Quant-based equity mutual funds use mathematical models and data-driven rules to build portfolios. They occupy a small corner of the Indian mutual fund industry. The category has just 11 schemes managing around ₹11,700 crore, with only two having a track record of more than seven years, as most were launched after 2021.
Asset management companies (AMCs) appear reluctant to push the category aggressively. Indian fund houses have traditionally built their equity businesses around star fund managers, large research teams and discretionary stock-picking. Quant investing, by contrast, requires a different investment architecture, combining large datasets, quantitative models and specialised technology. Consequently, only a handful of AMCs have made quant investing a meaningful part of their equity offerings.
With returns from these funds so far being uneven and largely inconsistent, the real question is: Is quant investing genuinely a better way to pick stocks? Is it simply another investment philosophy yet to prove its worth? Let’s take a detailed look.
Quant investing
In traditional active equity funds, fund managers and research teams analyse companies, assess market conditions and make investment decisions based on their judgement. Quant funds, on the other hand, use data, mathematical models and predefined rules to select stocks and construct portfolios.
Quant strategies attempt to reduce behavioural biases by following a predefined and typically backtested investment process, with limited human intervention.
However, like any other strategy, quant investing is not a sure-shot ticket to superior returns. The success of any quant strategy ultimately depends on whether the factors and signals embedded in its model continue to work across changing market environments.
Factors
Quant funds use predefined factors such as value, quality, momentum, low volatility and growth to identify stocks. Their models systematically score stocks on these factors and select or weight them according to the fund’s strategy. Many quant funds combine multiple factors rather than relying on a single one, seeking to build more diversified and robust portfolios.
Momentum favours recent price winners, often with returns adjusted for volatility.
Quality screens for profitability, earnings stability, cash flows and manageable debt.
Value seeks stocks that look inexpensive on measures such as P/E, P/B and dividend yield.
Low volatility favours stocks with lower historical price fluctuations.
Growth favours companies with strong, consistent sales, profit or cash-flow growth.
We analysed the portfolio composition, quantitative models, factors and key metrics used by these funds to construct and manage their portfolios. However, disclosure of proprietary models remains limited.
The analysis throws up several interesting findings:
From factor investing to factor engineering: Many funds are moving beyond the conventional value-quality-momentum-low-volatility framework. 360 ONE, for example, combines momentum with filters for secular, cyclical and defensive characteristics, while also explicitly seeking to avoid value traps. Aditya Birla incorporates sell-side earnings revisions, while Quant Quantamental combines quantitative and fundamental screens.
Kotak takes a layered approach. Momentum is used for initial stock selection, quality then filters out weaker companies, and low volatility is finally used to determine position weights.
Combining models with human judgment: Although models drive the investment process, most funds still leave some room for human intervention. Quant Quantamental is a clear example, combining quantitative signals with fundamental inputs in a hybrid approach.
Factor rotation: While many funds use a relatively consistent set of factors to build and reconstruct their portfolios, ICICI Prudential’s model is described as dynamic, allowing factor exposures to rotate over time.
Same benchmark, but different risk profiles: While quant funds typically use either the BSE 200 or Nifty 200 as their benchmark, their market-cap allocations vary sharply. An analysis of the average market-cap allocation over the past year, broken down into large-, mid- and small-cap stocks, shows that Nippon India Quant held 84 per cent in large-caps. Quant Quantamental and Kotak Quant were more balanced, with 56 per cent and 54 per cent in large-caps, respectively. Motilal Oswal Quant, meanwhile, has shown a greater willingness to shift across market-cap segments. At one point, it held 76 per cent in small-cap stocks in September 2024. Thus, while the benchmark provides a broad reference point, the model determines the portfolio’s actual risk profile. Motilal Oswal Quant also has leeway to allocate a portion of the portfolio outside model-driven selections.
Momentum dominates: Momentum appears either as a primary factor or as a critical component of nearly every major quant strategy analysed, including funds from 360 ONE, Aditya Birla, DSP, Kotak, Nippon, SBI and UTI. Interestingly, each fund uses momentum differently. Some employ it as the primary stock-selection engine, while others combine it with quality filters or low-volatility overlays. The prominence of momentum also highlights one of the key risks of quant investing: a strategy that works strongly during one market phase can reverse sharply when market leadership changes.
Rebalancing speed: As per the fund presentation notes, Axis, DSP, Motilal Oswal and SBI Quant rebalance monthly, while Aditya Birla and Nippon India Quant rebalance quarterly. Frequent rebalancing allows a model to respond faster to changes in its underlying signals. However, it can also increase portfolio turnover and transaction costs.
What should you do
Not all quant funds are created equal. Their investment approaches differ significantly, making them distinct investment engines rather than a homogeneous category.
For instance, 360 ONE Quant places greater emphasis on momentum, while Axis Quant gives relatively higher weight to quality and value. Aditya Birla SL Quant gives equal importance to its factors, whereas Nippon India and SBI Quant dynamically allocate across factors. Aditya Birla SL Quant and Quant Quantamental combine man and machine, using quantitative models alongside fund-manager judgement.
Therefore, investors should look beyond the “quant” label. What matters is the investment engine under the hood. Examine the factors used, how they are combined, how frequently the portfolio is rebalanced, the market-cap exposure created by the model, and how much human judgement remains in the process.
Secondly, no single factor works all the time, nor does any combination of factors guarantee consistent outperformance. Momentum can crash, value can remain dormant for years, growth can become overvalued, quality can lag during speculative rallies, and low volatility can underperform in rapidly rising markets.
Multi-factor investing recognises that market leadership can change over time. When one factor struggles, another may compensate, potentially making the overall strategy more resilient. But multi-factor investing is not a free pass either. Combining factors can reduce dependence on a single source of return, but it does not eliminate factor risk.
Above all, returns from these funds have so far been uneven across schemes and periods. Only two funds have a track record of more than seven years. A five-year rolling-return analysis over the last seven-year period shows that Nippon India delivered an average CAGR of 20 per cent, outperforming the Nifty 200 Total Return Index’s 17.5 per cent. In contrast, DSP Quant lagged, with a 12.5 per cent CAGR.
Among funds with a shorter track record, Quant Quantamental delivered relatively better returns over the one-, three- and five-year periods, while 360 ONE Quant and Kotak Quant did better over the three-year period.
The category’s short history offers limited evidence across market cycles, while wide return dispersion shows that the “quant” label alone says little. Outcomes depend on factor selection, model design and the strategy’s ability to adapt to changing markets.
Investors should look beyond the “quant” label, understand each fund’s investment engine and evaluate its performance across market cycles.
Published on September 19, 2026
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