Losses feel different
Prospect theory asks how the framing of gains and losses can influence choices under risk.
Kahneman & Tversky · 1979Better decisions begin with better questions.
A research-led look at the human patterns behind investing, paired with systematic tools built for India.
Independent research. Clear methods. No predictions.
The human factor
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 perspectiveProspect theory asks how the framing of gains and losses can influence choices under risk.
Kahneman & Tversky · 1979The disposition effect describes a tendency studied in how investors treat winners and losers.
Shefrin & Statman · 1985Trading research has examined how confidence and activity can interact with investor outcomes.
Barber & Odean · 2000A small research pulse
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Model Portfolio
Regime-adaptive · Nifty 200 universe · Buffer-based selection · Quarterly rebalance
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| Stock | Weight | Price | Today | 1 Month |
|---|---|---|---|---|
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
Our end-to-end quant pipeline runs monthly and adapts to market regimes in real time.
Classify the market as GROWTH or DEFENSIVE using 3-month NiftyBeES momentum. Shift allocation to equities or gold accordingly.
Score each stock in the Nifty 200 using MR12 × MR6 composite momentum. Cross-sectional Z-scores normalize across sectors.
Buffer selection picks the top 15 mandatory stocks and retains any rank ≤ 45 from last cycle. Iterative weight capping ensures diversification.
Strategy Methodology
Every parameter is academically grounded and India-market calibrated.
3-month NiftyBeES return determines market regime. GROWTH regime overweights equities; DEFENSIVE shifts to GoldBeES and low-beta names.
Dual-horizon momentum captures both trend persistence and recent acceleration. Stocks are ranked by a geometric composite of 12-month and 6-month returns.
Buffer logic reduces unnecessary churn: any stock ranked ≤ 45 from the last cycle is automatically retained, keeping portfolio turnover below 30% per quarter.
Free-float market cap weights ensure the portfolio is investable at scale. Iterative redistribution of excess weight from capped stocks prevents concentration risk.
Live Research
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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