India's 52-Week High Proximity: 26.7% CAGR Over 25 Years
We ran the 52-week high proximity strategy on Indian stocks (BSE + NSE) from 2000 to 2025. The result: 26.73% annualized, 23.4% down capture, Sharpe of 0.735. A $1 investment grew to $444.67. The S&P 500 returned $7.30 over the same period.
Contents
- Method
- What is the 52-Week High Proximity Strategy?
- What We Found
- 26.73% CAGR. 23.4% down capture. $444.67 from $1.
- Year-by-Year Returns
- 2000–2001: cash while SPY dropped
- 2002: limited losses while SPY fell 20%
- 2003–2007: the Sensex bull market, captured fully
- 2008: the worst single year at -52.8%
- 2009 recovery: +52.8% in a single year
- 2022: the defining protection year
- Why India Specifically
- Limitations
- Run This Screen Yourself
- Part of a Series
- References
India is the outlier in this strategy's global results. No other exchange comes close to these numbers. Understanding why requires looking at both the signal mechanics and the market structure that makes anchoring bias so visible in India.
Method
- Data source: Ceta Research (FMP financial data warehouse)
- Universe: BSE + NSE, market cap > ₹20B
- Period: 2000–2025 (25 years, 103 quarterly periods)
- Rebalancing: Quarterly (January, April, July, October), equal weight
- Benchmark: S&P 500 Total Return (SPY, in USD, cross-currency comparison)
- Returns: Calculated in INR; SPY in USD for reference
- Cash rule: Hold cash if fewer than 10 stocks qualify
The signal is the proximity ratio: current price divided by the 52-week high (rolling 252 trading days). Stocks are ranked by proximity ratio and the top 30 are held each quarter. Financial data uses a 45-day lag on annual filings.
Note on benchmarking: India returns are in INR. SPY is in USD. The comparison shows how the strategy performed against the global equity benchmark, not a currency-adjusted return.
What is the 52-Week High Proximity Strategy?
George and Hwang (2004) documented that stocks trading near their 52-week high outperform stocks far from it. Their paper, published in the Journal of Finance, showed the signal predicted 12-month forward returns across US stocks.
The mechanism is anchoring bias. Investors mentally treat the 52-week high as a ceiling. When a stock approaches that level, some investors sell preemptively, worried the price won't break through. This creates selling pressure that temporarily depresses the price below its fundamental value. When earnings or news push the stock past the anchor, the undervaluation corrects quickly.
The strategic implication: stocks near their 52-week high aren't expensive. They're finishing their undervaluation period. The signal captures this before the correction closes.
Proximity ratio = adjClose / MAX(high over 252 trading days)
A ratio of 1.0 means the stock is exactly at its 52-week high. A ratio of 0.95 means it's 5% below. We select the top 30 stocks by this ratio, the ones closest to their annual peak.
What We Found
26.73% CAGR. 23.4% down capture. $444.67 from $1.
| Metric | 52-Week High India | S&P 500 |
|---|---|---|
| CAGR | 26.73% | 8.01% |
| Excess Return | +18.72% | — |
| Total Return | $444.67 per $1 | $7.30 per $1 |
| Max Drawdown | -54.1% | -45.53% |
| Sharpe Ratio | 0.735 | 0.354 |
| Sortino Ratio | 1.51 | — |
| Calmar Ratio | 0.494 | — |
| Up Capture | 160.46% | — |
| Down Capture | 23.4% | — |
| Cash Periods | 10% of quarters | — |
| Avg Stocks (invested) | 28.0 | — |
| Win Rate | 63.1% | — |
The down capture of 23.4% is the most striking number. When US markets fell, this strategy absorbed less than a quarter of the decline. That's not a rounding error, it reflects a structural feature of the signal. When markets sell off, fewer stocks are near their 52-week highs. The screen naturally migrates toward cash or a smaller, more defensive subset of the universe.
The Sortino ratio of 1.51 confirms the asymmetry. The strategy generates strong upside returns relative to its downside risk. A Sharpe of 0.735 is already well above the S&P 500's 0.354, but the Sortino paints an even better picture because it weights the type of volatility that matters to investors.
