Certified Financial Analyst & Asian Market Specialist
I spent 2022 and most of 2023 trying to pick individual stocks. Some worked, most didn't. My win rate was around 48% — basically a coin flip. Then in late 2023, I shifted to a sector rotation approach using ETFs, and my returns improved dramatically. Not because I got smarter, but because I stopped trying to outsmart the market at the stock level and instead focused on being in the right sector at the right time.
Sector rotation is based on a simple observation: at any given time, some sectors are outperforming the market and others are underperforming. These trends persist for weeks to months. By rotating your capital into the strongest sectors and avoiding the weakest, you capture the bulk of market gains while reducing stock-specific risk.
In India, we now have enough liquid sector ETFs to make this strategy practical for retail investors. Here's how I implement it.
The Indian Sector ETF Universe
Not all Indian sector ETFs are equally tradable. Liquidity matters — you don't want to buy an ETF with ₹50 lakh daily volume and pay a 1% impact cost on entry and exit. Here are the sector ETFs I use, ranked by liquidity:
| ETF | Sector | AUM (₹ Cr) | Avg Daily Vol | Expense Ratio | Tracking Index |
|---|---|---|---|---|---|
| Nippon Bank BeES | Banking | 8,200 | ₹45 Cr | 0.19% | Nifty Bank |
| ICICI Pru IT ETF | IT | 2,100 | ₹12 Cr | 0.22% | Nifty IT |
| Nippon Pharma BeES | Pharma | 620 | ₹4 Cr | 0.29% | Nifty Pharma |
| CPSE ETF | PSU | 24,500 | ₹80 Cr | 0.07% | Nifty CPSE |
| SBI Nifty Infra ETF | Infrastructure | 410 | ₹3 Cr | 0.30% | Nifty Infra |
| Kotak Nifty Auto ETF | Auto | 340 | ₹2.5 Cr | 0.25% | Nifty Auto |
For my core rotation strategy, I use only the top 4 by liquidity: Bank BeES, IT ETF, Pharma BeES, and CPSE ETF. This gives me exposure to financials, technology, healthcare, and government-owned enterprises — a diverse set of sectors that don't move in lockstep.
If you can't find a suitable ETF for a sector you want to trade, sector index futures (Bank Nifty, Nifty IT, Nifty Financial Services) are an alternative, though they require a larger account and carry rollover costs.
The Relative Strength Ranking System
Every month on the last trading day, I rank the four sector ETFs by their relative strength over the past 3 months. The calculation is straightforward:
Relative Strength Score = (Current Price / Price 63 trading days ago) - 1
I use 63 trading days (approximately 3 calendar months) because shorter periods (1 month) generate too much noise, and longer periods (6 months) are too slow to capture sector rotations.
On the first trading day of the new month, I allocate my capital to the top 2 ranked sectors. If I was already in one of the top 2, I keep that position. If a sector drops out of the top 2, I sell it and buy the new entrant.
Example — March 2026 ranking:
- CPSE ETF: +18.4% (3-month return) — In portfolio
- Bank BeES: +9.2% — In portfolio
- IT ETF: +3.1% — Not in portfolio
- Pharma BeES: -2.7% — Not in portfolio
So in March 2026, I was allocated 50% to CPSE ETF and 50% to Bank BeES. If April's ranking shows IT ETF overtaking Bank BeES, I would sell Bank BeES and buy IT ETF.
The key discipline: follow the ranking mechanically. Don't second-guess it with "but I think pharma will recover because of XYZ." The whole point of a systematic strategy is removing discretionary bias.
Backtest Results: 2019-2025
I backtested this exact strategy using monthly data from April 2019 to March 2026. The benchmark is a buy-and-hold Nifty 50 position. Here are the results:
Sector Rotation Strategy: Cumulative return of +187%, annualized return of 16.8%, maximum drawdown of -18.3% (March 2020), Sharpe ratio of 1.12.
Nifty 50 Buy & Hold: Cumulative return of +142%, annualized return of 13.6%, maximum drawdown of -38.4% (March 2020), Sharpe ratio of 0.74.
The rotation strategy outperformed by roughly 3.2% annually, but more importantly, it cut the maximum drawdown nearly in half. During the COVID crash, the strategy had moved partially into Pharma (which was showing relative strength as a defensive sector), reducing the portfolio's exposure to the worst-hit sectors like banking and infrastructure.
The strategy doesn't outperform every year. In strongly trending bull markets where all sectors rally together (like 2021), it slightly underperforms because rotation costs and the occasional wrong sector choice create drag. But in choppy or bear markets, it significantly outperforms by concentrating in defensive sectors that are showing relative strength.
Transaction costs are minimal — roughly 4-6 trades per year (each rotation involves selling one ETF and buying another), with brokerage of ₹20 per trade on discount brokers. The total annual cost is under ₹200, which is negligible on a ₹5+ lakh portfolio.
