Video summary

How This 15-Stock Basket Strategy Made 20.59% in a Negative Market | Shashank Udupa | FWS

Main summary

Key takeaways

Finance

Finance-focused summary: 15-stock quant basket strategy

Market / macro context & rationale for entry (India)

  • Claimed setup: After a volatile period of roughly 1–2 years, during which many investors saw around 0% to -2% returns, the speaker argues that:

    • Earnings have caught up, and
    • Valuations are no longer as stretched This is framed as a potential “spring reaction”—especially in mid/small caps.

  • Key drivers cited:

    • FPI/FIIs exodus and rotation: Foreign flows moved from India to other markets (speaker references a path such as China/Hong Kong → US → South Korea, then “back in US” framing).
    • External shocks:
      • Trump tariffs (trade-war risk)
      • Iran/geo crisis pushing crude oil to ~ $110
    • Earnings growth evidence: In small & midcaps, median earnings growth ~15% YoY (contrasted with large caps described as “not doing great”).

  • Index performance comparison referenced:

    • Basket: ~ +20.59% one-year return
    • Benchmark: ~ -1.64% (described as “market/index” in the app context)

Stocks / instruments / sectors mentioned

Individual stocks / companies (tickers not given in subtitles)

  • Reliance
  • TCS
  • SBI / SGFC Bank (subtitles say “SGFC Bank”; likely referring to SBI; merger issue mentioned)
  • Cupid (sexual wellness company)
  • Loris (company name mentioned; no ticker given)

Index / ETF / benchmark instruments

  • Nifty 50 (large-cap representation)
  • Nifty 500 (mid + small representation)
  • “Nifty bees” referenced (implying Nifty ETFs; no specific ETF ticker provided)

Other assets / sleeves

  • Real estate
  • Gold
  • US / global investing (no specific ETF/stock named)
  • Liquid fund / cash-equivalent used during “cash call” periods

Strategy: how the 15-stock basket is built & managed

Core approach: quant rules (momentum + risk-adjusted)

The manager describes quant investing as:

  • Using math/rules to decide which stocks to buy/sell
  • Relying primarily on price/momentum, with fundamentals in the background

“Quant pillars” explicitly stated

  1. Beta (market sensitivity)

    • Target lower beta so the basket falls less in market drawdowns.

  2. Volatility control

    • Use mean of daily returns and standard deviation to avoid overly volatile names.

  3. Sharpe ratio (risk-adjusted returns)

    • Sharpe = (portfolio return − risk-free rate) / volatility
    • Guidance: higher Sharpe is better, targeting ~2 for the basket.

Momentum implementation details

  • Momentum thesis: Stocks going up tend to keep going up; exits must follow rules because no one can predict the future.

  • Rebalancing cadence:

    • Started with monthly rebalancing
    • Shifted to every 2 weeks to improve performance
    • Noted trade-off: transaction costs increase
    • Rebalance described as “every alternate Friday”

Portfolio construction / constraints

  • Universe restriction / eligibility filters:

    • Avoid upper/lower circuit dynamics via checks such as volume
    • Avoid extremely low/high-priced names using a price-range filter (so subscribers can actually buy)
    • While the basket may lean toward higher-risk/higher-reward small caps, product rules manage risk.

  • Position sizing:

    • Target about 6% per stock (for a 15-stock basket)
    • If a stock rises above target weight, the manager trims (examples referenced where weights are reduced after spikes)

Risk management & drawdown behavior (explicit examples)

  • Stated drawdown behavior:

    • “Almost zero down months”
    • Max drawdown ~2%+ (as claimed)

  • Cash call / defensive phase example:

    • March 2nd: benchmark fell about 10%, while the manager claimed the basket was flat (~0%)
    • Explanation: during severe shocks (e.g., oil tensions / Iran-related spike), the approach either held cash/liquid or stayed defensive.

  • Re-deployment back into risk:

    • After rebound, re-entered gradually (example: ~50% cash / 50% Nifty rather than full exposure) to balance upside capture vs oversold risk.

Performance outcomes claimed (with key numbers)

Portfolio performance

  • ~ +20.59% one-year return (approx per subtitles)
  • Benchmark ~ -1.64%
  • Additional references include:
    • Duration around 10 months
    • “~22% alpha” referenced
    • Also states ~20% at the time of discussion

Rotation during rebounds (theme/sector)

  • When earlier momentum stocks didn’t recover, the strategy rotated into new momentum names.
  • Pharma is mentioned as a sector that helped during later rally phases.

Stock-specific momentum winners cited

  • Cupid: ~ +140% since entry; described as behaving like “never fell down” (pure momentum behavior)
  • Loris: ~ +85% over a similar window

Weight-impact emphasis

  • The speaker emphasizes that:
    • A move like 6% → 10% affects returns more than
    • Losses from reductions (i.e., concentrating on profit/loss impact through weights)

Fees / who should buy

Product fee quoted

  • Annual plan: ₹13,999.9 inclusive of GST (framed as “13,9.99”)
  • 6-month plan: ₹7,699.9
  • Speaker frames this as an expense ratio-like cost.

When it “mathematically makes sense” (rule-of-thumb)

  • If investing around ₹1–₹2 lakh: not recommended (fees too high relative to expected net performance after churn/rebalancing).
  • Minimum recommended size: ₹3 lakh+
    • Claimed net fee impact: ~3% effective
    • If basket return is ~20%, speaker’s illustrative net estimate: ~20% − 3% ≈ +17%

Disclosures / compliance references

  • The speaker states they are a SEBI-registered research analyst.
  • Mentions SEBI compliance constraints:
    • Cannot recommend stocks he has bought recently (to avoid front-running/conflicts)
    • Claims audits/compliance explain differences between recommended universe and personal portfolio.

Returns verification claim

  • Mentions a verification process called PARVA:
    • Framed as past returns and risk assessment
    • Run by CDSL (speaker wording includes CDSL and/or a rating agency)
    • Checks trade timestamps and rebalance actions via APIs
  • Claim: verified returns, not just “digital screen” output.

Explicit step-by-step process (as described)

  1. Build the 15-stock basket using quant rules:

    • Lower beta
    • Lower volatility via daily-return statistics (std dev)
    • Higher Sharpe ratio (risk-adjusted excess returns)

  2. Apply additional filters:

    • Volume checks to avoid liquidity/price-limit effects
    • Price-range constraints for investor accessibility

  3. Scan and rebalance:

    • Start monthly, later switch to every 2 weeks
    • Keep stocks that satisfy momentum rules; remove those that fail

  4. Risk controls:

    • Cap position size around ~6% per stock
    • During major selloffs, use cash/liquid (“cash call”) then re-enter gradually

Presenters / sources mentioned

  • Shashank Udupa — SEBI-registered research analyst; manages the “Fluid Q Quant Basket” (in the app)
  • Sharon — host/interviewer
  • SEBI and CDSL — referenced as authorities in the PARVA verification process

Original video