Year-by-Year Returns
| Year | India Strategy | S&P 500 | Excess |
|---|---|---|---|
| 2000 | 0.0% (cash) | -10.5% | n/a |
| 2001 | 0.0% (cash) | -9.2% | n/a |
| 2002 | -5.3% | -19.9% | +14.6% |
| 2003 | +119.1% | +24.1% | +95.0% |
| 2004 | +28.3% | +10.2% | +18.1% |
| 2005 | +84.4% | +7.2% | +77.2% |
| 2006 | +68.4% | +13.7% | +54.7% |
| 2007 | +112.2% | +4.4% | +107.8% |
| 2008 | -52.8% | -34.3% | -18.5% |
| 2009 | +52.8% | +24.7% | +28.1% |
| 2010 | +14.8% | +14.3% | +0.5% |
| 2011 | -16.0% | +2.5% | -18.5% |
| 2012 | +54.8% | +17.1% | +37.7% |
| 2013 | +12.4% | +27.8% | -15.4% |
| 2014 | +65.1% | +14.5% | +50.6% |
| 2015 | +21.7% | -0.1% | +21.8% |
| 2016 | +18.0% | +14.4% | +3.6% |
| 2017 | +65.6% | +21.6% | +44.0% |
| 2018 | -13.4% | -5.2% | -8.2% |
| 2019 | +30.1% | +32.3% | -2.2% |
| 2020 | +16.9% | +15.6% | +1.3% |
| 2021 | +76.4% | +31.3% | +45.1% |
| 2022 | +13.4% | -19.0% | +32.4% |
| 2023 | +42.1% | +26.0% | +16.1% |
| 2024 | +43.1% | +25.3% | +17.8% |
| 2025 | -0.3% | +15.3% | -15.6% |
2000–2001: cash while SPY dropped
The signal found no qualifying stocks in India during 2000 and 2001. The BSE universe was too thin and too depressed, stocks weren't near their 52-week highs after the dot-com selloff and domestic market weakness. The screen held cash.
SPY fell 10.5% in 2000 and 9.2% in 2001. The India strategy sat out both years at 0%. It wasn't skill. It was the signal refusing to fire in a bad environment. That's exactly the downside protection mechanism at work.
2002: limited losses while SPY fell 20%
In 2002, the strategy entered the market but stayed selective. Only -5.3% against SPY's -19.9%. The proximity filter kept the portfolio in the subset of Indian stocks still showing relative strength, companies that hadn't broken down despite the broader weakness.
2003–2007: the Sensex bull market, captured fully
The Sensex ran from roughly 2,900 in early 2003 to over 21,000 by January 2008. This is one of the great emerging market bull runs. The 52-week high strategy captured it with compounding force: +119.1%, +28.3%, +84.4%, +68.4%, +112.2% across five consecutive years.
The up capture ratio of 160.46% reflects these years. India's momentum-driven retail market amplified the signal. When stocks broke to new annual highs on the Sensex, the buying pressure continued well past the breakout. The strategy was already positioned before the crowd arrived.
2008: the worst single year at -52.8%
The 2008 global financial crisis hit India hard. The BSE Sensex fell from 21,000 to under 8,000. The strategy fell 52.8%, worse than SPY's 34.3%. Concentrated emerging market stocks in a full-risk-on portfolio sell off faster in a global deleveraging event.
This is the strategy's honest weakness. The downside protection works well in ordinary corrections but less well in acute global crises. When all correlations go to 1, the proximity filter can't protect you.
2009 recovery: +52.8% in a single year
The rebound matched the drawdown in speed. +52.8% in 2009 while SPY returned 24.7%. Indian stocks near their new annual highs in 2009 were companies that survived the crisis with business models intact and were early to rebound. The signal found them.
2022: the defining protection year
2022 stands out. US rates rose aggressively, the S&P 500 fell 19.0%, and most global markets declined. The India 52-week high strategy returned +13.4%, a 32.4 percentage point outperformance.
