Behavioural finance · Indian markets

Better decisions begin with better questions.

Markets move.
So do we.

A research-led look at the human patterns behind investing, paired with systematic tools built for India.

Independent research. Clear methods. No predictions.

THE INVESTOR, IN CONTEXT01 / 04
01 Loss aversion
02 Recency
03 Anchoring
THENDECISIONS HAPPEN HERENOW
↗Data describes the market.
Behaviour shapes the response.
Portfolio today—
This month—
Research lensMind × market
UniverseNifty 200 + ETFs

The human factor

Good investing is also a study of ourselves.

Prospect theory, the disposition effect and research on overconfidence offer useful ways to examine how people respond to gains, losses and uncertainty. They are prompts for reflection, not labels for individuals or signals about what a market will do.

Add your perspective
A

Losses feel different

Prospect theory asks how the framing of gains and losses can influence choices under risk.

Kahneman & Tversky · 1979
B

What we hold on to

The disposition effect describes a tendency studied in how investors treat winners and losers.

Shefrin & Statman · 1985
C

Confidence has a cost

Trading research has examined how confidence and activity can interact with investor outcomes.

Barber & Odean · 2000

A small research pulse

What do you do when the market gets loud?

Share how you think about investing in three quick questions. There are no right answers; your perspective helps shape better conversations about financial behaviour.

01:00Three questions · Your choice to send

Your answers stay in this browser until you choose to open a text message. Review or edit it before sending.

01 A holding you own falls sharply. What is your first move?
02 A stock has risen for several weeks. What matters most?
03 What helps you make a calmer decision?

Model Portfolio

Hybrid Dynamic Momentum 30

Regime-adaptive · Nifty 200 universe · Buffer-based selection · Quarterly rebalance

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Today —
1 Week —
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vs Nifty 50 —

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Current Holdings

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Stock Weight Price Today 1 Month

Top Performers (1M)

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Strategy Analytics (1Y)

Sharpe Ratio —
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Live prices via NSE data · Returns are simulated buy-and-hold, not actuals · Past performance is not indicative of future results · Not investment advice

How it works

Three steps to institutional alpha

Our end-to-end quant pipeline runs monthly and adapts to market regimes in real time.

01
📡

Regime Detection

Classify the market as GROWTH or DEFENSIVE using 3-month NiftyBeES momentum. Shift allocation to equities or gold accordingly.

02
⚡

Momentum Ranking

Score each stock in the Nifty 200 using MR12 × MR6 composite momentum. Cross-sectional Z-scores normalize across sectors.

03
🎯

Portfolio Construction

Buffer selection picks the top 15 mandatory stocks and retains any rank ≤ 45 from last cycle. Iterative weight capping ensures diversification.

Strategy Methodology

Quantitative rigor,
institutional grade

Every parameter is academically grounded and India-market calibrated.

📈
Regime Engine

Adaptive Regime Detection

3-month NiftyBeES return determines market regime. GROWTH regime overweights equities; DEFENSIVE shifts to GoldBeES and low-beta names.

Regime = GROWTH if R(NiftyBeES, 3M) > 0 else DEFENSIVE Equity_wt = 0.85 (GROWTH) | 0.50 (DEFENSIVE) Gold_wt = 0.15 (GROWTH) | 0.30 (DEFENSIVE)
⚖️
Momentum Score

Composite Momentum Ratio

Dual-horizon momentum captures both trend persistence and recent acceleration. Stocks are ranked by a geometric composite of 12-month and 6-month returns.

MR12 = P_now / P_12M_ago MR6 = P_now / P_6M_ago Composite = MR12 × MR6 Z_score = (Composite − μ) / σ
🔒
Buffer Selection

Turnover-Controlled Selection

Buffer logic reduces unnecessary churn: any stock ranked ≤ 45 from the last cycle is automatically retained, keeping portfolio turnover below 30% per quarter.

Mandatory: top 15 by Z_score Retained: prev_rank ≤ 45 ∩ curr_rank ≤ 45 Target: 30 stocks total Max weight: 5% per stock (iterative cap)
🏗️
Weight Construction

FFMC-Weighted Allocation

Free-float market cap weights ensure the portfolio is investable at scale. Iterative redistribution of excess weight from capped stocks prevents concentration risk.

w_i = FFMC_i / Σ FFMC_j (raw weight) Iterative cap: while any w_i > 5%: excess = Σ max(w_i − 5%, 0) redistribute excess ∝ FFMC of uncapped re-normalize

Live Research

Browse the strategy library

Each model runs live against the same NSE data behind the portfolio above — pick a strategy to open its live backtest.

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Community

Trade ideas that outperform

Follow quantitative portfolios created by the VishwNivesh community. Publish yours and earn when others subscribe.

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Pricing

Institutional access,
retail pricing

Explorer

Free

Forever

  • Live Hybrid Momentum 30 portfolio
  • NAV chart vs Nifty 50
  • Performance metrics dashboard
  • Community portfolio leaderboard
  • Strategy methodology docs

Institutional

Custom

Bespoke strategy

  • Custom factor model development
  • Dedicated quant analyst support
  • White-label portfolio reports
  • Sharekhan API integration
  • SLA-backed data pipeline

Ready to take the next step?

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