One Filter. Four Factors. 19 Years: How Scaled Turnover Reshapes Every Factor Premium on the Nifty 500

Introduction: One Filter, Four Tests

Our earlier study on Nifty 200 Momentum 30 produced an uncomfortable finding: the entire momentum premium in India lives in the illiquid half of the universe. High Scaled Turnover momentum — the stocks large funds actually hold — returned just 8.51% Net CAGR over 19 years, below a plain Nifty 50 index fund.

The natural follow-up question: is this a momentum-specific quirk, or something deeper? Does Scaled Turnover reshape every equity factor premium — or is momentum uniquely distorted by liquidity?

To answer this, we ran the same Scaled Turnover decomposition across four classic equity factors on the broader Nifty 500 universe — Quality (High ROE), Value (Low PE, Low PB, High Dividend Yield), Low Volatility (36-month price standard deviation), and risk-adjusted Momentum (12-month return divided by 12-month volatility). Same 19-year window. Same cost structure. Same annual rebalance.

The result is a factor-by-factor map of how liquidity interacts with each premium — and the differences are as revealing as the similarities.

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Methodology: Universe, Factors and Parameters

Data Source & Universe

Provider: EODHD Financial APIs  |  Coverage: December 2006 – December 2025 (19 years)  |  Universe: Nifty 500 stocks by market capitalisation

Survivorship bias correction: All stocks that were ever constituents of the Nifty 500 between Dec 2006 and Dec 2025 are included — including those subsequently delisted, merged, or acquired. Returns through each stock's delisting date are included in performance calculations.

Factor Definitions

Portfolio Construction & Execution Parameters

Scaled Turnover: The Splitting Variable

For each factor's top 50 stocks, we compute average daily Scaled Turnover at the rebalance date, then split them at the median into 25 low-turnover (relatively illiquid, under-traded relative to size) and 25 high-turnover (relatively liquid, actively traded). All other parameters are held constant.

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Quality Factor: The Most Liquidity-Sensitive Premium

If there is one finding from this study that demands immediate attention from Indian equity investors, it is this: the quality factor is the most brutally destroyed by high Scaled Turnover of any factor we tested.

High-turnover Quality stocks — the well-known, widely covered, high-ROE companies that institutional portfolios typically concentrate in — returned just 8.16% Net CAGR over 19 years. That is 2.25% below the Nifty 50 benchmark, with significantly higher volatility (37% vs 20.6%) and a recovery time of nearly 4 years from the worst drawdown.

Why Does High Turnover Destroy the Quality Premium?

The answer lies in how premiums are generated. A quality premium — the excess return earned by holding high-ROE companies — exists because the market has not fully priced in the persistence of that quality. When a stock is heavily traded relative to its size (high Scaled Turnover), it typically signals high institutional attention, frequent analyst coverage, and active price discovery. That quality is already known, already priced, and already competed away.

Low-turnover quality stocks are the opposite: genuinely high-ROE businesses that trade quietly, attract little institutional notice, and whose quality advantage remains underpriced for extended periods. That is where the 15.45% CAGR lives. It is not a mystery — it is the fundamental economics of information asymmetry in a partially efficient market.

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Value Factor: The Risk Story Is Bigger Than the Return Story

Value is the most nuanced finding of this study. The CAGR gap between low and high Scaled Turnover is a relatively modest 1.51% (13.74% vs 12.23%). An investor might dismiss this as marginal. That would be a mistake — because when you look at the risk and recovery metrics, the Scaled Turnover split is doing something profound.

Low-turnover value stocks recovered from their maximum drawdown in just 6 months — the fastest recovery of any strategy in this entire study. High-turnover value took 10 months. The Nifty 500 took 60 months. This is a dramatic difference in the lived experience of holding these portfolios through a crisis.

The drawdown itself tells a similar story: low-turnover value peaked at -54.03% — a brutal number, but actually shallower than the Nifty 500's own -55.12%. High-turnover value dropped to -64.35%, a full 10 percentage points worse. The volatility gap — 26.7% vs 34.2% — further confirms that low-turnover value is structurally calmer.

Why Does Value Respond to Turnover This Way?

Cheap stocks (low PE, low PB) come in two varieties. The first is genuinely neglected — businesses that are cheap because institutional capital has not yet noticed them, their analyst coverage is thin, and their story has not been widely told. These are typically low-turnover. The second is cheap-and-known: value traps that every investor is aware of, actively debated in market commentary, and which trade briskly (high turnover) precisely because informed sellers and buyers disagree sharply on their prospects.