This is where the asymmetry proves itself outside of retrospective bull market framing. The signal rotated the portfolio into the subset of Indian stocks still showing relative strength during a global selloff. India's domestic economy was less exposed to rate hike transmission than US equities. The combination of a domestically-oriented market and a signal that naturally exits broken-down stocks created genuine protection.
Why India Specifically
The anchoring bias mechanism exists in every market. But the effect is strongest where retail investor participation is highest and where institutional arbitrage is slowest.
India checks both boxes. BSE and NSE have unusually high retail participation, individual investors account for a large share of daily volume, particularly in the mid-cap segment where this strategy operates. Retail investors anchor strongly to round numbers, 52-week highs, and all-time highs. They sell near those levels expecting resistance.
Institutional investors in India, while growing, have historically been less concentrated in the mid-cap space where the signal fires best. Foreign institutional investors move in and out at the large-cap level. The mid-cap universe the proximity screen targets is relatively less arbitraged.
The result: anchoring discounts persist longer in India than in developed markets. The strategy captures those discounts before the repricing, quarter after quarter.
Limitations
Currency risk. Returns are in INR. INR depreciation reduces USD equivalent returns. Indian investors wouldn't face this, but the cross-currency comparison against SPY is imprecise.
Max drawdown of 54.1%. The strategy isn't a low-volatility approach. 2008 required holding through a -52.8% year. Investors who couldn't stay the course would have locked in losses at the worst moment.
Emerging market liquidity. India's mid-cap segment has thinner order books. Transaction costs modeled here are size-tiered but may not fully reflect real execution for large positions.
Survivorship bias. Profiles use current exchange listings. Indian companies that delisted or failed aren't tracked through failure. This likely understates historical drawdowns somewhat.
2025 context. The -0.3% result in 2025 (vs SPY +15.3%) shows the strategy can miss when US-driven momentum diverges from Indian domestic conditions. No signal works every year.
Run This Screen Yourself
Current 52-week high proximity screen (Indian stocks):
SELECT
s.symbol,
p.companyName,
p.sector,
ROUND(s.adjClose / MAX(s.high) OVER (
PARTITION BY s.symbol
ORDER BY s.date
ROWS BETWEEN 251 PRECEDING AND CURRENT ROW
), 4) AS proximity_ratio,
ROUND(p.mktCap / 1e9, 2) AS mktcap_bn_inr
FROM stock_eod s
JOIN profile p ON s.symbol = p.symbol
WHERE p.exchange IN ('BSE', 'NSE')
AND p.mktCap > 20000000000
AND s.date = (SELECT MAX(date) FROM stock_eod)
QUALIFY ROW_NUMBER() OVER (PARTITION BY s.symbol ORDER BY s.date DESC) = 1
ORDER BY proximity_ratio DESC
LIMIT 30
Run this screen on Ceta Research
The full backtest code (Python + DuckDB) is on GitHub.
Part of a Series
This post is part of our 52-week high proximity global exchange comparison:
- Korea (KSC): 11.4% CAGR, 39% Down Capture
- Thailand (SET): Anchoring Works in Emerging Markets Too
- Japan (JPX): Steady Alpha in a Flat Market
- Global Comparison: 52-Week High Proximity Across Exchanges
References
- George, T. & Hwang, C. (2004). "The 52-Week High and Momentum Investing." Journal of Finance, 59(5), 2145–2176.
- Jegadeesh, N. & Titman, S. (1993). "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." Journal of Finance, 48(1), 65–91.
- Rouwenhorst, K. (1998). "International Momentum Strategies." Journal of Finance, 53(1), 267–284.
Run It Yourself
Explore the data behind this analysis on Ceta Research. Query our financial data warehouse with SQL, build custom screens, and run your own backtests across 70,000+ stocks on 20 exchanges.
Data: Ceta Research (FMP financial data warehouse). Universe: BSE + NSE, market cap > ₹20B. Quarterly rebalance, equal weight, transaction costs included, 2000–2025. Returns in INR.
