nifty 100 TRI for large-cap strategies or Nifty Midcap 150 TRI for mid-cap strategies), the alpha story changes meaningfully. TRI refers to a total return index, which includes dividends. Here is the lowdown.

Regulatory pivot

Indian mutual funds operate under a strict SEBI-led benchmarking framework. Here, each MF scheme is mapped to a specific index reflecting its investment universe, market-cap orientation and style. This ensures transparent and consistent performance evaluation. PMSes, by contrast, operate with greater flexibility and relatively fewer disclosure requirements. Until 2022, PMS providers often selected benchmarks. These were typically broader and less volatile indices, which could make relative performance appear stronger.

Effective April 1, 2023, SEBI, through the Association of Portfolio Managers in India (APMI), mandated the tagging of each investment approach to one of four broad strategies: Equity, Debt, Hybrid and Multi-Asset. The regulator also limited APMI to prescribing a maximum of three benchmarks per strategy. For equity, these are the Nifty 50 TRI, S&P BSE 500 TRI and MSEI SX 40. Secondary benchmarks were made optional in October 2024, but primary performance reporting continues to rely largely on these broad indices.

However, PMS strategies themselves are still defined by self-declared categories such as large-cap, mid-cap, small-cap, Multi Cap & Flexi Cap, and hybrid variants. Accordingly, we conducted an analysis to evaluate outperformance both against APMI-suggested indices and against category-relevant common benchmarks aligned with each strategy’s actual investment universe.

Analysis

This study draws on performance data from PMSBazaar, an independent PMS aggregator that tracks self-categorised equity strategies. We considered six major categories: Large-cap, mid-cap, small-cap, multi-cap and flexi-cap, mid- & large-cap, and small- & mid-cap. The dataset spans one-, three-, five-, and 10-year periods as of March 31, 2026, and includes a total of 403 equity strategies.

We analysed the data as follows. First, we calculated the percentage of strategies outperforming their respective APMI-suggested benchmarks. Second, we measured the percentage of strategies outperforming a common category-relevant benchmark. These category-aligned benchmarks are not prescribed by regulation, but were used here to enable a like-for-like comparison across PMS strategies within each segment.

For example, in the large-cap category, we considered 30 strategies. Of these, 26 are benchmarked against the Nifty 50 TRI and four against the Nifty 500 TRI. Accordingly, outperformance under the APMI framework was computed relative to each strategy’s own benchmark. Separately, to enable a like-for-like comparison, we used the Nifty 100 TRI as a common benchmark for this category and calculated outperformance against it.

Findings

Large-cap: With 30 strategies, the large-cap category shows the smallest divergence between the two benchmarks. Against relevant benchmarks, 43 per cent of strategies outperformed over one year, compared with 47 per cent under the APMI benchmark, a modest 4 percentage point gap. Over five and 10 years, the divergence disappears. This convergence is logical: The APMI-suggested Nifty 50 TRI is itself a large-cap index, making it a reasonable proxy. The BSE 500 is also heavily skewed towards large-caps.

However, the broader picture is less encouraging. Only 33-43 per cent of strategies outperform their benchmarks, implying that a majority underperform even passive index funds.

Mid-cap: This category presents one of the starkest contrasts. Against relevant benchmarks, only 14 per cent outperform over one year, and virtually none over three and five years. In contrast, when measured against APMI benchmarks, outperformance rises to 29 per cent, 47 per cent, 57 per cent and 33 per cent across one-, three-, five-, and 10-year periods, respectively.

This gap suggests benchmark choice materially influences the appearance of outperformance. Mid-cap strategies seem to fare better when compared with broader indices. Against more representative mid-cap benchmarks, their alpha generation looks less compelling.

Small-cap: Small-cap strategies show a mixed picture. Against relevant benchmarks, outperformance is moderate and relatively consistent at around one-third across most periods. However, against APMI benchmarks, outperformance rises sharply, reaching as high as 75 per cent over five years. Notably, three small-cap strategies use the Nifty 50 as their benchmark.

Multi-cap and flexi-cap: Of the 268 strategies, 69 follow the Nifty 50 TRI, while 199 track the BSE 500 TRI. For the relevant benchmark comparison, the Nifty 500 TRI was used. The divergence is minimal, as the BSE 500 and Nifty 500 are broadly similar in return characteristics. Moreover, broad indices such as the BSE 500 TRI are appropriate benchmarks for multi-cap and flexi-cap strategies.

Mid- & large-cap: These strategies show a noticeable drop in outperformance when evaluated against the relevant benchmark, the Nifty LargeMidcap 250 index. While 62 per cent outperform APMI benchmarks over one year, only 52 per cent do so against the relevant benchmark. The divergence widens over three, five and 10 years, highlighting the issue of benchmark mismatch.

Small- & mid-cap: This category also exhibits a pronounced discrepancy. Only 27-39 per cent of strategies outperform relevant benchmarks (Nifty MidSmall 400 TRI) across timeframes, compared with 38-74 per cent under APMI benchmarks.

Pros and cons

The APMI framework standardises reporting, improves comparability across PMS providers and aligns disclosures with regulatory expectations. However, broad indices may not always reflect the true market-cap exposure and volatility profile of specific PMS strategies, especially in mid- and small-cap categories. That can make outperformance appear stronger than it would against more category-aligned benchmarks.

This makes comparison against relevant benchmarks important. Category-aligned benchmarks can offer a more accurate read of stock-picking ability, allow more meaningful peer comparisons and limit the scope for favourable benchmark selection.

Takeaways

Across all six categories, relevant benchmarks show materially lower outperformance ratios than APMI-suggested indices. The mid-cap category is the clearest example: Outperformance falls to 0 over three and five years against relevant benchmarks, versus 47 per cent and 57 per cent against APMI benchmarks. This shows how benchmark choice can materially influence the impression of outperformance.

A broad benchmark such as the S&P BSE 500 can also accommodate portfolios that shift meaningfully across market-cap buckets during volatile periods. The issue arises when strategies labelled as small-cap or mid-cap funds carry meaningful exposure outside their stated segment. In such cases, the stated category and the actual portfolio construction may not be fully aligned.

A key structural gap in the PMS ecosystem is the absence of a SPIVA-like framework. Globally-recognised frameworks such as SPIVA (S&P Indices Versus Active) regularly publish data on how active funds perform against their benchmarks across time horizons. PMS investors lack a comparable standardised evaluation.

A category-wise, benchmark-aligned performance framework for PMS could improve transparency and investor understanding. It would also help distinguish genuine alpha generation from outperformance that looks stronger under less representative benchmarks.

Published on April 18, 2026