SIFPrime Brief
AcademyAdmissions openLive online NISM Series V-D trainingStarts Oct 26, Nov 2 or Nov 9 · 10 days · Daily 7:30–8:30 PM IST · ₹2,999View courseOpen window2 SIF NFOs openFirst close · 14 OctReview NFOsMarket34 SIFs liveDaily NAVs trackedExplore fundsResearchNewSeptember SIF performance reportFinal NAVs and rankingsGet reportPartnersBuild your SIF distribution practiceResearch and supportPartner desk
AcademyAdmissions openLive online NISM Series V-D trainingStarts Oct 26, Nov 2 or Nov 9 · 10 days · Daily 7:30–8:30 PM IST · ₹2,999View courseOpen window2 SIF NFOs openFirst close · 14 OctReview NFOsMarket34 SIFs liveDaily NAVs trackedExplore fundsResearchNewSeptember SIF performance reportFinal NAVs and rankingsGet reportPartnersBuild your SIF distribution practiceResearch and supportPartner desk
Back to module planSIFPrime AcademySeries V-D Chapter Study
CourseAcademy AIPreparation

Series V-D · CH11

Mutual Fund Scheme Performance

Evaluate performance against an appropriate benchmark using total return, risk-adjusted measures, tracking error and prescribed disclosure standards.

Exam weight

5 questions

Practice set

8 questions

Module

Mutual Funds

Workbook

Pages 273-287

Chapter path

Prepare, revise, sample, scan, mock

0%

Login to save progress

Mutual Funds chapters

CH1Investment LandscapeCH2Concept and Role of a Mutual FundCH3Legal Structure of Mutual Funds in IndiaCH4Legal and Regulatory FrameworkCH5Scheme Related InformationCH6Fund Distribution and Channel Management PracticesCH7Net Asset Value, Total Expense Ratio and Pricing of UnitsCH8TaxationCH9Investor ServicesCH10Risk, Return and Performance of FundsCH11Mutual Fund Scheme PerformanceCH12Mutual Fund Scheme Selection

Step 01 · Prepare

Understand the chapter before memorising it

Read the structured notes in order. The headings follow the concepts, calculations and exam traps in the official workbook.

CH11 · 5 questions

Mutual Fund Scheme Performance

NISM Series V-D | 5 marks | Official workbook pages 273-287

What this chapter is about

Evaluate performance against an appropriate benchmark using total return, risk-adjusted measures, tracking error and prescribed disclosure standards.

Core concepts

  • A benchmark must match the scheme's objective, asset allocation and investment strategy. Past returns should not be used to choose the benchmark after the fact.
  • A price return index captures only price movement. A total return index also includes dividends or distributions from index constituents.
  • Absolute return, CAGR and point-to-point return answer different questions. Compare like periods and the same return convention.
  • Sharpe ratio compares excess return with total volatility. Treynor uses beta. Jensen's alpha estimates return beyond that explained by market exposure.
  • Tracking error measures how closely an index fund follows its benchmark. Lower tracking error generally means closer replication.
  • Performance disclosure must follow prescribed periods, benchmark presentation and risk statements. A single top-performing period is not enough.

Exam focus

  • Sharpe ratio is a risk-adjusted performance measure.
  • Past returns are not an appropriate basis for selecting a benchmark.
  • TRI includes dividends as well as capital gains.
  • Tracking error is the key measure for benchmark replication by an index fund.

Quick revision - 60 second scan

  • Choose benchmark from mandate, not outcome.
  • Use TRI for fuller benchmark return.
  • Sharpe uses total risk.
  • Treynor uses beta.
  • Tracking error evaluates passive replication.

Previous chapter

CH10 · Risk, Return and Performance of Funds

Next chapter

CH12 · Mutual Fund Scheme Selection

Need help preparing for NISM V-D?

Build your concepts with 10 live online classes, worked examples and revision. Choose October 26, November 2 or November 9, 2026, daily 7:30–8:30 PM IST. Course fee: ₹2,999.

Ask about the next batchAsk on WhatsAppArrange team training

Send an enquiry before deciding. Your free study resources remain available without registration.

Academy AI