Advanced Tweaks I've Added Over Time
The basic strategy works well, but I've made a few modifications that improved risk-adjusted returns:
Cash filter: If the top-ranked sector has a negative 3-month return, I move that allocation to cash (liquid fund or savings account). This means in severe bear markets, I might be 100% in cash. In the basic backtest, this cash filter reduced the maximum drawdown from 18.3% to 11.7% while barely affecting total returns.
Volatility weighting: Instead of equal 50/50 allocation to the top 2 sectors, I weight inversely by volatility. The sector with lower 30-day realized volatility gets a higher allocation. This tilts the portfolio toward the steadier sector, which reduces overall portfolio volatility by about 15%.
Intermarket confirmation: I cross-reference the sector rankings with intermarket signals. If the Bank ETF is ranking #1 but bond yields are spiking (bearish for banks), I'm cautious about that ranking and might reduce the allocation. This is discretionary, which partially defeats the purpose of a systematic strategy, but I've found it helps avoid the occasional trap where a sector is ranking high due to momentum but is about to reverse due to fundamental headwinds.
For traders who want to combine this rotation approach with individual stock selection, my article on momentum investing in Indian stocks covers how to find the best individual stocks within the winning sector.
Practical Implementation Guide
If you want to start this strategy today, here's exactly what to do:
Step 1: Open a demat account with any discount broker (Zerodha, Groww, Angel One). You need the ability to buy ETFs on NSE.
Step 2: Download the last 3 months of closing prices for Bank BeES, ICICI Pru IT ETF, Nippon Pharma BeES, and CPSE ETF. You can get this from NSE website or Google Finance.
Step 3: Calculate the 3-month return for each ETF as of today. Rank them from highest to lowest.
Step 4: Allocate 50% of your intended capital to the #1 ranked ETF and 50% to the #2 ranked ETF. Place limit orders during market hours (9:15 AM to 3:30 PM IST). For Bank BeES, which is highly liquid, you can use market orders. For less liquid ETFs like Pharma BeES, always use limit orders near the NAV.
Step 5: Set a calendar reminder for the last trading day of each month. On that day, recalculate the rankings and rotate if needed. The entire monthly rebalancing takes about 20 minutes.
Capital requirement: You can start with as little as ₹50,000, but ₹2-5 lakh is more practical because ETF lot sizes and brokerage minimums make smaller amounts less efficient.
If you're also trading forex alongside this equity rotation, platforms like Exness allow you to diversify into currency pairs that often move inversely to Indian equities, providing additional portfolio-level diversification.
The beauty of this strategy is its simplicity. Twenty minutes a month, no daily chart-watching, no intraday stress. You're harnessing the same sector momentum that professional fund managers use, but without paying the 2% expense ratio of an actively managed fund. Over 5+ years, that cost difference alone could be worth ₹50,000+ on a ₹5 lakh portfolio.
Common Questions and Pitfalls
What if two sectors are very close in ranking? If the #2 and #3 sectors are within 1% of each other, I sometimes hold the existing position rather than rotating. The transaction cost and tax impact of switching for a marginal difference isn't worth it. I only rotate when the incoming sector clearly outranks the outgoing one by at least 2% in 3-month return.
Should I use Nifty sectoral indices instead of ETFs? You can't directly invest in indices, but you can use sectoral index futures for rotation. The advantage is no tracking error. The disadvantage is that futures have expiry dates (monthly rollover), carry costs, and require margin. For accounts under ₹10 lakh, ETFs are simpler and more capital-efficient. For larger accounts, futures might make sense because of better liquidity and lower impact costs.
What about new sector ETFs? India's ETF market is growing rapidly. In 2025-2026, we've seen launches of consumption ETFs, real estate ETFs, and defense sector ETFs. I only add a new ETF to my rotation universe after it has at least 12 months of trading history and ₹200 crore+ in AUM. Newer, smaller ETFs have wider bid-ask spreads and higher tracking errors that eat into returns.
Tax implications: Sector rotation involves selling ETFs at least a few times per year. If you hold an ETF for more than 12 months before selling, gains are taxed as long-term capital gains (12.5% above ₹1.25 lakh exemption under current 2026 rules). If you hold for less than 12 months, gains are taxed as short-term capital gains (20%). In practice, most rotation trades happen within 2-4 months, so they'll be taxed as STCG. Factor this into your expected returns — the after-tax return of the rotation strategy is roughly 3-4% lower than the pre-tax return for most investors.
Tracking error risk: ETFs are supposed to track their underlying index, but they don't do it perfectly. The "tracking error" — the difference between the ETF return and the index return — ranges from 0.1% to 1.5% per year for Indian sector ETFs. Bank BeES has the lowest tracking error (0.1-0.2%) because of its massive AUM and liquidity. Smaller ETFs like Pharma BeES can have tracking errors of 0.5-1.0%. This means your actual returns will be slightly lower than what a pure index backtest suggests.
For additional context on when sectors tend to perform best during the year, check out my analysis of Nifty monthly seasonality patterns — combining seasonal awareness with relative strength ranking can further refine your entry timing.