IndiaSEBI Compliance

Recommendation Performance Tracking for SEBI RAs (2026)

The 2025 SEBI master circular shifted the burden of proof on RA marketing. Past performance now has to be disclosed honestly, in full, with methodology. The Twitter screenshot of a winning trade is no longer enough. The honest disclosure is harder to produce than the screenshot, but it is also far more credible to prospective clients who know how easy the screenshot was to game.

May 28, 2026 ยท 8 min read ยท By Aktai Team

Note: Performance-disclosure rules continue to evolve via SEBI master circular updates. Confirm the current cadence and methodology requirements before publishing.

Track records are awkward because honest ones include losers. Most RAs starting out have a mix: some recommendations that worked, some that did not, a few that did nothing for two years and then doubled. The instinct is to highlight the best. The Advertisement Code and the master circular push the other way: disclose the universe.

The good news is that an honest universe disclosure ages much better than a Twitter highlight reel. Prospects who have been in markets long enough know that no one bats 0.900. A 55% hit rate with thoughtful methodology beats a curated 100% win-rate screenshot in every serious conversation.

The eight fields every performance disclosure should carry

Period covered

A defined window with start and end dates. Disclosed up front, not after the fact.

Universe

Every recommendation issued during the period. Long, short, target-changes, all included.

Return calculation

Entry price (publication date close), exit price (target date close or recommendation close date close), absolute and percentage return.

Hit rate

Number of recommendations where the actual return moved in the recommended direction, divided by total recommendations.

Average return per recommendation

Sum of returns divided by count, with the formula disclosed.

Best and worst

The highest-returning and lowest-returning recommendation in the period, with context.

Benchmark comparison

Return of an appropriate benchmark over the same window for context (Nifty 50, sector index, or universe-matched index).

Open recommendations

Recommendations still live at the end of the period, marked as such, with current return.

Six common mistakes

Showing only winning recommendations

Fix: Include every recommendation issued during the disclosed window. Methodology rules apply uniformly.

Picking a flattering benchmark

Fix: Match the benchmark to the recommendation universe. A large-cap watchlist benchmarks against Nifty 50, not Nifty Smallcap 50.

Not disclosing methodology

Fix: Publish the calculation rule alongside the numbers. The methodology is more important than the numbers.

Backtest passed off as track record

Fix: Backtests are clearly labelled as hypothetical. Live track record is separate. Do not blend the two.

No date stamps on recommendations

Fix: Without timestamps, performance cannot be verified. Reg 25 audit trail solves this if you use a tool that hashes on send.

Performance shown out of context of risk

Fix: A 40% return with a 60% drawdown is a different story. Include drawdown or volatility for honest framing.

The display format that works

Tables beat paragraphs for performance data. A scannable table with one row per recommendation, columns for ticker, recommendation date, recommended action, target horizon, entry price, exit or current price, and return percentage, lets a prospect verify the numbers in seconds. A summary row at the bottom with the period, hit rate, average return, benchmark comparison, and number of open recommendations rounds it out. Two paragraphs of methodology underneath the table cover the rest.

The hosting matters less than the format. A static page on your website is enough. A live, filterable interface is nicer but not required. The SEBI inspector wants to see the disclosure exists and matches your audit trail.

The data flow from research note to performance disclosure

The disclosure is much easier to produce when your research workflow is structured. Each recommendation has a date stamp, a recommended action, a target price or direction, and a horizon when it goes out. The data lives in the audit trail. A quarterly job pulls the audit trail, marks closed recommendations against current price, and calculates the universe statistics. With a good workflow this is a 30-minute exercise per quarter. With an Excel-and-WhatsApp workflow it is a weekend of reconstruction.

How Aktai supports performance tracking

Every research note marked as sent in Aktai is timestamped, hashed, and stored. The note carries the ticker, the recommended action (when explicit), and the recipient. The data needed for a compliant performance disclosure is therefore already structured; producing the quarterly disclosure is an export-and-summarise job. A dedicated public-facing performance scorecard with the eight methodology fields above is on the Tier 1 product roadmap and pairs naturally with the audit trail that already exists.

FAQ

Are SEBI Research Analysts required to publish performance of past recommendations?

Yes. The SEBI master circular on Research Analyst Regulations (June 2025 and subsequent updates) requires RAs to maintain and disclose past recommendation performance to clients and prospects. The intent is to prevent cherry-picked marketing where a few winning trades are shown without the full universe of recommendations.

What time period must I cover in the performance disclosure?

A minimum of one year is standard, with three-year and five-year periods recommended once you have the track record. The disclosure must cover all recommendations issued during the period, not a selected subset. Showing only the last 12 months of an 8-year practice is non-compliant if the older recommendations are also live.

How do I calculate returns when I have hundreds of recommendations?

The standard approach is to calculate the return on each recommendation as price at recommendation date vs price after the recommendation's stated horizon (or current price for open recommendations), then summarise across the universe: hit rate, average return per recommendation, average return on hits, average drawdown on misses. The methodology is disclosed alongside the numbers.

Can I exclude recommendations that turned out badly?

No. Selective exclusion violates the Advertisement Code and the spirit of the master circular. You include every recommendation that was published. If a recommendation was later marked as closed for a documented reason (target hit, stop loss triggered, time horizon expired), that gets disclosed as part of the methodology.

Where do I publish the performance disclosure?

On your website prominently, and accessible from any marketing material that discusses performance. The disclosure is updated at the cadence the master circular requires (typically quarterly). Many RAs also include a one-line summary in their client onboarding pack and a link in the email footer.

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