Quant-based funds in India: early results mixed, structural limits visible

# What this story explains Quant-based equity mutual funds rely on data, mathematical rules and predefined factors to select stocks. In India the category is still small and young, and returns have been uneven. This brief explains how these funds differ, why results vary, and what limits their near-term case.

# How quant funds work, simply Quant strategies score and rank stocks using measurable factors — examples include value (cheapness), quality (profitability and balance-sheet strength), momentum (recent price performance), low volatility and growth (sales or earnings expansion). Models apply filters and weights, then construct portfolios with limited discretionary decisions. The hoped-for benefits are systematic decision-making and fewer behavioural errors.

Indian quant funds do not all use the same recipe. The article analysed portfolios, factor sets and construction rules and found meaningful variation:

• Factor engineering beyond the usual quartet: Some funds layer additional filters or rework standard factors. One combines momentum with secular, cyclical and defensive filters and explicitly seeks to avoid value traps. Another uses sell-side earnings revisions as an input.
• Multi-step processes: Some managers use momentum for initial selection, then apply quality screens to remove weak companies and low-volatility rules to set position sizes.
• Hybrid human-model approaches: Several funds permit human intervention. One product labeled "quantamental" blends quantitative signals with fundamental analysis. Most firms still leave scope for judgment despite model-driven processes.
• Dynamic factor rotation: At least one fund's model changes factor exposures over time rather than staying fixed, which alters portfolio behavior across cycles.

# The small size and short track records matter The category comprises 11 schemes managing about ₹11,700 crore, and only two funds have track records beyond seven years. Many quant funds launched after 2021. That limited history makes it hard to assess model resilience across multiple market regimes.

# Same benchmark, different risks

• One fund held about 84% in large caps over the last year.
• Others were roughly balanced, with roughly 50–56% in large caps.
• Another fund has at times taken heavy small-cap exposures, once holding 76% in small caps in September 2024.

# Performance so far: uneven and inconsistent Returns across the category have been mixed. The article frames the central question plainly: do quant methods reliably beat discretionary stock-picking? The short answer, based on current evidence, is that quant investing in India remains an alternative investment philosophy that has yet to deliver consistently superior results across market conditions.

# What investors should watch

• Factor definitions and construction: similar-sounding factors can be implemented very differently, with large implications for outcomes.
• Rebalancing rules and turnover: how often models rebalance changes trading costs and realized returns.

• Track record length: longer records across different cycles give more confidence in model robustness.

# Bottom line Quant funds use objective, repeatable rules to build portfolios, but in India the category is young, small and heterogeneous. Differences in factor engineering, allocation rules and human overlays explain much of the variation in performance. Investors should read fund disclosures carefully and treat quant funds as distinct strategies rather than interchangeable products.