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'Tripled My Money, Super Bullish On This Sector': Shankar Sharma's Most Blunt Interview

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Key takeaways

Finance

Finance/markets takeaways (from Shankar Sharma interview)

Macro vs market reaction

Even though Middle East/Strait of Hormuz risks seem to be unwinding and crude prices are cooling, Sharma says markets are not reacting much because:

  • The geopolitical issue is not “put to bed”—it can escalate suddenly, leaving lingering risk.
  • The AI/data-center trade hasn’t lost momentum. Since the US remains the main “fountainhead” for AI, global sentiment toward India stays tepid, limiting upside in India’s large caps.

Where the “bull market” is hiding

  • While large caps look tepid, Sharma argues small caps are where the major upward momentum and money flows are.

Valuation framework: how to think about P/E in India

Avoid “low P/E” as a rule (India-specific)

Sharma’s view is blunt:

“Anything which is low P should be avoided like a plague.”

Rationale (India-specific claim): P/E correlates with how many years the company can likely remain in business:

  • 5 P/E ≈ ~5 years of business viability
  • 50 P/E ≈ ~50+ years
  • So a low P/E stock usually deserves to be low.

Compressed valuations are usually bad

  • When valuations are “compressed”, Sharma says it is almost always bad for large parts of India.

Interpretation of “cheapness”

  • Small caps: “cheapness” often reflects inherent growth ahead
  • Large caps: “cheapness” often reflects slower/less reliable earnings growth going forward

How to play cyclicals (step-by-step idea)

Sharma’s cyclical approach:

  1. Buy cyclicals when P/E multiples are high (i.e., profits are compressing less and the market hasn’t fully punished the stock yet)

  2. Exit when P/E multiples are compressed (profits may have improved, but the market pays less per rupee of earnings)

In short: the “good time” is when multiples are expanding, not when they’re at the bottom.

Index/sector rotation thesis (Nifty vs broader indices)

Churn: Nifty 50 vs smaller indices

  • Nifty 50 churn is low, while smaller indices churn is high:
    • In ~2 years, only about 6%–8% of Nifty 50 constituents changed
    • For Nifty 150/500, change/reconstitution is around 45%–50%

Conclusion: The “bench” of newer names is getting used more in smaller/mid segments, while Nifty 50 stays more stagnant.

Why big index constituents may lag

  • Large index entrants often become high market-cap/liquidity names before inclusion, leaving less upside than smaller stocks that can still “triple.”
  • Example cited: Zomato as a name that entered Nifty earlier but, over the discussed period, “delivered nothing” (as characterized in the discussion).

AI/data-center trade: return/risk math and why negative free cash can be fine

The AI trade isn’t expected to disappear

  • Sharma does not expect the AI trade to go away.
  • Nvidia: he cites that Nvidia reported strong numbers and was up ~5% after market, which he reads as supportive.

IRR argument for data-center businesses

  • He cites estimated 5-year IRR ~18%–20% to 21% (depending on assumptions).

Negative free cash / borrowing-cost reasoning

Sharma addresses “negative free cash” concerns by comparing returns vs cost of capital:

  • If you can invest at ~18% IRR
  • while borrowing long-end at roughly ~5% (30-year yield “and change”) plus a 1%–2% premium (for private-company cost),
  • then borrowing cost might be roughly ~6%–7%,
  • which he argues is not terribly wrong versus ~18% returns.

Governance/management pushback rebuttal

  • He emphasizes the “Magnificent 7” promoters/founders:
    • calls them “best managements in the world”
    • attributes 25–30 years of experience (e.g., Amazon mid-90s; Google early 2000s)
  • He argues negative free cash isn’t automatically bad for growth companies.

Implied recommendation: focus capital where AI/data-center capex + the infrastructure ecosystem benefits—especially in small caps.

Cautions/risks he explicitly raised

  • Inventory / raw-material timing risk (small caps):

    • He calls it a “worry 100%.”
    • Concern: companies may have benefited from cheap raw materials while earlier actions pushed prices up, but that benefit may bleed out in Q2.

