📈 Mutual Fund Analysis on Value Research Online

Three AI Templates, One Decision Tree — Which to Use and When

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Published: April 2, 2026  |  15 min read  |  Introduction & Decision Guide

📚 Multimedia Learning Hub

Overview, knowledge test, and complete learning path for the VRO MF Analysis feature

What You Will Learn

This article introduces the Finmagine AI Advisor v2.14.0 mutual fund analysis feature — the first capability outside Indian and US stocks. A panel appears automatically on every Value Research Online fund page, offering three analysis templates. This guide covers how the panel works, what each template produces, and — most importantly — which one to reach for in each situation.

Topics covered:

  • How the panel activates: No setup, no click — it appears within 4 seconds of opening any VRO fund page
  • What data it reads: Fund metadata, all 8 trailing return periods, top 10 holdings, asset allocation
  • Deep Analysis: 7-dimension fund audit ending in a SEBI suitability verdict
  • Active vs Index: A cost-benefit evaluation — net alpha after fees vs the best passive alternative
  • Portfolio Fit: A portfolio construction assessment using your own context
  • The decision tree: Which template to open for any given question
  • AI platform guidance: Which AI to paste the prompt into for best results
Which platforms does the Finmagine AI Advisor work on as of v2.14.0?
Three platforms: Screener.in (11 templates for Indian stocks), stockanalysis.com (4 templates for US stocks), and Value Research Online (3 templates for mutual funds).
Do you need to click anything on VRO to activate the Finmagine panel?
No. The panel appears automatically within 3–4 seconds of opening any VRO fund detail page (/funds/{id}/{slug}/). No setup, no button click, no extension popup needed.
What are the three MF analysis templates and their one-line purposes?
Deep Analysis: 7-dimension fund audit with SEBI suitability verdict. Active vs Index: Does alpha justify expense ratio? Portfolio Fit: Does this fund belong in MY portfolio?
What data does the Finmagine panel read from a VRO page?
Fund name, AMC, category, benchmark, fund manager, inception date, VR rating, expense ratio, AUM, risk level, 8 trailing return periods (1M to 10Y) with benchmark and category comparisons, top 10 holdings with weights, and asset allocation breakdown.
Which template should you use if you want a comprehensive, unbiased evaluation of a fund before investing?
Deep Analysis. It covers all 7 dimensions — benchmark integrity, alpha consistency, expense drag, AUM suitability, portfolio quality, return consistency, and SEBI suitability — and delivers a verdict of Suitable / Conditionally Suitable / Not Suitable.
Which template is designed specifically to answer the question "Should I invest in an active fund or just buy an index?"
Active vs Index. It computes net alpha after fees, checks whether the benchmark is fair (TRI vs PRI), identifies the best passive alternative, and delivers a direct Choose Active or Choose Index verdict.
What makes Portfolio Fit different from the other two templates?
Portfolio Fit is not a fund quality rating. It evaluates whether THIS specific fund belongs in YOUR specific portfolio — based on your existing holdings, investment horizon, risk tolerance, and financial goals. It requires you to fill in a context box first.
What is the recommended AI for Deep Analysis and Portfolio Fit?
Both work best with Claude or ChatGPT. For Active vs Index, Claude is specifically recommended for its structured reasoning on fee justification and benchmark comparison.
What is the TRI vs PRI benchmark trap?
A fund using a Price Return Index (PRI) as its benchmark appears to outperform because PRI excludes dividends. The correct comparison is against the Total Return Index (TRI), which includes dividends reinvested. SEBI mandated TRI benchmarks from 2018. The Active vs Index template flags this discrepancy.
Why does the panel wait 3–4 seconds before loading data?
VRO lazy-loads certain content — especially the Returns tab — only after the page fully renders. The panel waits to ensure all data is present, then automates a click on the Returns tab and polls until the table populates.
Which template is fastest to use — no input required from the user?
Both Deep Analysis and Active vs Index require no user input — the panel reads all data from the page. Portfolio Fit requires the user to fill in a context box describing their portfolio and goals.
Does the Finmagine AI Advisor store or transmit your personal data?
No. Prompts are generated entirely in your browser. No user identity, portfolio details, or browsing history is collected or transmitted.

What Is the VRO MF Analysis Feature?

