12-Month Momentum on Indian Stocks: 17.68% CAGR Over 25 Years

Pure price momentum works in India. Buying the top 30 NSE stocks by 12-month trailing return, semi-annually, delivered 17.68% annually from 2004 to 2025. That's +6.33% per year over the Sensex. The portfolio captured 120% of the Sensex's upside with 61% of its downside. The factor is alive in a market where it hasn't been systematically traded to exhaustion.

Contents

1. Method
2. What We Found
3. Backtest Methodology
4. Limitations
5. Takeaway
6. Part of a Series
7. References
8. Run This Screen Yourself

Data: FMP financial data warehouse, 2000–2025. Updated March 2026.

Method

India started with 0% invested through 2003. The combination of market cap threshold and momentum data requirements excluded the full NSE universe in those early periods. The live backtest ran from the first period with 10+ qualifying stocks (mid-2004 entry).

What We Found

25-year summary (2000–2025):

The total return difference is significant. $1 invested in the strategy in 2000 became $63.6 by 2025. The same dollar in the Sensex became $15.5. The 6,258% vs 1,452% comparison reflects what +6.33% annual excess return compounds to over 25 years.

The down capture of 61.16% means the portfolio fell about 39% less than the Sensex in bad markets. Momentum in Indian equities has been concentrated in domestic consumer, financial, and infrastructure sectors that hold up better than the broad index in most downturns.

The 2008 exception. Momentum couldn't protect against the global financial crisis. The strategy fell -68.6% in 2008 vs the Sensex's -51.3%, the worst single-year performance in the dataset. Indian equities were caught in the same forced-selling wave as global risk assets. The subsequent recovery was fast: +71.5% in 2009.

Year-by-year standouts:

2022 is a demonstration of the down capture story. The Sensex gained 3.4%, while the India momentum portfolio gained 7.6%. The portfolio was concentrated in domestic-facing Indian sectors with genuine earnings momentum. The 4-point outperformance in a low-growth year reflects the factor capturing real sector leadership.

The bull market magnification. Up capture of 119.86% means the portfolio amplified Indian bull markets. The years 2005, 2007, 2017, 2021, and 2023 all show the strategy running well above the Sensex. Indian equity bull markets have been concentrated in specific sectors, and momentum captures that concentration.

Backtest Methodology

• Data: FMP financial data via Ceta Research warehouse. Price data from stock_eod (adjusted closes).
• Point-in-time: Market cap filter uses annual key_metrics filings with 45-day lag. No look-ahead bias. Indian filings typically arrive April-May for fiscal year ending March.
• Signal: Price at T-12M to T-1M. The 1-month skip avoids short-term reversal contamination.
• Data quality: Stocks with adjusted close < $1 at either lookback date excluded. Momentum capped at 500% per stock.
• Cash periods: 18% of semi-annual periods had fewer than 10 qualifying stocks. These periods contribute 0% return (no positions taken). This applies to early data periods when FMP coverage of NSE was thinner.
• Equal weight: 30 positions when invested. No intraperiod rebalancing.
• Transaction costs: Modeled as size-tiered. Indian liquidity is lower than US, so costs are higher in the model.
• Benchmark: BSE Sensex (^BSESN) total return. Returns are in local currency (INR) terms.
• Entry execution: Next-day close (MOC model). Signals generated from prior close, positions entered at next trading day's close.

Limitations

Local currency returns. The benchmark is the Sensex (INR). Returns are in local currency terms. For non-INR investors, currency fluctuations would affect realized returns. From 2000 to 2025, the INR depreciated roughly 50% against the USD, so USD-denominated returns would be lower.

2008 shows the tail risk. A -68.60% max drawdown is severe. The fast recovery is historically documented, but a drawdown that large requires patience most investors don't have.

18% cash periods in early data. Coverage of NSE in FMP's database is thinner before 2004. The strategy was in cash for the first few years and several subsequent periods. This reduces exposure during those windows.

Emerging market structural risks. FPI limits, SEBI regulatory changes, and market access restrictions are real ongoing factors that can change the investability of this strategy. The historical backtest doesn't account for these.

No transaction costs in emerging market context. The model uses size-tiered costs, but Indian mid-cap liquidity can be thin. Actual execution costs for a large position would be higher than modeled.