Scaled Turnover filters for the first — patient, neglected cheapness — and away from the second. The result is not a dramatic CAGR uplift, but a portfolio that crashes less and recovers faster, which over 19 years of compounding produces meaningfully different outcomes.

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Low Volatility: The Turnover-Agnostic Exception

Low Volatility is the anomaly that validates the rule. After Quality (7.29% CAGR gap), Momentum (3.15%), and Value (1.51%), the Low Volatility factor produces a Scaled Turnover gap of just 0.08% CAGR — a rounding error across 19 years.

Across every metric — CAGR, Sharpe, Calmar, drawdown, volatility — the two Low Volatility portfolios are nearly indistinguishable. Both significantly outperform the Nifty 500 on every risk dimension. The Sharpe of 0.87 (low-turnover) is the highest of any strategy in this entire study, more than 1.7x the Nifty 500's 0.51.

Why Is Low Volatility Immune to Scaled Turnover?

Low Volatility works through a fundamentally different mechanism than the other three factors. Quality, Value, and Momentum all earn their premium — at least in part — because of market neglect, information asymmetry, or underreaction. These are liquidity-mediated effects: the premium exists precisely because the stocks are not actively enough priced.

Low Volatility is different. The low-vol anomaly — the empirical finding that low-volatility stocks earn higher risk-adjusted returns than theory predicts — appears to stem from structural return patterns, systematic behavioural biases (investors' preference for lottery-like payoffs in high-vol stocks), and constraints facing institutional investors who are benchmarked on absolute returns. None of these mechanisms depend on whether a stock is heavily or lightly traded relative to its size. A low-volatility stock is low-volatility by construction — and that property persists regardless of how actively it trades.

Academically, this aligns with the literature: Blitz and van Vliet (2007) and Baker, Bradley, and Wurgler (2011) both document the low-vol anomaly as driven by investor preference for high-volatility stocks, an effect that is independent of liquidity. Our data confirms this holds in the Indian context.

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Momentum: High Returns, Brutal Crash Risk Regardless of Turnover

Risk-adjusted momentum (12-month return ÷ 12-month volatility) on the Nifty 500 tells a story of two uncomfortable truths: Scaled Turnover matters for returns, but neither bucket of momentum is a comfortable strategy to hold through an Indian bear market.

Low-turnover risk-adjusted momentum returns a respectable 15.04% CAGR. But a maximum drawdown of -75.73% with a 68-month (nearly 6-year) recovery is a severe risk profile. High-turnover momentum is worse across every metric: 11.89% CAGR with -77.70% drawdown and 70-month recovery — worse than the Nifty 500 on risk while barely outperforming it on return.

Why Does Momentum Carry Such Deep Drawdowns on Nifty 500?

Momentum strategies are subject to what the academic literature calls "momentum crashes" — sharp, sudden reversals that occur precisely when momentum has been strongest, typically at market turning points (Barroso and Santa-Clara, 2015; Daniel and Moskowitz, 2016). The Nifty 500 universe, which includes mid-cap stocks, amplifies this: mid-cap momentum stocks are often more cyclically exposed, concentrate in sectors experiencing rapid trend reversals (infrastructure, small NBFCs, specialty chemicals), and have less foreign institutional support as anchors during corrections.

The risk-adjusted momentum formula (return ÷ volatility) helps somewhat — our 68-month recovery is better than the 100-month recovery seen for simple high-Scaled-Turnover momentum in our Nifty 200 study. But the Nifty 500's broader, more volatile mid-cap tail means the crash risk is structurally higher than a large-cap only universe.

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The Meta-Finding: Why Does Turnover Affect Factors Differently?

Putting all four factors together, a clear pattern emerges — and it has a coherent theoretical explanation.

The pattern reflects a single underlying principle: factors that derive their premium from information asymmetry or market neglect are sensitive to Scaled Turnover; factors that derive their premium from structural behavioural biases are not.

Quality and Momentum are information-mediated premiums — they exist because the market is slow to price quality persistence and trend continuation. The moment a stock is heavily traded, that information is quickly processed and the premium disappears. Value sits in between: genuine neglect drives the low-turnover outperformance on risk metrics, but the return gap is smaller because value stocks in any liquidity tier are fundamentally cheap and earn that cheapness over time.