Finmagine AI Advisor v2.14.0 adds mutual fund analysis on Value Research Online — the extension's first capability outside stocks. Open any fund detail page on valueresearchonline.com and a Finmagine panel appears within 3–4 seconds, embedded directly on the page. No setup. No button. No configuration.

The panel reads the fund's public data — trailing returns across 8 periods, benchmark comparison, category rank, expense ratio, AUM, fund manager, top 10 holdings, and asset allocation — and assembles one of three precision-structured prompts. You select your template, copy the prompt, and paste it into your AI of choice.

📌 The core idea: Value Research Online has all the data. AI has the reasoning capability. The Finmagine panel bridges the two — so instead of reading 20 data points and trying to synthesise them yourself, the AI does the synthesis and delivers a structured verdict.

How to Access It

  1. Install Finmagine AI Advisor from the Chrome Web Store (v2.14.0 or later)
  2. Open any fund page on Value Research Online — for example, valueresearchonline.com/funds/19701/ppfas-flexi-cap-fund/
  3. Wait 3–4 seconds. A panel labelled ✨ MF Analysis appears in the page, above the fund's tab bar
  4. Click one of the three template buttons to generate your prompt
  5. Click Copy Prompt, then paste into your AI platform
💡 Pro tip: The panel works on any VRO fund detail page — equity funds, debt funds, hybrid funds, index funds, ETFs. If you land on the VRO homepage, fund screener, or category pages, the panel does not appear. Navigate to an individual fund page to activate it.

What Data the Panel Reads

Data PointUsed In
Fund name, AMC, categoryAll templates
Benchmark, fund managerDeep Analysis, Active vs Index
Inception date, exit loadDeep Analysis
VR rating, risk levelDeep Analysis
Expense ratio (ER)All templates
AUM (₹ Cr)Deep Analysis, Active vs Index
Trailing returns — 8 periods (1M to 10Y): fund, benchmark, category, rankAll templates
Top 10 holdings + weightsDeep Analysis, Portfolio Fit
Asset allocation % and market cap breakdown %Deep Analysis, Portfolio Fit

The Three Templates

🔬 Deep Analysis Best with Claude or ChatGPT

What it answers: "Is this a good fund, and is it suitable for me as a general investor?"

What it produces: A 7-dimension fund audit ending in one of three SEBI-aligned verdicts: Suitable, Conditionally Suitable, or Not Suitable.

The 7 dimensions:

  1. Benchmark Mandate Integrity — Is the fund actually investing where it says? Does it hug an easy-to-beat PRI benchmark instead of the correct TRI benchmark?
  2. Alpha Consistency and Decay — Does outperformance hold across 1Y, 3Y, 5Y, 7Y, 10Y? Or does it fade as the fund grows?
  3. Expense Ratio Competitiveness — What is the 10-year compounded cost drag at the fund's actual return? Is the ER justified vs peers?
  4. AUM Suitability — Is the fund too large for its strategy to work? (Especially relevant for mid/small cap mandates.)
  5. Portfolio Construction Quality — Are the top 10 holdings coherent with the mandate? Is concentration appropriate?
  6. Return Consistency vs Category — Does the fund consistently beat the category average, or does it alternate between top and bottom quartile?
  7. SEBI-Compliant Suitability Assessment — A structured verdict based on all dimensions above.

Best used when: You are evaluating a fund from scratch, doing your first due diligence before investing, or reviewing a fund in your portfolio annually.

⚖️ Active vs Index Best with Claude

What it answers: "Does this fund's historical alpha justify paying its expense ratio, or should I just buy an index fund?"

What it produces: A focused cost-benefit evaluation ending in a direct verdict: Choose Active or Choose Index — with specific conditions attached.

Key analyses performed:

  • Net alpha after fees — True excess return over the correct benchmark, after subtracting the expense ratio
  • Benchmark legitimacy check — Is the fund comparing itself to the right index? TRI or PRI? Correct composition?
  • Best passive alternative — Which specific index fund or ETF is the fairest comparison?
  • Alpha sustainability — Has the net alpha been consistent, or was it a one-period phenomenon?

Best used when: You are specifically weighing an active fund against its equivalent index fund — especially in large cap or multi-cap categories where passive alternatives are widely available and often cheaper.

🎯 Portfolio Fit Best with Claude or ChatGPT

What it answers: "Does this fund belong in MY portfolio, given what I already own and what I am trying to achieve?"

