In a previous study published on freefincal, we showed that the Nifty 200 Momentum 30’s apparent alpha largely disappears among high-turnover stocks, and survives only in the illiquid half. The natural follow-up question is: is this a momentum-specific quirk, or does Scaled Turnover reshape other factor premia as well? Is Scaled Turnover a Universal Filter for Factor Premia in India?
About the author: T. Desai runs BacktestIndia.com – a free NSE backtesting platform covering 19 years of data, including delisted stocks.
To find out, I extended the same framework to Quality, Value, and Low Volatility — tested across the broader Nifty 500 universe over 19 years (December 2006 to December 2025).
The Study: For each factor, the Nifty 500 universe was ranked by the relevant factor score and the top 50 stocks were selected. These were then split into two equal groups of 25 — low Scaled Turnover (least liquid) and high Scaled Turnover (most liquid) — where Scaled Turnover is defined as daily trading turnover (shares traded × price) divided by market capitalisation.
All portfolios were backtested with annual rebalancing, equal weighting, and realistic costs including brokerage, slippage, LTCG (12.5%), and STCG (20%). The Nifty 50 returned a net CAGR of 10.41% over the same period.
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The Results
Quality (High ROE)
- Low Scaled Turnover: 15.45% Net CAGR | Max Drawdown -65.04% | Recovery 14 months
- High Scaled Turnover: 8.16% Net CAGR | Max Drawdown -72.02% | Recovery 45 months
Quality takes the biggest hit. High-turnover quality stocks — the well-known, widely covered, high-ROE companies that institutional portfolios typically concentrate in — returned just 8.16% over 19 years, more than 2% below a plain Nifty 50 index fund. The entire quality premium lives in the illiquid, under-researched half: 15.45% CAGR with a recovery time of just 14 months from its worst drawdown.
Value (Low PE, Low PB, High Dividend Yield)
- Low Scaled Turnover: 13.74% Net CAGR | Max Drawdown -54.03% | Recovery 6 months
- High Scaled Turnover: 12.23% Net CAGR | Max Drawdown -64.35% | Recovery 10 months
The return gap here is a modest 1.51% — but the risk story is dramatic. Low-turnover value recovered from its maximum drawdown in just 6 months — the fastest of any strategy in the entire study. High-turnover value took 10 months. The Nifty 500 took 60 months. For value investors, Scaled Turnover is primarily a risk management tool, not just a return enhancer.
Low Volatility (36-month price standard deviation) — the exception
- Low Scaled Turnover: 14.11% Net CAGR | Sharpe 0.87 | Max Drawdown -42.80%
- High Scaled Turnover: 14.03% Net CAGR | Sharpe 0.85 | Max Drawdown -45.79%
Low Volatility is the sole factor that appears completely turnover-agnostic. Both halves delivered nearly identical returns and risk metrics. The Sharpe of 0.87 is the highest among strategies in this study — more than 1.7x the Nifty 500’s 0.51. Its premium appears to stem from structural investor behavioural biases rather than information asymmetry, which is why liquidity does not reshape it the way it does the other three factors.
Momentum (12-month return ÷ 12-month volatility)
- Low Scaled Turnover: 15.04% Net CAGR | Max Drawdown -75.73% | Recovery 68 months
- High Scaled Turnover: 11.89% Net CAGR | Max Drawdown -77.70% | Recovery 70 months
Consistent with the earlier study, the momentum premium is concentrated in illiquid stocks. However, the headline CAGR comes with a sobering caveat: both momentum buckets carry brutal drawdown risk regardless of turnover. A nearly 6-year recovery period demands a level of patience that most investors will underestimate before they experience it.
What This Suggests
Scaled Turnover functions as a near-universal filter for factor premia on the NSE — with one clear exception. The pattern has a coherent explanation: factors that earn their premium through information asymmetry and market neglect (Quality, Momentum, Value) are sensitive to the level of a stock’s trading activity. Factors driven by structural behavioural biases (Low Volatility) are not.
This has implications beyond momentum funds. Any factor-based fund — quality, value, or otherwise — faces the same structural constraint as AUM grows: it is forced to deploy capital in high-turnover stocks, precisely where the data show factor premia tend to disappear. Low Volatility funds are the exception — and with the best risk-adjusted profile of any strategy in this study (Sharpe 0.87, drawdown -42.80%, 8-month recovery), it is also the most AUM-scalable factor available to Indian investors.
Individual investors with smaller portfolios, able to hold low-turnover stocks without market impact, retain a structural advantage in Quality and Momentum that large funds cannot replicate as they scale.
The full methodology is documented here: One Filter. Four Factors. 19 Years: How Scaled Turnover Reshapes Every Factor Premium on the Nifty 500.
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