Low Volatility stands apart because its premium is not about information. It is about what investors do with information — systematically overweighting high-volatility, lottery-like stocks regardless of what they know about those stocks. That behavioural bias operates the same in liquid and illiquid stocks alike.

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Academic Context

The relationship between liquidity and factor premia has a substantial academic foundation, though its application to all four factors simultaneously on Indian data is novel to this study.

Amihud and Mendelson (1986) first formalised the illiquidity premium — the idea that less liquid assets must offer higher expected returns to compensate investors for bearing liquidity risk. Amihud (2002) later provided a tractable measure of illiquidity (daily return impact per dollar of trading volume) that showed persistent return premia for illiquid stocks across markets. Our Scaled Turnover metric inverts this: high Scaled Turnover signals high liquidity and, consequently, lower expected premium for factors that are information-mediated.

On the quality factor specifically, Novy-Marx (2013) documented that profitability (gross profit-to-assets) explains significant cross-sectional return variation, but subsequent work — including Hou, Xue, and Zhang (2015) — has shown that this premium is strongest among stocks with lower institutional ownership and coverage. Low Scaled Turnover is a reasonable proxy for exactly this condition in the Indian market.

The Low Volatility anomaly's independence from liquidity is consistent with Baker, Bradley, and Wurgler (2011), who attribute it to benchmark-constrained institutional investors and retail investor preference for high-vol, lottery-like stocks — mechanisms that operate independently of how actively a stock trades. Our 19-year Indian data strongly supports this interpretation.

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Practical Implications

1. Factor fund AUM is a direct threat to Quality and Momentum, not to Low Volatility. As a Quality or Momentum fund grows, it is forced into high-Scaled-Turnover stocks — where our data shows Quality earns 8.16% and Momentum earns 11.89%. Individual investors running smaller portfolios can still access the low-turnover bucket where Quality earns 15.45% and Momentum earns 15.04%. For Low Volatility funds, AUM growth is structurally less damaging because the factor is turnover-agnostic.

2. Value fund selection should weight risk metrics, not just return. With only a 1.51% CAGR gap, the return case for selecting low-turnover Value is modest. But a 6-month vs 10-month recovery from maximum drawdown — and a -54% vs -64% maximum drawdown — is a dramatically different investor experience during a crisis. For Value strategies, the turnover filter is primarily a risk management tool.

3. Low Volatility is the most scalable factor for institutional Indian investors. It is the only factor where large and small investors are on equal footing. Its Sharpe of 0.87, maximum drawdown of -42.80%, and 8-month recovery make it the most efficient risk-adjusted strategy in this study — and none of these properties erode with AUM growth.

4. Scaled Turnover is a second-order filter, not a first-order one. The first-order decision is which factor to use. The second-order decision is which half of that factor's universe to hold. This study shows that second-order decision has a surprisingly large impact — particularly for Quality and Momentum investors.

5. Portfolio size thresholds matter. For a ₹50 lakh portfolio (which is what our slippage is modelled on), accessing low-turnover Nifty 500 stocks is feasible. Portfolios of ₹5–50 lakh can realistically implement these strategies without meaningful market impact. Above ₹5 Cr, investors should carefully track the Scaled Turnover profile of their holdings and model slippage more conservatively.

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Limitations & Counterarguments

1. Slippage is Held Constant Across Turnover Buckets

We applied a uniform 0.05% slippage based on a ₹50 lakh portfolio across all strategies. In practice, low-Scaled-Turnover stocks would face higher market impact — particularly for larger portfolios or during stressed market conditions. Future analyses will test differentiated slippage by turnover bucket, which would narrow the net return gaps for low-turnover strategies somewhat.

2. Is the Quality Turnover Effect Just a Small-Cap Effect?

The Nifty 500 includes mid-cap stocks which have lower average market caps than the Nifty 200. Low-turnover mid-caps could mechanically overlap with the small-cap effect documented globally. However, Scaled Turnover is normalised by market cap — so a mid-cap company with active trading (high Scaled Turnover) is correctly classified as liquid. The effect we measure is relative trading activity within a market-cap tier, not across tiers.

3. Sector Concentration Risk

Low-turnover Quality and Value stocks may concentrate in sectors with structurally lower trading activity — PSUs, utilities, certain industrials. A formal sector-neutral decomposition is in progress and will be published as a follow-up. Our preliminary examination shows the turnover-return relationship persists within sectors, not just across them.