What it produces: A personalised portfolio construction assessment — not a standalone fund quality rating — ending in one of three verdicts: Strong Fit, Conditional Fit, or Poor Fit, plus a single actionable sentence.

Requires your input: Before generating, you fill in a context box describing your existing portfolio, investment horizon, risk tolerance, and goals. The AI uses this context to assess:

  • Mandate fit — Does the fund's category and style match a gap in your portfolio?
  • Overlap — Do your existing funds already own the same top 10 stocks?
  • Goal-mandate alignment — Does this fund's typical time horizon match yours?
  • SIP vs lump sum guidance — Given current valuations and your horizon, which mode makes more sense?
  • Position sizing — What allocation percentage is appropriate given your risk profile?

Best used when: You are already considering a fund and want to understand how it fits with what you already own — especially before adding a new fund category or consolidating an existing one.

Which Template to Use: The Decision Tree

The three templates answer different questions. Use this decision tree to pick the right one for your situation:

🌳 MF Template Decision Tree

Do you have a specific portfolio and want to know if THIS fund fits into it?
→ Use Portfolio Fit. Fill in your portfolio context and generate.
Are you specifically asking "should I buy an active fund or just go with an index fund?"
→ Use Active vs Index. Best when the fund is in a category with good passive alternatives (large cap, Nifty 50, Nifty Next 50).
Do you want a full, unbiased evaluation of the fund — covering quality, cost, mandate, and suitability — before making any decision?
→ Use Deep Analysis. This is the default starting point for any fund you are evaluating for the first time.
Not sure where to start?
→ Always start with Deep Analysis. It gives you the complete picture. Then use Active vs Index or Portfolio Fit to drill into a specific decision.

Practical Scenario Examples

Your SituationBest TemplateWhy
First time evaluating PPFAS Flexi Cap FundDeep AnalysisYou need the full picture before forming an opinion
Deciding between HDFC Mid-Cap Opportunities Fund and Nifty Midcap 150 Index FundActive vs IndexClassic active-vs-passive question in a category with a good benchmark
You already own 3 large cap funds and are considering adding a 4thPortfolio FitYou need overlap analysis and mandate fit, not a standalone quality score
Annual portfolio review — checking each fund you holdDeep Analysis for eachReassess quality, alpha decay, and AUM growth since you invested
Comparing two similar funds before choosing oneDeep Analysis on both, then compareRun Deep Analysis on each, paste both outputs into a single AI conversation and ask for comparison
New to mutual funds, evaluating your first fundDeep AnalysisMost thorough, most educational — covers all the dimensions you need to understand
💡 Power user workflow: Run Deep Analysis first to understand the fund's character. If it scores well, run Active vs Index to confirm the fee is justified. If it passes both, run Portfolio Fit with your actual portfolio context to verify it genuinely adds value to what you already own. Three prompts, three AI conversations, one well-researched investment decision.

Which AI to Paste Into

The Finmagine panel generates a structured text prompt. You copy it and paste it into any AI of your choice. For mutual fund analysis, the recommendations are:

TemplateFirst ChoiceSecond ChoiceWhy
Deep AnalysisClaudeChatGPTComplex multi-dimensional reasoning; Claude's structured output is particularly strong for verdicts with conditions
Active vs IndexClaudeChatGPTRequires rigorous fee arithmetic and benchmark logic; Claude handles the net alpha math more reliably
Portfolio FitClaude or ChatGPTBoth handle personalised context well; choose whichever you use regularly
⚠️ Note on Gemini Deep Research: Gemini's Deep Research mode excels at stock analysis where it reads BSE/NSE PDFs. For mutual fund templates, it is not the recommended choice — the prompts are data-rich and structured, not document-retrieval tasks. Use Claude or ChatGPT for MF templates.

What to Expect from the AI Output

A well-structured AI response to any of the three templates should include:

If your AI response reads like a brochure ("this is a well-managed fund with a strong track record"), push back with: "Give me a verdict with specific numbers and conditions."

Deeper Reading: The Full Tutorial Series

This article is your entry point. The full series covers each template in detail:

Start Your Mutual Fund Analysis with AI

Install Finmagine AI Advisor, visit any mutual fund page on Value Research Online, and choose your template. Deep Analysis, Active vs Index, and Portfolio Fit are available from day one.

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