  • Large-cap “reversion to mean” timing risk:

    • Investors expecting a quick turnaround may need to wait longer.
    • Core reason: India large caps are not the main global demand story, since the US remains central for AI.

Concrete portfolio / stock mentions

Sharma’s personal actions (as stated)

  • He tweeted on March 11 that small caps are the place to be and said he almost tripled capital since then.
  • He still expects the AI/data-center trade to continue “for a while.”

Single-name examples (not necessarily full endorsements)

  • Paytm: he says he bought a bit, calling it an exception despite disliking the overall BFSI space; it has “been doing well.”
  • Corning: mentioned as a prior purchase; he says optical fibers have gone up ~3x in 6–8 months (as stated).

Large Indian names discussed mainly as “no-go” / lagging

  • Reliance (RIL) and HDFC Bank are cited as heavyweights that have lagged for years.

HDFC Bank deep-dive

  • Sharma attributes issues to leadership transition after Aditya Puri left.
  • He believes governance issues are “surfacing.”
  • Claim: banks generally require taking risk to grow, which backfires ~3 years later.

  • Conclusion (his words):

    “Banks in general are a no-go area. HDFC Bank is a no-go area.”

  • He dismisses “2x book”-style valuation as not sufficient.

Sector thesis: “infrastructure tech” linked to electrification + data centers

Definition of “infrastructure tech”

Sharma defines it (in this discussion) as companies enabling:

  • Electrification
  • Power + renewable/thermal buildout supporting the data-center ecosystem
  • Cables and connectivity for data centers

Optical fiber tech cycle (structural capex, not a one-time boom)

He argues optical fiber moved from “commodity-like” toward an AI-driven evolution:

  • multi-core cables
  • hollow core cables
  • intermittent bonded ribbons (IBRs)

Therefore, newer data centers will require upgrades, implying repeated capex and multi-year demand for suppliers.

Primary markets / IPO pricing view

  • Pricing is becoming more rational (less aggressive than the earlier “crazy” IPO period).
  • However, he worries that IPO/QIP/preferential issues siphon liquidity from secondary markets, which can limit secondary performance.
  • If India sees a small uptick in secondary market activity, he expects IPO/QIPs to keep coming.

Explicit instruments/tickers/assets mentioned

Indices

  • Nifty 50
  • Nifty 150
  • Nifty 500

Sectors/themes

  • AI/data centers
  • consumer tech
  • infrastructure tech
  • electrification
  • optical fibers
  • BFSI

Stocks/companies

  • Nvidia
  • HDFC Bank
  • Reliance (RIL)
  • Paytm
  • Zomato
  • Corning
  • Amazon
  • Facebook (Meta)
  • Google (Alphabet)
  • “Magnificent 7 / Mac 7s” (as a group)

Rates/instruments

  • 30-year bond yield context (borrowing benchmark discussed around ~5% and change)

Commodities/geopolitics

  • crude oil (noted as cooling off; no exact price provided)

Numbers & time references pulled from the subtitles

  • AI/data-center IRR (5-year): ~18%–21%
  • Nvidia: up ~5% after market (as stated)
  • Optical fibers (Corning context): ~3x in 6–8 months
  • Macro timing: Q1 described as “better than expected”
  • Index churn:
    • ~6%–8% change in Nifty 50 constituents over ~2 years
    • ~45%–50% change in Nifty 150/500
  • Investment action timing: March 11 tweet; since then almost tripled capital
  • Geopolitics timeline: Middle East/Strait of Hormuz described as not resolved, with repeated “announcement after announcement”

Disclosures / disclaimers

  • No explicit “not financial advice” or regulatory disclaimer appears in the subtitles provided.

Presenters/sources mentioned (end)

  • Shankar Sharma (founder, G Quant Finaxry)
  • Neeraj (interviewer; mentioned by first name only)
  • Alex (another participant; referred to as “Alex here”)

Original video