4. Factor Definition Choices

Quality as "High ROE" is one of several valid quality metrics (others include ROCE, asset turnover, accruals quality). Value as a composite of Low PE, Low PB, and High Dividend Yield is one implementation — researchers using earnings yield alone, or EV/EBITDA, would produce different stock selection. The directional findings are likely robust to alternative definitions, but the specific numbers are definition-dependent.

5. Robustness Across Sub-Periods

Our 19-year window spans multiple regimes: the 2008 global financial crisis, the 2013 taper tantrum, the 2020 COVID crash, and the 2022 rate tightening cycle. The Scaled Turnover effect for Quality and Momentum was positive in the large majority of rolling 5-year windows examined — but during specific periods (particularly 2009–2011 reflation rallies), high-turnover stocks of all factors temporarily outperformed as liquidity itself became the dominant market driver. The effect is structural and persistent over full cycles, but not monotonic year-by-year.

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Key Takeaways

1. Scaled Turnover is a universal factor filter — but not uniformly powerful. Quality is the most sensitive (7.29% CAGR gap), followed by Momentum (3.15%), Value (1.51%), and Low Volatility (0.08%).
2. High-turnover Quality stocks destroy capital relative to the Nifty 500. 8.16% CAGR, Sharpe 0.22, 45-month recovery — worse than a passive index fund on every dimension.
3. Low Volatility is the most AUM-scalable factor. Its turnover-agnostic premium means large funds can access it as efficiently as individual investors. Sharpe 0.87, drawdown -42.80%, recovery 8 months — best risk-adjusted profile in the study.
4. Value's turnover split is primarily a risk story. The 1.51% return gap understates the case. A 6-month vs 10-month recovery and -54% vs -64% drawdown are dramatically different investment experiences in a downturn.
5. Momentum carries brutal crash risk regardless of turnover. Both low-turnover (-75.73%, 68 months) and high-turnover (-77.70%, 70 months) momentum suffered similarly catastrophic drawdowns. Momentum suits only investors with extreme patience and conviction.
6. Individual investors with sub-₹5 Cr portfolios have a structural access edge. They can hold the low-turnover buckets of Quality and Momentum that large funds cannot access at scale.
7. The mechanism matters. Factors driven by information asymmetry (Quality, Momentum) are liquidity-sensitive. Factors driven by structural behavioural biases (Low Volatility) are not. Understanding why a premium exists tells you how it will survive scale.

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Frequently Asked Questions

Conclusion

The finding from our Nifty 200 momentum study — that the momentum premium in India is largely an illiquidity premium — turns out to be a specific instance of a broader principle. Across the Nifty 500 universe over 19 years, Scaled Turnover is a consistent signal of how much of a factor's premium remains unpriced and accessible.

For Quality and Momentum, the premium is overwhelmingly concentrated in low-Scaled-Turnover stocks — lightly traded, under-researched, under-institutionalised companies whose fundamental advantages have not yet been fully arbitraged by the market. For Value, the Scaled Turnover filter is less about return enhancement and more about crash resistance — separating genuinely neglected cheapness from actively debated cheapness. For Low Volatility, the filter is irrelevant, because the premium is not information-mediated at all.

The practical implications are unambiguous. Individual systematic investors with manageable portfolio sizes have a structural access advantage in Quality and Momentum that no large fund can replicate as it scales. The only factor where scale is not a disadvantage is Low Volatility — which also happens to have the best risk-adjusted return profile of any strategy in this study.

Before allocating to any factor strategy, ask not just what the factor is — but where in the liquidity spectrum its premium lives, whether you can access that part of the market at your portfolio size, and whether the mechanism generating the premium is one that survives scrutiny.

About This Research

Data Source: EODHD Financial APIs (December 2006 – December 2025)
Platform: BacktestIndia.com Strategy Laboratory
Universe: Nifty 500 | Top 50 per factor → 25 low / 25 high Scaled Turnover | Equal-weighted | Annual rebalance
Costs: 0.11% transaction cost + 0.05% slippage (₹50L portfolio) | LTCG 12.5% | STCG 20%
Survivorship bias: Corrected — includes delisted and merged companies
Author: T. Desai | Published: April 7, 2026
Contact: backtestindia@gmail.com
License: CC BY-NC 4.0 — cite freely with attribution
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