Takeaway

12-month momentum on NSE stocks delivered 17.68% CAGR from 2000 to 2025. The +6.33% excess over the Sensex is durable momentum alpha. The 61.16% down capture means the portfolio fell about 39% less than the index in bad years.

The explanation likely involves market structure. Indian equities are driven by domestic growth cycles, consumer expansion, and infrastructure investment. Momentum in this environment captures real economic trends that persist for 6-12 months. The market is also less saturated with systematic momentum traders compared to US equities.

The 2008 risk remains the main caution. Any globally correlated crash will hit India momentum hard. But the evidence from 2022, 2015, and other non-global-crisis downturns suggests the strategy has genuine downside protection in the scenarios that matter most.

Part of a Series

This is part of a multi-exchange 12-month momentum study:

• 12M Momentum: Global 18-Exchange Comparison — published April 14
• 12M Momentum: Germany Best Sharpe in 18 Exchanges — published April 18
• 12M Momentum: UK Consistent Alpha, Low Down Capture — published April 20
• 12M Momentum: US Underperforms SPY by 3.34%/yr — published April 23

Data: Ceta Research (FMP financial data warehouse), 2000–2025. Universe: NSE, market cap > ₹20B (point-in-time, 45-day lag). Full methodology: METHODOLOGY.md. Past performance does not guarantee future results. This is educational content, not investment advice.

References

• Jegadeesh, N. & Titman, S. (1993). Returns to Buying Winners and Selling Losers. Journal of Finance, 48(1), 65-91.
• Asness, C., Moskowitz, T. & Pedersen, L. (2013). Value and Momentum Everywhere. Journal of Finance.
• Daniel, K. & Moskowitz, T. (2016). Momentum Crashes. Journal of Financial Economics.

Run This Screen Yourself

The current 12-month momentum screen for Indian stocks is live on Ceta Research:

cetaresearch.com/data-explorer?q=9S2cYgWPNk

-- 12-Month Momentum India Screen
-- Live at: cetaresearch.com/data-explorer?q=9S2cYgWPNk
WITH universe AS (
    SELECT p.symbol, p.companyName, p.exchange, k.marketCap / 1e9 AS market_cap_billions
    FROM profile p JOIN key_metrics_ttm k ON p.symbol = k.symbol
    WHERE k.marketCap > 20000000000 AND p.isActivelyTrading = true
      AND p.exchange IN ('NSE')
),
price_12m_ago AS (
    SELECT symbol, adjClose AS price_12m,
           ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY ABS(CAST(dateEpoch AS BIGINT) -
               CAST(EXTRACT(EPOCH FROM (CURRENT_DATE - INTERVAL '365' DAY))::BIGINT AS BIGINT))) AS rn
    FROM stock_eod
    WHERE CAST(date AS DATE) BETWEEN CURRENT_DATE - INTERVAL '395' DAY
                                 AND CURRENT_DATE - INTERVAL '335' DAY
      AND adjClose > 0
),
price_1m_ago AS (
    SELECT symbol, adjClose AS price_1m,
           ROW_NUMBER() OVER (PARTITION BY symbol ORDER BY ABS(CAST(dateEpoch AS BIGINT) -
               CAST(EXTRACT(EPOCH FROM (CURRENT_DATE - INTERVAL '30' DAY))::BIGINT AS BIGINT))) AS rn
    FROM stock_eod
    WHERE CAST(date AS DATE) BETWEEN CURRENT_DATE - INTERVAL '45' DAY
                                 AND CURRENT_DATE - INTERVAL '15' DAY
      AND adjClose > 0
)
SELECT u.symbol, u.companyName, u.exchange,
    ROUND(u.market_cap_billions, 2) AS market_cap_billions,
    ROUND((p1m.price_1m - p12.price_12m) / p12.price_12m * 100, 1) AS return_12m_1m_pct
FROM universe u
JOIN price_12m_ago p12 ON u.symbol = p12.symbol AND p12.rn = 1
JOIN price_1m_ago p1m ON u.symbol = p1m.symbol AND p1m.rn = 1
WHERE p12.price_12m > 1.0 AND p1m.price_1m > 1.0
  AND (p1m.price_1m - p12.price_12m) / p12.price_12m <= 5.0
ORDER BY return_12m_1m_pct DESC NULLS LAST
LIMIT 30;