The Most Anomalous Anomaly in Finance
If markets were perfectly efficient, past price performance should have no predictive power over future returns. Yet across virtually every equity market studied - US, Europe, Japan, and India - stocks that performed well over the past 6–12 months tend to continue outperforming for the next 3–6 months.
This is momentum. It is one of the most replicated findings in empirical finance, and it works in India.
What the Indian Data Shows
Several academic studies using NSE data from 2000–2022 document the following:
A strategy of buying the top quintile of stocks by 12-month return (excluding the most recent month) and holding for 3–6 months generates 3–5% annual alpha over the Nifty 500 after transaction costs.
The effect is strongest in the mid-cap segment (₹5,000–₹30,000 Cr market cap) where institutional coverage is lower and information diffusion is slower. In large-caps, the effect is smaller but still present.
The momentum crash risk: Momentum strategies experience sharp reversals during market regime changes - particularly around RBI rate decisions, general elections, and global risk-off events. The 2020 COVID crash destroyed 3 years of momentum alpha in a single month.
The Mechanics: How to Build a Momentum Screen
The Classic 12-1 Momentum Signal
- For each stock in your universe, calculate total return over the past 12 months
- Exclude the most recent 1 month (the short-term reversal effect makes the last month predictive of near-term mean reversion, not continuation)
- Rank all stocks by this 11-month return
- Buy the top quintile (top 20%); avoid or short the bottom quintile
- Rebalance monthly or quarterly
Enhancements for Indian Markets
Earnings momentum overlay: Combine price momentum with earnings revision momentum. Stocks where analysts are consistently revising earnings estimates upward AND which have strong price momentum show significantly higher returns with lower crash risk.
Volatility-adjusted momentum: Divide the 12-1 price return by the stock's annualised volatility. This gives you a Sharpe-ratio-like momentum signal that avoids selecting high-momentum stocks that simply happened to be extremely volatile.
| Pure Price Momentum | Vol-Adjusted Momentum |
|---|---|
| Higher return potential | Similar return, lower drawdown |
| Higher crash risk | 30–40% lower maximum drawdown |
| More suitable for bull markets | More suitable for all-weather strategies |
Why Momentum Persists: Behavioural Explanations
Anchoring and under-reaction: When a company reports earnings above expectations, investors anchored to the old price initially under-react. The price gradually adjusts over weeks or months, creating a trend.
Herding and trend-following: Retail investors and momentum-following fund managers pile into recent winners, amplifying the trend beyond what fundamentals justify. This is both the source of momentum returns and the source of momentum crash risk.
Practical Implementation Constraints
Transaction costs kill momentum strategies at small scales. A monthly-rebalanced momentum strategy generating 5% annual alpha needs turnover management. If you are trading a ₹10 lakh portfolio with 1% average impact cost, transaction costs can erode 3–4% annually.
Liquidity filter: Only apply momentum strategies to stocks with average daily trading volume > ₹5 Cr. Below that, your own trading will move the price against you.
Tax consideration: Monthly rebalancing generates short-term capital gains at 20%. Quarterly rebalancing is more tax-efficient even if it slightly reduces signal quality.
Intrynsic's market signals dashboard provides a pre-calculated momentum ranking for NSE stocks, updated daily, with volatility-adjusted scores and liquidity filters applied.